diff --git a/datasets/3kricegenome.yaml b/datasets/3kricegenome.yaml index b953a5a05..45ac198cd 100644 --- a/datasets/3kricegenome.yaml +++ b/datasets/3kricegenome.yaml @@ -4,6 +4,10 @@ Documentation: https://github.com/awslabs/open-data-docs/tree/main/docs/3kricege Contact: http://iric.irri.org/contact-us ManagedBy: '[International Rice Research Institute](https://www.irri.org/)' UpdateFrequency: Not updated +Collabs: + ASDI: + Tags: + - agriculture Tags: - agriculture - food security diff --git a/datasets/aef-source.yaml b/datasets/aef-source.yaml new file mode 100644 index 000000000..fde49e2b4 --- /dev/null +++ b/datasets/aef-source.yaml @@ -0,0 +1,24 @@ +Name: Google Satellite Embedding V1 +Description: COG (Cloud-Optimized GeoTIFF) files that together contain the AlphaEarth Foundations annual Satellite Embedding dataset. It contains the annual embeddings for the years from 2018 to 2024, inclusive. +Documentation: https://source.coop/tge-labs/aef +Contact: https://cloudnativegeo.org/join +ManagedBy: "[Source Cooperative](https://source.coop/)" +UpdateFrequency: As new data versions become available +Tags: + - aws-pds + - machine learning + - satellite imagery + - aerial imagery + - earth observation + - imaging +License: CC-BY 4.0 +Citation: "The AlphaEarth Foundations Satellite Embedding dataset is produced by Google and Google DeepMind." +Resources: + - Description: Google Satellite Embedding V1 + ARN: arn:aws:s3:::us-west-2.opendata.source.coop/tge-labs/aef + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://source.coop/tge-labs/aef/)' +ADXCategories: + - Environmental Data diff --git a/datasets/africa-field-boundary-labels.yaml b/datasets/africa-field-boundary-labels.yaml index 271fe1b24..80996af96 100644 --- a/datasets/africa-field-boundary-labels.yaml +++ b/datasets/africa-field-boundary-labels.yaml @@ -13,6 +13,10 @@ Documentation: Information on the primary dataset can be found [here](https://gi Contact: airg@clarku.edu ManagedBy: "[The Agricultural Impacts Research Group](https://agroimpacts.info/)" UpdateFrequency: "Updated versions of the dataset are added as they are developed" +Collabs: + ASDI: + Tags: + - agriculture Tags: - agriculture - machine learning diff --git a/datasets/ag-loam.yaml b/datasets/ag-loam.yaml index e4c2e6533..6c03ec929 100644 --- a/datasets/ag-loam.yaml +++ b/datasets/ag-loam.yaml @@ -9,6 +9,10 @@ Documentation: https://github.com/UCR-Robotics/AG-LOAM Contact: Hanzhe Teng (hteng007@ucr.edu), Konstantinos Karydis (kkarydis@ece.ucr.edu) ManagedBy: "[Autonomous Robots and Control Systems Lab](https://sites.google.com/view/arcs-lab)" UpdateFrequency: NA +Collabs: + ASDI: + Tags: + - agriculture Tags: - aws-pds - robotics diff --git a/datasets/ai3.yaml b/datasets/ai3.yaml new file mode 100644 index 000000000..6d3d50d0d --- /dev/null +++ b/datasets/ai3.yaml @@ -0,0 +1,37 @@ +Name: AI3 Protein-Ligand Binding Affinity Dataset +Description: > + The rapid advancement of computing technologies, particularly artificial intelligence (AI), has revolutionized various domains, including drug discovery. Curated datasets are crucial for developing reliable, generalizable, and accurate models for practical applications. Generating experimental data on a large scale is an expensive and arduous process. In domains such as medical diagnostics where real-life data is hard to obtain, synthetic data has been shown to be extremely valuable. We, teams from IIIT Hyderabad, Intel, AWS, and Insilico Medicine, have performed physics-based calculations (molecular dynamics simulations) on about 20,000 protein-ligand complexes. The dataset comprises molecular dynamics snapshots, binding affinities calculated using the MM-PBSA method, and individual energy components, including electrostatic and van der Waals interactions. DatasetFileFormats essentially incorporate i. 3D coordinates of the protein-ligand complexes (pdb) in tar.gz files, and ii. CSV files containing the energy data. DatasetUsages are on i. ML scoring function for predicting binding affinities of given protein-ligand complexes, ii. Classification models for predicting correct binding poses of ligands, iii. identification of cryptic binding pockets, and iv. optimization of binding features by exploiting the individual components of the energy (experimental data has only the total binding affinity). Further, the novelty of the dataset highlights the fact that existing AI/ML training datasets lack dynamic data and are inherently biased. Further, binding affinity data existing in the literature are obtained from different experimental protocols. Therefore, this dataset has been uniquely created (from the same computational protocols) followed by free energy calculations with molecular dynamics (MD) simulations. The dynamic data-enriched protein-ligand coordinates can be used to effectively train convolutional neural network-based regression models for more accurate binding affinity prediction. +Documentation: https://github.com/devalab/AI3 +Contact: devalab@iiit.ac.in +ManagedBy: International Institute of Information Technology Hyderabad +UpdateFrequency: Not updated +Tags: + - pharmaceutical + - simulations + - health + - life sciences + - machine learning + - protein + - molecular dynamics + - aws-pds +License: https://devalab.in/AI3.html +Resources: + - Description: ai3data bucket includes coordinates and the energetics of ~20,000 protein-ligand binding affinity datasets. The subfolders of ai3data bucket consist of Version 1, Version2 and Version 3. Version1 contains the total Size of 10.4 GiB (Initial structure of the protein-ligand complex and the average binding affinities along with average energy components). Version2 contains the total Size of 1.2 TiB (Five trajectories of protein-ligand complex (200 snapshots in all) and the closest two water molecules for each of the protein-ligand complex, and the time series of the binding affinities along with average energy components). Version3 contains the total Size of 10.7 TiB (Five trajectories of completely solvated protein-ligand complex (200 snapshots in all), and the time series of binding affinities along with average energy components). + ARN: arn:aws:s3:::ai3data + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: "AI3: Protein-Ligand Binding Affinity Dataset" + URL: https://github.com/devalab/AI3 + AuthorName: Deva Priyakumar Lab + AuthorURL: https://github.com/devalab + Publications: + - Title: "PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications" + URL: https://www.nature.com/articles/s41597-022-01631-9 + AuthorName: U. Deva Priyakumar + AuthorURL: https://devalab.in/ + - Title: "PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications" + URL: https://www.nature.com/articles/s41597-023-02872-y + AuthorName: U. Deva Priyakumar + AuthorURL: https://devalab.in diff --git a/datasets/allen-hmba-releases.yaml b/datasets/allen-hmba-releases.yaml new file mode 100644 index 000000000..b7275588d --- /dev/null +++ b/datasets/allen-hmba-releases.yaml @@ -0,0 +1,46 @@ +Name: Human and Mammalian Brain Atlas +Description: + Human and Mammalian Brain Atlas (HMBA) is a major atlas of the BRAIN Initiative Cell Atlas Network (BICAN) that proposes to establish a comprehensive, + highly granular cell atlas in complete adult human, macaque, and marmoset brains that links brain structure, function and cellular architecture. + Release artifacts have been made available in this OpenData bucket to enable utilization along with their paper publications by the neuroscience community. +Documentation: https://portal.brain-map.org/explore/hmba +Contact: awspds@alleninstitute.org +ManagedBy: "[Allen Institute](http://www.alleninstitute.org/)" +UpdateFrequency: Never +Tags: + - aws-pds + - biology + - gene expression + - neurobiology + - life sciences + - single-cell transcriptomics + - Mus musculus + - Homo sapiens + - non-human primate +License: http://www.alleninstitute.org/legal/terms-use/ +Citation: +Resources: + - Description: Project data files in a public bucket + ARN: arn:aws:s3:::allen-hmba-releases + Region: us-west-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Human-Mammalian Brain - Basal Ganglia - Data + URL: https://alleninstitute.github.io/abc_atlas_access/descriptions/HMBA-BG_dataset.html + AuthorName: Allen Institute for Brain Science + AuthorURL: www.alleninstitute.org + - Title: Human-Mammalian Brain - CCF Book + URL: https://alleninstitute.github.io/CCF-MAP/ + AuthorName: Allen Institute for Brain Science + AuthorURL: www.alleninstitute.org + Tools & Applications: + - Title: HMBA Basal Ganglia resources in Brain Knowledge Platform's Data Catalog + URL: https://knowledge.brain-map.org/data/POZ2HCPBT60DSDJ8UA7 + AuthorName: Allen Institute for Brain Science + AuthorURL: www.alleninstitute.org + + + + + diff --git a/datasets/alliance-genome-resources.yaml b/datasets/alliance-genome-resources.yaml new file mode 100644 index 000000000..3bf8e1ffa --- /dev/null +++ b/datasets/alliance-genome-resources.yaml @@ -0,0 +1,85 @@ +Name: Alliance of Genome Resources +Description: The Alliance of Genome Resources is a consortium that integrates genomic, genetic, and molecular data from leading model organism databases including Drosophila melanogaster, Caenorhabditis elegans, Danio rerio (zebrafish), Mus musculus (mouse), Rattus norvegicus (rat), Saccharomyces cerevisiae (yeast), Xenopus laevis and Xenopus tropicalis (frogs), and human reference data. The Alliance provides comprehensive datasets including gene annotations, disease associations, expression data (bulk and single-cell RNA-Seq), protein and genetic interactions, orthology relationships, variants and alleles, and complete genome sequences with annotations. Data is organized into Alliance-wide integrated datasets and organism-specific collections, supporting comparative genomics, disease modeling, and functional genomics research. +Documentation: https://github.com/alliance-genome/agr_open_data +Contact: help@alliancegenome.org +ManagedBy: Alliance of Genome Resources Consortium +UpdateFrequency: Quarterly releases (every ~3 months) +Tags: + - aws-pds + - genomic + - bioinformatics + - biology + - gene expression + - life sciences + - genetic + - genome + - Drosophila melanogaster + - Caenorhabditis elegans + - Danio rerio + - Mus musculus + - Rattus norvegicus + - Homo sapiens + - transcriptomics + - protein + - vcf + - fasta +License: Most Alliance data is available under CC0 1.0 Universal (Public Domain Dedication). Some datasets may use CC-BY 4.0 (attribution required). Full details at https://www.alliancegenome.org/terms-of-use +Citation: Alliance of Genome Resources Consortium. Alliance of Genome Resources Portal - unified model organism research platform. Nucleic Acids Research (2023). https://doi.org/10.1093/nar/gkac1003 +Resources: + - Description: Alliance-wide integrated datasets including disease associations, gene expression, molecular and genetic interactions, orthology relationships, gene descriptions, and variants across all Alliance organisms. Data is organized by release version (8.3.0/, 8.2.0/, etc.), then by data type, with organism-specific collections for FB (FlyBase/Drosophila), MGI (Mouse), RGD (Rat), SGD (Yeast), WB (Worm), XBXL/XBXT (Xenopus), ZFIN (Zebrafish), and HUMAN reference data. Available in TSV, JSON, and VCF formats. + ARN: arn:aws:s3:::alliance-genome-downloads + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://alliance-genome-downloads.s3.amazonaws.com/)' + - Description: FlyBase-specific data for Drosophila melanogaster and related species, including gene annotations, GO annotations, expression data (bulk RNA-Seq, single-cell RNA-Seq), disease associations, phenotypes, interactions, orthologs, genome sequences (FASTA), and genome annotations (GFF3/GTF). Data organized by release (current/, FB2025_04/, etc.) with precomputed analysis files and complete Chado XML database dumps. Publicly accessible via HTTPS for direct download without AWS credentials. + ARN: arn:aws:s3:::s3ftp.flybase.org + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse via HTTPS](https://s3ftp.flybase.org/releases/current/)' +DataAtWork: + Tutorials: + - Title: Alliance of Genome Resources AWS Data Access Tutorials + URL: https://github.com/alliance-genome/agr_open_data/blob/main/TUTORIAL.md + AuthorName: Alliance of Genome Resources Consortium + AuthorURL: https://www.alliancegenome.org + Tools & Applications: + - Title: Alliance of Genome Resources Portal + URL: https://www.alliancegenome.org + AuthorName: Alliance of Genome Resources Consortium + AuthorURL: https://www.alliancegenome.org + - Title: FlyBase - Drosophila Database + URL: https://flybase.org + AuthorName: FlyBase Consortium + AuthorURL: https://flybase.org + - Title: WormBase - C. elegans Database + URL: https://www.wormbase.org + AuthorName: WormBase Consortium + AuthorURL: https://www.wormbase.org + - Title: ZFIN - Zebrafish Database + URL: https://zfin.org + AuthorName: ZFIN + AuthorURL: https://zfin.org + - Title: MGI - Mouse Genome Database + URL: http://www.informatics.jax.org + AuthorName: MGI + AuthorURL: http://www.informatics.jax.org + - Title: RGD - Rat Genome Database + URL: https://rgd.mcw.edu + AuthorName: RGD + AuthorURL: https://rgd.mcw.edu + - Title: SGD - Saccharomyces Genome Database + URL: https://www.yeastgenome.org + AuthorName: SGD + AuthorURL: https://www.yeastgenome.org + - Title: Xenbase - Xenopus Database + URL: http://www.xenbase.org + AuthorName: Xenbase + AuthorURL: http://www.xenbase.org + Publications: + - Title: Alliance of Genome Resources Portal - unified model organism research platform + URL: https://doi.org/10.1093/nar/gkac1003 + AuthorName: Alliance of Genome Resources Consortium +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/amazon-last-mile-challenges.yaml b/datasets/amazon-last-mile-challenges.yaml index 27353960d..6d513d0d2 100644 --- a/datasets/amazon-last-mile-challenges.yaml +++ b/datasets/amazon-last-mile-challenges.yaml @@ -7,6 +7,10 @@ Contact: lastmile-research-challenge@amazon.com ManagedBy: "[Amazon](https://www.amazon.com/)" UpdateFrequency: None +Collabs: + ASDI: + Tags: + - infrastructure Tags: - transportation - machine learning diff --git a/datasets/anvilproject.yaml b/datasets/anvilproject.yaml new file mode 100644 index 000000000..56cccb605 --- /dev/null +++ b/datasets/anvilproject.yaml @@ -0,0 +1,207 @@ +Name: NHGRI AnVIL Project + +Description: "The NHGRI Analysis, Visualization, and Informatics Lab-space + (AnVIL) Project (https://anvilproject.org/) is the National Human Genome + Research Institute's cloud-based platform for genomic data sharing and + analysis. AnVIL hosts widely used human genome reference datasets generated + through NHGRI-funded research. AnVIL on Open Data on AWS provides public + access to open-access datasets available through AnVIL. The project is a + collaborative effort involving NHGRI, the Broad Institute, Johns Hopkins + University, the University of California Santa Cruz, Vanderbilt University + Medical Center, Brigham and Women's Hospital, the Carnegie Institution for + Science, the City University of New York, the Fred Hutchinson Cancer Research + Center, Harvard University, Oregon Health & Science University, Massachusetts + General Hospital, Moffitt Cancer Center, Penn State University, and Washington + University." + +Documentation: "https://explore.anvilproject.org/datasets" + +Contact: "https://anvilproject.org/help" + +ManagedBy: "The AnVIL Project, and UC Santa Cruz Genomics Institute, University of California, Santa Cruz (UCSC)" + +UpdateFrequency: Quarterly + +Tags: + - life sciences + - biology + - genome + - genomic + - gene expression + - Homo sapiens + +License: "https://anvilproject.org/faq/data-security" + +Citation: "Schatz MC, Philippakis AA, Afgan E, Banks E, Carey VJ, Carroll RJ, et al. [Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)](https://www.cell.com/cell-genomics/fulltext/S2666-979X(21)00106-3). Cell Genomics. 2022;2. doi:10.1016/j.xgen.2021.100085" + +Resources: + - Description: "An S3 bucket containing all publicly accessible data files in the AnVIL Project. The bucket layout and access procedures are documented at https://github.com/DataBiosphere/azul/blob/develop/docs/mirror.rst and metadata can be viewed at https://explore.anvilproject.org/datasets or accessed programmatically at https://service.explore.anvilproject.org/" + ARN: arn:aws:s3:::anvilproject + Region: us-east-1 + Type: S3 Bucket + Explore: + - "[Data Browser UI](https://explore.anvilproject.org/datasets)" + - "[Azul REST Web Service](https://service.explore.anvilproject.org/)" + - Description: "Notifications for new NHGRI AnVIL data" + ARN: arn:aws:sns:us-east-1:160936121715:anvilproject-object_created + Region: us-east-1 + Type: SNS Topic + +DataAtWork: + Publications: + - Title: "Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)" + URL: "https://doi.org/10.1016/j.xgen.2021.100085" + AuthorName: "Michael C. Schatz, Anthony A. Philippakis, Enis Afgan, Eric + Banks, Vincent J. Carey, Robert J. Carroll, Alessandro Culotti, Kyle + Ellrott, Jeremy Goecks, Robert L. Grossman, Ira M. Hall, Kasper D. + Hansen, Jonathan Lawson, Jeffrey T. Leek, Anne O’Donnell Luria, Stephen + Mosher, Martin Morgan, Anton Nekrutenko, Brian D. O’Connor, Kevin + Osborn, Benedict Paten, Candace Patterson, Frederick J. Tan, Casey + Overby Taylor, Jennifer Vessio, Levi Waldron, Ting Wang, Kristin + Wuichet, AnVIL Team" + - Title: "Beyond the Human Genome Project: The Age of Complete Human Genome + Sequences and Pangenome References" + URL: "https://doi.org/10.1146/annurev-genom-021623-081639" + AuthorName: "Dylan J. Taylor, Jordan M. Eizenga, Qiuhui Li, Arun Das, + Katharine M. Jenike, Eimear E. Kenny, Karen H. Miga, Jean Monlong, Rajiv + C. McCoy, Benedict Paten, and Michael C. Schatz" + - Title: "CNPI: Rapid Analyses of Human Copy Number Data" + URL: "https://doi.org/10.1016/j.jmb.2025.169313" + AuthorName: "Jack Ustanik, Tychele N. Turner" + - Title: "The Galaxy platform for accessible, reproducible, and + collaborative data analyses: 2024 update" + URL: "https://doi.org/10.1093/nar/gkae410" + AuthorName: "The Galaxy Community" + - Title: "The complete sequence of a human Y chromosome" + URL: "https://doi.org/10.1038/s41586-023-06457-y" + AuthorName: "Arang Rhie, Sergey Nurk, Monika Cechova, Savannah J. Hoyt, + Dylan J. Taylor, Nicolas Altemose, Paul W. Hook, Sergey Koren, Mikko + Rautiainen, Ivan A. Alexandrov, Jamie Allen, Mobin Asri, Andrey V. + Bzikadze, Nae-Chyun Chen, Chen-Shan Chin, Mark Diekhans, Paul Flicek, + Giulio Formenti, Arkarachai Fungtammasan, Carlos Garcia Giron, Erik + Garrison, Ariel Gershman, Jennifer L. Gerton, Patrick G. S. Grady, + Andrea Guarracino, Leanne Haggerty, Reza Halabian, Nancy F. Hansen, + Robert Harris, Gabrielle A. Hartley, William T. Harvey, Marina Haukness, + Jakob Heinz, Thibaut Hourlier, Robert M. Hubley, Sarah E. Hunt, Stephen + Hwang, Miten Jain, Rupesh K. Kesharwani, Alexandra P. Lewis, Heng Li, + Glennis A. Logsdon, Julian K. Lucas, Wojciech Makalowski, Christopher + Markovic, Fergal J. Martin, Ann M. Mc Cartney, Rajiv C. McCoy, Jennifer + McDaniel, Brandy M. McNulty, Paul Medvedev, Alla Mikheenko, Katherine M. + Munson, Terence D. Murphy, Hugh E. Olsen, Nathan D. Olson, Luis F. + Paulin, David Porubsky, Tamara Potapova, Fedor Ryabov, Steven L. + Salzberg, Michael E. G. Sauria, Fritz J. Sedlazeck, Kishwar Shafin, + Valery A. Shepelev, Alaina Shumate, Jessica M. Storer, Likhitha + Surapaneni, Angela M. Taravella Oill, Françoise Thibaud-Nissen, Winston + Timp, Marta Tomaszkiewicz, Mitchell R. Vollger, Brian P. Walenz, Allison + C. Watwood, Matthias H. Weissensteiner, Aaron M. Wenger, Melissa A. + Wilson, Samantha Zarate, Yiming Zhu, Justin + M. Zook, Evan E. Eichler, Rachel J. O’Neill, Michael C. Schatz, Karen H. + Miga, Kateryna D. Makova, Adam M. Phillippy" + - Title: "Approaching complete genomes, transcriptomes and epi-omes with + accurate long-read sequencing" + URL: "https://doi.org/10.1038/s41592-022-01716-8" + AuthorName: "Sam Kovaka, Shujun Ou, Katharine M. Jenike, Michael C. + Schatz" + - Title: "The complete sequence and comparative analysis of ape sex + chromosomes" + URL: "https://doi.org/10.1038/s41586-024-07473-2" + AuthorName: "Kateryna D. Makova, Brandon D. Pickett, Robert S. Harris, + Gabrielle A. Hartley, Monika Cechova, Karol Pal, Sergey Nurk, DongAhn + Yoo, Qiuhui Li, Prajna Hebbar, Barbara C. McGrath, Francesca Antonacci, + Margaux Aubel, Arjun Biddanda, Matthew Borchers, Erich Bornberg-Bauer, + Gerard G. Bouffard, Shelise Y. Brooks, Lucia Carbone, Laura Carrel, + Andrew Carroll, Pi-Chuan Chang, Chen-Shan Chin, Daniel E. Cook, Sarah J. + C. Craig, Luciana de Gennaro, Mark Diekhans, Amalia Dutra, Gage H. + Garcia, Patrick G. S. Grady, Richard E. Green, Diana Haddad, Pille + Hallast, William T. Harvey, Glenn Hickey, David A. Hillis, Savannah J. + Hoyt, Hyeonsoo Jeong, Kaivan Kamali, Sergei L. Kosakovsky Pond, Troy + M. LaPolice, Charles Lee, Alexandra P. Lewis, Yong-Hwee E. Loh, + Patrick Masterson, Kelly M. McGarvey, Rajiv C. McCoy, Paul Medvedev, + Karen H. Miga, Katherine M. Munson, Evgenia Pak, Benedict Paten, + Brendan J. Pinto, Tamara Potapova, Arang Rhie, Joana L. Rocha, Fedor + Ryabov, Oliver A. Ryder, Samuel Sacco, Kishwar Shafin, Valery A. + Shepelev, Viviane Slon, Steven J. Solar, Jessica M. Storer, Peter H. + Sudmant, Sweetalana, Alex Sweeten, Michael G. Tassia, Françoise + Thibaud-Nissen, Mario Ventura, Melissa A. Wilson, Alice C. Young, + Huiqing Zeng, Xinru Zhang, Zachary A. Szpiech, Christian D. Huber, + Jennifer L. Gerton, Soojin V. Yi, Michael C. Schatz, Ivan A. + Alexandrov, Sergey Koren, Rachel J. O’Neill, Evan E. Eichler, Adam M. + Phillippy" + - Title: "Scalable Nanopore sequencing of human genomes provides a + comprehensive view of haplotype-resolved variation and methylation" + URL: "https://doi.org/10.1038/s41592-023-01993-x" + AuthorName: "Mikhail Kolmogorov, Kimberley J. Billingsley, Mira Mastoras, + Melissa Meredith, Jean Monlong, Ryan Lorig-Roach, Mobin Asri, Pilar + Alvarez Jerez, Laksh Malik, Ramita Dewan, Xylena Reed, Rylee M. Genner, + Kensuke Daida, Sairam Behera, Kishwar Shafin, Trevor Pesout, Jeshuwin + Prabakaran, Paolo Carnevali, Jianzhi Yang, Arang Rhie, Sonja W. Scholz, + Bryan J. Traynor, Karen H. Miga, Miten Jain, Winston Timp, Adam M. + Phillippy, Mark Chaisson, Fritz J. Sedlazeck, Cornelis Blauwendraat, + Benedict Paten" + - Title: "The Human Pangenome Project: a global resource to map genomic + diversity" + URL: "https://doi.org/10.1038/s41586-022-04601-8" + AuthorName: "Ting Wang, Lucinda Antonacci-Fulton, Kerstin Howe, Heather A. + Lawson, Julian K. Lucas, Adam M. Phillippy, Alice B. Popejoy, Mobin + Asri, Caryn Carson, Mark J. P. Chaisson, Xian Chang, Robert Cook-Deegan, + Adam L. Felsenfeld, Robert S. Fulton, Erik P. Garrison, Nanibaa’ A. + Garrison, Tina A. Graves-Lindsay, Hanlee Ji, Eimear E. Kenny, Barbara A. + Koenig, Daofeng Li, Tobias Marschall, Joshua F. McMichael, Adam M. + Novak, Deepak Purushotham, Valerie A. Schneider, Baergen I. Schultz, + Michael W. Smith, Heidi J. Sofia, Tsachy Weissman, Paul Flicek, Heng Li, + Karen H. Miga, Benedict Paten, Erich D. Jarvis, Ira M. Hall, Evan E. + Eichler, David Haussler, the Human Pangenome Reference Consortium" + - Title: "Deciphering the impact of genomic variation on function" + URL: "https://doi.org/10.1038/s41586-024-07510-0" + AuthorName: "IGVF Consortium" + - Title: "A complete reference genome improves analysis of human genetic variation" + URL: "https://doi.org/10.1126/science.abl3533" + AuthorName: "Sergey Aganezov, Stephanie M. Yan, Daniela C. Soto, Melanie + Kirsche, Samantha Zarate, Pavel Avdeyev, Dylan J. Taylor, Kishwar + Shafin, Alaina Shumate, Chunlin Xiao, Justin Wagner, Jennifer McDaniel, + Nathan D. Olson, Michael E. G. Sauria, Mitchell R. Vollger, Arang Rhie, + Melissa Meredith, Skylar Martin, Joyce Lee, Sergey Koren, Jeffrey A. + Rosenfeld, Benedict Paten, Ryan Layer, Chen-Shan Chin, Fritz J. + Sedlazeck, Nancy F. Hansen, Danny E. Miller, Adam M. Phillippy, Karen H. + Miga, Rajiv C. McCoy, Megan Y. Dennis, Justin M. Zook, Michael C. + Schatz" + - Title: "Jasmine and Iris: population-scale structural variant comparison + and analysis" + URL: "https://doi.org/10.1038/s41592-022-01753-3" + AuthorName: "Melanie Kirsche, Gautam Prabhu, Rachel Sherman, Bohan Ni, + Alexis Battle, Sergey Aganezov, Michael C. Schatz" + - Title: "A draft human pangenome reference" + URL: "https://www.nature.com/articles/s41586-023-05896-x" + AuthorName: "Wen-Wei Liao, Mobin Asri, Jana Ebler, Daniel Doerr, Marina + Haukness, Glenn Hickey, Shuangjia Lu, Julian K. Lucas, Jean Monlong, + Haley J. Abel, Silvia Buonaiuto, Xian H. Chang, Haoyu Cheng, Justin Chu, + Vincenza Colonna, Jordan M. Eizenga, Xiaowen Feng, Christian Fischer, + Robert S. Fulton, Shilpa Garg, Cristian Groza, Andrea Guarracino, + William T. Harvey, Simon Heumos, Kerstin Howe, Miten Jain, Tsung-Yu Lu, + Charles Markello, Fergal J. Martin, Matthew W. Mitchell, Katherine M. + Munson, Moses Njagi Mwaniki, Adam M. Novak, Hugh E. Olsen, Trevor + Pesout, David Porubsky, Pjotr Prins, Jonas A. Sibbesen, Jouni Sirén, + Chad Tomlinson, Flavia Villani, Mitchell R. Vollger, Lucinda L. + Antonacci-Fulton, Gunjan Baid, Carl A. Baker, Anastasiya Belyaeva, + Konstantinos Billis, Andrew Carroll, Pi-Chuan Chang, Sarah Cody, Daniel + E. Cook, Robert M. Cook-Deegan, Omar E. Cornejo, Mark Diekhans, Peter + Ebert, Susan Fairley, Olivier Fedrigo, Adam L. Felsenfeld, Giulio + Formenti, Adam Frankish, Yan Gao, Nanibaa’ A. Garrison, Carlos Garcia + Giron, Richard E. Green, Leanne Haggerty, Kendra Hoekzema, Thibaut + Hourlier, Hanlee P. Ji, Eimear E. Kenny, Barbara A. Koenig, Alexey + Kolesnikov, Jan O. Korbel, Jennifer Kordosky, Sergey Koren, HoJoon Lee, + Alexandra P. Lewis, Hugo Magalhães, Santiago Marco-Sola, Pierre Marijon, + Ann McCartney, Jennifer McDaniel, Jacquelyn Mountcastle, Maria + Nattestad, Sergey Nurk, Nathan D. Olson, Alice B. Popejoy, Daniela Puiu, + Mikko Rautiainen, Allison A. Regier, Arang Rhie, Samuel Sacco, Ashley D. + Sanders, Valerie A. Schneider, Baergen I. Schultz, Kishwar Shafin, + Michael W. Smith, Heidi J. Sofia, Ahmad N. Abou Tayoun, Françoise + Thibaud-Nissen, Francesca Floriana Tricomi, Justin Wagner, Brian Walenz, + Jonathan M. D. Wood, Aleksey V. Zimin, Guillaume Bourque, Mark J. P. + Chaisson, Paul Flicek, Adam M. Phillippy, Justin M. Zook, Evan E. + Eichler, David Haussler, Ting Wang, Erich D. Jarvis, Karen H. Miga, Erik + Garrison, Tobias Marschall, Ira M. Hall, Heng Li, Benedict Paten" + +ADXCategories: + - Healthcare & Life Sciences Data + diff --git a/datasets/aodn_animal_acoustic_tracking_delayed_qc.yaml b/datasets/aodn_animal_acoustic_tracking_delayed_qc.yaml index 3cc75d42e..07b78c759 100644 --- a/datasets/aodn_animal_acoustic_tracking_delayed_qc.yaml +++ b/datasets/aodn_animal_acoustic_tracking_delayed_qc.yaml @@ -23,10 +23,15 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - biodiversity Tags: -- oceans -- marine mammals -- biology + - aws-pds + - oceans + - marine mammals + - biology License: http://creativecommons.org/licenses/by/4.0/ Resources: - Description: Cloud Optimised AODN dataset of IMOS - Animal Tracking Facility - Acoustic @@ -38,12 +43,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - Animal Tracking Facility - Acoustic Tracking - Quality Controlled Detections (2007 - ongoing) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_acoustic_tracking_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_acoustic_tracking_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_acoustic_tracking_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_animal_ctd_satellite_relay_tagging_delayed_qc.yaml b/datasets/aodn_animal_ctd_satellite_relay_tagging_delayed_qc.yaml index 04be616b7..b0e740ceb 100644 --- a/datasets/aodn_animal_ctd_satellite_relay_tagging_delayed_qc.yaml +++ b/datasets/aodn_animal_ctd_satellite_relay_tagging_delayed_qc.yaml @@ -23,6 +23,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - biodiversity Tags: - oceans - marine mammals @@ -40,12 +44,12 @@ DataAtWork: Tutorials: - Title: Accessing Satellite Relay Tagging Program - Southern Ocean - MEOP Quality Controlled CTD Profiles - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_ctd_satellite_relay_tagging_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_ctd_satellite_relay_tagging_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/animal_ctd_satellite_relay_tagging_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_model_sea_level_anomaly_gridded_realtime.yaml b/datasets/aodn_model_sea_level_anomaly_gridded_realtime.yaml index 62d34c093..74f5261d8 100644 --- a/datasets/aodn_model_sea_level_anomaly_gridded_realtime.yaml +++ b/datasets/aodn_model_sea_level_anomaly_gridded_realtime.yaml @@ -7,12 +7,12 @@ DataAtWork: AuthorURL: https://github.com/aodn/aodn_cloud_optimised NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/model_sea_level_anomaly_gridded_realtime.ipynb Title: Accessing IMOS - OceanCurrent - Gridded sea level anomaly - Near real time - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/model_sea_level_anomaly_gridded_realtime.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/model_sea_level_anomaly_gridded_realtime.ipynb - AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb Description: "Gridded (adjusted) sea level anomaly (GSLA), gridded sea level (GSL)\ \ and surface geostrophic velocity (UCUR,VCUR) for the Australasian region. GSLA\ \ is mapped using optimal interpolation of detided, de-meaned, inverse-barometer-adjusted\ @@ -37,6 +37,10 @@ Resources: anomaly - Near real time Region: ap-southeast-2 Type: S3 Bucket +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean velocity diff --git a/datasets/aodn_mooring_ctd_delayed_qc.yaml b/datasets/aodn_mooring_ctd_delayed_qc.yaml index a6200feea..f7dcf3f4f 100644 --- a/datasets/aodn_mooring_ctd_delayed_qc.yaml +++ b/datasets/aodn_mooring_ctd_delayed_qc.yaml @@ -14,6 +14,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - chemistry @@ -27,12 +31,12 @@ Resources: DataAtWork: Tutorials: - Title: Accessing IMOS - Australian National Mooring Network (ANMN) - CTD Profiles - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_ctd_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_ctd_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_ctd_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_mooring_hourly_timeseries_delayed_qc.yaml b/datasets/aodn_mooring_hourly_timeseries_delayed_qc.yaml index d32f52eea..7af7dc113 100644 --- a/datasets/aodn_mooring_hourly_timeseries_delayed_qc.yaml +++ b/datasets/aodn_mooring_hourly_timeseries_delayed_qc.yaml @@ -22,6 +22,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - chemistry @@ -36,12 +40,12 @@ Resources: DataAtWork: Tutorials: - Title: Accessing IMOS - Moorings - Hourly time-series product - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_hourly_timeseries_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_hourly_timeseries_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_hourly_timeseries_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_mooring_satellite_altimetry_calibration_validation.yaml b/datasets/aodn_mooring_satellite_altimetry_calibration_validation.yaml index 0720d3e5c..3d7db8d1a 100644 --- a/datasets/aodn_mooring_satellite_altimetry_calibration_validation.yaml +++ b/datasets/aodn_mooring_satellite_altimetry_calibration_validation.yaml @@ -40,6 +40,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - climate Tags: - oceans - ocean currents @@ -54,12 +58,12 @@ Resources: DataAtWork: Tutorials: - Title: Accessing IMOS - SRS Satellite Altimetry Calibration and Validation Sub-Facility - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_satellite_altimetry_calibration_validation.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_satellite_altimetry_calibration_validation.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/mooring_satellite_altimetry_calibration_validation.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_bonneycoast_velocity_hourly_averaged_delayed_qc.yaml b/datasets/aodn_radar_bonneycoast_velocity_hourly_averaged_delayed_qc.yaml index dab12aea3..ab71ffd86 100644 --- a/datasets/aodn_radar_bonneycoast_velocity_hourly_averaged_delayed_qc.yaml +++ b/datasets/aodn_radar_bonneycoast_velocity_hourly_averaged_delayed_qc.yaml @@ -18,6 +18,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -33,12 +37,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Bonney Coast HF ocean radar site (South Australia, Australia) - Delayed mode sea water velocity - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_BonneyCoast_velocity_hourly_averaged_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_BonneyCoast_velocity_hourly_averaged_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_BonneyCoast_velocity_hourly_averaged_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_capricornbunkergroup_velocity_hourly_averaged_delayed_qc.yaml b/datasets/aodn_radar_capricornbunkergroup_velocity_hourly_averaged_delayed_qc.yaml index 8cfbfd3fd..cccf5485d 100644 --- a/datasets/aodn_radar_capricornbunkergroup_velocity_hourly_averaged_delayed_qc.yaml +++ b/datasets/aodn_radar_capricornbunkergroup_velocity_hourly_averaged_delayed_qc.yaml @@ -29,6 +29,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -45,12 +49,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Capricorn Bunker Group HF ocean radar site (Great Barrier Reef, Queensland, Australia) - Delayed mode sea water velocity - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_velocity_hourly_averaged_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_velocity_hourly_averaged_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_velocity_hourly_averaged_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_capricornbunkergroup_wave_delayed_qc.yaml b/datasets/aodn_radar_capricornbunkergroup_wave_delayed_qc.yaml index ce79a9139..7ed719d1b 100644 --- a/datasets/aodn_radar_capricornbunkergroup_wave_delayed_qc.yaml +++ b/datasets/aodn_radar_capricornbunkergroup_wave_delayed_qc.yaml @@ -29,6 +29,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -44,12 +48,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Capricorn Bunker Group HF ocean radar site (Great Barrier Reef, Queensland, Australia) - Delayed mode wave - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wave_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wave_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wave_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_capricornbunkergroup_wind_delayed_qc.yaml b/datasets/aodn_radar_capricornbunkergroup_wind_delayed_qc.yaml index cc0e67e30..4d97c7ecf 100644 --- a/datasets/aodn_radar_capricornbunkergroup_wind_delayed_qc.yaml +++ b/datasets/aodn_radar_capricornbunkergroup_wind_delayed_qc.yaml @@ -29,6 +29,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -45,12 +49,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Capricorn Bunker Group HF ocean radar site (Great Barrier Reef, Queensland, Australia) - Delayed mode wind - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wind_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wind_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CapricornBunkerGroup_wind_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_coffsharbour_velocity_hourly_averaged_delayed_qc.yaml b/datasets/aodn_radar_coffsharbour_velocity_hourly_averaged_delayed_qc.yaml index c625e26df..c6b0779ae 100644 --- a/datasets/aodn_radar_coffsharbour_velocity_hourly_averaged_delayed_qc.yaml +++ b/datasets/aodn_radar_coffsharbour_velocity_hourly_averaged_delayed_qc.yaml @@ -26,6 +26,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -41,12 +45,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Coffs Harbour HF ocean radar site (New South Wales, Australia) - Delayed mode sea water velocity - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_velocity_hourly_averaged_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_velocity_hourly_averaged_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_velocity_hourly_averaged_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_coffsharbour_wave_delayed_qc.yaml b/datasets/aodn_radar_coffsharbour_wave_delayed_qc.yaml index 91ca65cde..4155d3e90 100644 --- a/datasets/aodn_radar_coffsharbour_wave_delayed_qc.yaml +++ b/datasets/aodn_radar_coffsharbour_wave_delayed_qc.yaml @@ -26,6 +26,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -40,12 +44,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Coffs Harbour HF ocean radar site (New South Wales, Australia) - Delayed mode wave - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wave_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wave_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wave_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_coffsharbour_wind_delayed_qc.yaml b/datasets/aodn_radar_coffsharbour_wind_delayed_qc.yaml index 65bd8351c..075ac6293 100644 --- a/datasets/aodn_radar_coffsharbour_wind_delayed_qc.yaml +++ b/datasets/aodn_radar_coffsharbour_wind_delayed_qc.yaml @@ -41,12 +41,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Coffs Harbour HF ocean radar site (New South Wales, Australia) - Delayed mode wind - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wind_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wind_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoffsHarbour_wind_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_coralcoast_velocity_hourly_averaged_delayed_qc.yaml b/datasets/aodn_radar_coralcoast_velocity_hourly_averaged_delayed_qc.yaml index 25d372fbb..027e8d906 100644 --- a/datasets/aodn_radar_coralcoast_velocity_hourly_averaged_delayed_qc.yaml +++ b/datasets/aodn_radar_coralcoast_velocity_hourly_averaged_delayed_qc.yaml @@ -21,6 +21,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -36,12 +40,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Coral Coast HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoralCoast_velocity_hourly_averaged_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoralCoast_velocity_hourly_averaged_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_CoralCoast_velocity_hourly_averaged_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_newcastle_velocity_hourly_averaged_delayed_qc.yaml b/datasets/aodn_radar_newcastle_velocity_hourly_averaged_delayed_qc.yaml index 79f62c3f6..457a06109 100644 --- a/datasets/aodn_radar_newcastle_velocity_hourly_averaged_delayed_qc.yaml +++ b/datasets/aodn_radar_newcastle_velocity_hourly_averaged_delayed_qc.yaml @@ -15,7 +15,12 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: +- aws-pds - oceans - ocean currents - ocean velocity @@ -30,12 +35,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Newcastle HF ocean radar site (New South Wales, Australia) - Delayed mode sea water velocity - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_Newcastle_velocity_hourly_averaged_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_Newcastle_velocity_hourly_averaged_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_Newcastle_velocity_hourly_averaged_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_northwestshelf_velocity_hourly_averaged_delayed_qc.yaml b/datasets/aodn_radar_northwestshelf_velocity_hourly_averaged_delayed_qc.yaml index 611f7d002..b66b85a27 100644 --- a/datasets/aodn_radar_northwestshelf_velocity_hourly_averaged_delayed_qc.yaml +++ b/datasets/aodn_radar_northwestshelf_velocity_hourly_averaged_delayed_qc.yaml @@ -14,6 +14,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -29,12 +33,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Northwest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_NorthWestShelf_velocity_hourly_averaged_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_NorthWestShelf_velocity_hourly_averaged_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_NorthWestShelf_velocity_hourly_averaged_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_rottnestshelf_velocity_hourly_averaged_delayed_qc.yaml b/datasets/aodn_radar_rottnestshelf_velocity_hourly_averaged_delayed_qc.yaml index dee0b34df..723672d01 100644 --- a/datasets/aodn_radar_rottnestshelf_velocity_hourly_averaged_delayed_qc.yaml +++ b/datasets/aodn_radar_rottnestshelf_velocity_hourly_averaged_delayed_qc.yaml @@ -21,6 +21,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -36,12 +40,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Rottnest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_velocity_hourly_averaged_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_velocity_hourly_averaged_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_velocity_hourly_averaged_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_rottnestshelf_wave_delayed_qc.yaml b/datasets/aodn_radar_rottnestshelf_wave_delayed_qc.yaml index 2c57e130b..7488e2c72 100644 --- a/datasets/aodn_radar_rottnestshelf_wave_delayed_qc.yaml +++ b/datasets/aodn_radar_rottnestshelf_wave_delayed_qc.yaml @@ -21,6 +21,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -35,12 +39,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Rottnest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode wave - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wave_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wave_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wave_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_rottnestshelf_wind_delayed_qc.yaml b/datasets/aodn_radar_rottnestshelf_wind_delayed_qc.yaml index 020ffc1cc..dc515048c 100644 --- a/datasets/aodn_radar_rottnestshelf_wind_delayed_qc.yaml +++ b/datasets/aodn_radar_rottnestshelf_wind_delayed_qc.yaml @@ -21,6 +21,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -36,12 +40,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Rottnest Shelf HF ocean radar site (Western Australia, Australia) - Delayed mode wind - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wind_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wind_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_RottnestShelf_wind_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_southaustraliagulfs_velocity_hourly_averaged_delayed_qc.yaml b/datasets/aodn_radar_southaustraliagulfs_velocity_hourly_averaged_delayed_qc.yaml index e086d7f2b..931cb0297 100644 --- a/datasets/aodn_radar_southaustraliagulfs_velocity_hourly_averaged_delayed_qc.yaml +++ b/datasets/aodn_radar_southaustraliagulfs_velocity_hourly_averaged_delayed_qc.yaml @@ -28,6 +28,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -43,12 +47,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - South Australia Gulfs HF ocean radar site (South Australia, Australia) - Delayed mode sea water velocity - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_velocity_hourly_averaged_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_southaustraliagulfs_wave_delayed_qc.yaml b/datasets/aodn_radar_southaustraliagulfs_wave_delayed_qc.yaml index 9e493cb21..2bf2020b1 100644 --- a/datasets/aodn_radar_southaustraliagulfs_wave_delayed_qc.yaml +++ b/datasets/aodn_radar_southaustraliagulfs_wave_delayed_qc.yaml @@ -28,6 +28,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -42,12 +46,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - South Australia Gulfs HF ocean radar site (South Australia, Australia) - Delayed mode wave - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wave_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wave_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wave_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_southaustraliagulfs_wind_delayed_qc.yaml b/datasets/aodn_radar_southaustraliagulfs_wind_delayed_qc.yaml index e7ca87294..0ff3805f3 100644 --- a/datasets/aodn_radar_southaustraliagulfs_wind_delayed_qc.yaml +++ b/datasets/aodn_radar_southaustraliagulfs_wind_delayed_qc.yaml @@ -43,12 +43,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - South Australia Gulfs HF ocean radar site (South Australia, Australia) - Delayed mode wind - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wind_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wind_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_SouthAustraliaGulfs_wind_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_radar_turquoisecoast_velocity_hourly_averaged_delayed_qc.yaml b/datasets/aodn_radar_turquoisecoast_velocity_hourly_averaged_delayed_qc.yaml index 7ddf2f30e..2eb96c0b5 100644 --- a/datasets/aodn_radar_turquoisecoast_velocity_hourly_averaged_delayed_qc.yaml +++ b/datasets/aodn_radar_turquoisecoast_velocity_hourly_averaged_delayed_qc.yaml @@ -34,6 +34,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -45,12 +49,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - ACORN - Turquoise Coast HF ocean radar site (Western Australia, Australia) - Delayed mode sea water velocity - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_TurquoiseCoast_velocity_hourly_averaged_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_TurquoiseCoast_velocity_hourly_averaged_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/radar_TurquoiseCoast_velocity_hourly_averaged_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_carder_1day_aqua.yaml b/datasets/aodn_satellite_chlorophylla_carder_1day_aqua.yaml index f3b91fa06..18d16d2d4 100644 --- a/datasets/aodn_satellite_chlorophylla_carder_1day_aqua.yaml +++ b/datasets/aodn_satellite_chlorophylla_carder_1day_aqua.yaml @@ -16,6 +16,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -31,12 +35,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (Carder model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_carder_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_carder_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_carder_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_gsm_1day_aqua.yaml b/datasets/aodn_satellite_chlorophylla_gsm_1day_aqua.yaml index ffeb65205..2b76dec58 100644 --- a/datasets/aodn_satellite_chlorophylla_gsm_1day_aqua.yaml +++ b/datasets/aodn_satellite_chlorophylla_gsm_1day_aqua.yaml @@ -11,6 +11,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -26,12 +30,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (GSM model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_gsm_1day_noaa20.yaml b/datasets/aodn_satellite_chlorophylla_gsm_1day_noaa20.yaml index 04bbeb008..a1a854290 100644 --- a/datasets/aodn_satellite_chlorophylla_gsm_1day_noaa20.yaml +++ b/datasets/aodn_satellite_chlorophylla_gsm_1day_noaa20.yaml @@ -11,6 +11,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -26,12 +30,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Chlorophyll-a concentration (GSM model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_noaa20.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_noaa20.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_noaa20.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_gsm_1day_snpp.yaml b/datasets/aodn_satellite_chlorophylla_gsm_1day_snpp.yaml index 8463f6a98..406690b80 100644 --- a/datasets/aodn_satellite_chlorophylla_gsm_1day_snpp.yaml +++ b/datasets/aodn_satellite_chlorophylla_gsm_1day_snpp.yaml @@ -11,6 +11,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -26,12 +30,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - Satellite Remote Sensing - SNPP - 01 day - Chlorophyll-a concentration (GSM model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_snpp.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_snpp.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_gsm_1day_snpp.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_oc3_1day_aqua.yaml b/datasets/aodn_satellite_chlorophylla_oc3_1day_aqua.yaml index c1f0c76e8..0ce96b25a 100644 --- a/datasets/aodn_satellite_chlorophylla_oc3_1day_aqua.yaml +++ b/datasets/aodn_satellite_chlorophylla_oc3_1day_aqua.yaml @@ -12,6 +12,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -27,12 +31,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (OC3 model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_oc3_1day_noaa20.yaml b/datasets/aodn_satellite_chlorophylla_oc3_1day_noaa20.yaml index 15561fee7..a017b666f 100644 --- a/datasets/aodn_satellite_chlorophylla_oc3_1day_noaa20.yaml +++ b/datasets/aodn_satellite_chlorophylla_oc3_1day_noaa20.yaml @@ -12,6 +12,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -27,12 +31,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Chlorophyll-a concentration (OC3 model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_noaa20.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_noaa20.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_noaa20.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_oc3_1day_snpp.yaml b/datasets/aodn_satellite_chlorophylla_oc3_1day_snpp.yaml index 21e308746..bf5b513ee 100644 --- a/datasets/aodn_satellite_chlorophylla_oc3_1day_snpp.yaml +++ b/datasets/aodn_satellite_chlorophylla_oc3_1day_snpp.yaml @@ -12,6 +12,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -27,12 +31,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - Satellite Remote Sensing - SNPP - 01 day - Chlorophyll-a concentration (OC3 model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_snpp.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_snpp.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oc3_1day_snpp.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_oci_1day_aqua.yaml b/datasets/aodn_satellite_chlorophylla_oci_1day_aqua.yaml index 1dfa47ee8..d040491b1 100644 --- a/datasets/aodn_satellite_chlorophylla_oci_1day_aqua.yaml +++ b/datasets/aodn_satellite_chlorophylla_oci_1day_aqua.yaml @@ -12,6 +12,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -27,12 +31,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Chlorophyll-a concentration (OCI model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_oci_1day_noaa20.yaml b/datasets/aodn_satellite_chlorophylla_oci_1day_noaa20.yaml index 4e4e0d548..dd4117717 100644 --- a/datasets/aodn_satellite_chlorophylla_oci_1day_noaa20.yaml +++ b/datasets/aodn_satellite_chlorophylla_oci_1day_noaa20.yaml @@ -12,6 +12,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -27,12 +31,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Chlorophyll-a concentration (OCI model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_noaa20.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_noaa20.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_noaa20.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_chlorophylla_oci_1day_snpp.yaml b/datasets/aodn_satellite_chlorophylla_oci_1day_snpp.yaml index cbd80b556..ae98aa384 100644 --- a/datasets/aodn_satellite_chlorophylla_oci_1day_snpp.yaml +++ b/datasets/aodn_satellite_chlorophylla_oci_1day_snpp.yaml @@ -12,6 +12,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -27,12 +31,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - Satellite Remote Sensing - SNPP - 01 day - Chlorophyll-a concentration (OCI model) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_snpp.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_snpp.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_chlorophylla_oci_1day_snpp.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_aqua.yaml b/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_aqua.yaml index bcab1b679..f8a6efe64 100644 --- a/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_aqua.yaml +++ b/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_aqua.yaml @@ -11,6 +11,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -25,12 +29,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Diffuse attenuation coefficient (k490 ) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_noaa20.yaml b/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_noaa20.yaml index 4fb0a6a63..347e2e0d3 100644 --- a/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_noaa20.yaml +++ b/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_noaa20.yaml @@ -12,6 +12,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -27,12 +31,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - Satellite Remote Sensing - NOAA20 - 01 day - Diffuse attenuation coefficient (k490) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_noaa20.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_noaa20.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_noaa20.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_snpp.yaml b/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_snpp.yaml index 5f010d89f..cf3e34ab9 100644 --- a/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_snpp.yaml +++ b/datasets/aodn_satellite_diffuse_attenuation_coefficent_1day_snpp.yaml @@ -11,6 +11,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -26,12 +30,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - Satellite Remote Sensing - SNPP - 01 day - Diffuse attenuation coefficient (k490) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_snpp.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_snpp.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_diffuse_attenuation_coefficent_1day_snpp.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_ghrsst_l3c_1day_nighttime_himawari8.yaml b/datasets/aodn_satellite_ghrsst_l3c_1day_nighttime_himawari8.yaml index dad13e67a..83f66d92c 100644 --- a/datasets/aodn_satellite_ghrsst_l3c_1day_nighttime_himawari8.yaml +++ b/datasets/aodn_satellite_ghrsst_l3c_1day_nighttime_himawari8.yaml @@ -23,6 +23,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -36,12 +40,12 @@ Resources: DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - SST - L3C - Himawari-8 - 1 day - night time - Australia - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3c_1day_nighttime_himawari8.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3c_1day_nighttime_himawari8.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3c_1day_nighttime_himawari8.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.yaml b/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.yaml index 5af507889..268b31e44 100644 --- a/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.yaml +++ b/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.yaml @@ -21,6 +21,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -35,12 +39,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - SST - L3S - Multi Sensor - 1 day - day and night time - Australia - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_multi_sensor_australia.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.yaml b/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.yaml index df88d49d6..087232afd 100644 --- a/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.yaml +++ b/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.yaml @@ -33,12 +33,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - SST - L3S - Single Sensor - 1 day - day and night time - Australia - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.yaml b/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.yaml index 148231ba2..f81f1e8c0 100644 --- a/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.yaml +++ b/datasets/aodn_satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.yaml @@ -18,6 +18,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -32,12 +36,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - SST - L3S - Single Sensor - 1 day - day and night time - Southern Ocean - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_southernocean.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.yaml b/datasets/aodn_satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.yaml index 39ddf0ec2..bfeaf5479 100644 --- a/datasets/aodn_satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.yaml +++ b/datasets/aodn_satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.yaml @@ -16,6 +16,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -30,12 +34,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - SST - L3S - Single Sensor - 1 month - day time - Australia - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_1month_daytime_single_sensor_australia.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.yaml b/datasets/aodn_satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.yaml index e33ef6710..0bf089d00 100644 --- a/datasets/aodn_satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.yaml +++ b/datasets/aodn_satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.yaml @@ -21,6 +21,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -35,12 +39,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - SST - L3S - Multi Sensor - 3 day - day and night time - Australia - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_3day_daynighttime_multi_sensor_australia.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.yaml b/datasets/aodn_satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.yaml index 84276551c..fdc735c5d 100644 --- a/datasets/aodn_satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.yaml +++ b/datasets/aodn_satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.yaml @@ -19,6 +19,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -33,12 +37,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - SST - L3S - Single Sensor - 6 day - day and night time - Australia - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l3s_6day_daynighttime_single_sensor_australia.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.yaml b/datasets/aodn_satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.yaml index 239d8bb9d..e22e952e3 100644 --- a/datasets/aodn_satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.yaml +++ b/datasets/aodn_satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.yaml @@ -16,6 +16,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -28,12 +32,12 @@ Resources: DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - SST - L4 - GAMSSA - World - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_gamssa_1day_multi_sensor_world.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.yaml b/datasets/aodn_satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.yaml index 0946d2f66..957d24a05 100644 --- a/datasets/aodn_satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.yaml +++ b/datasets/aodn_satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.yaml @@ -17,6 +17,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -29,12 +33,12 @@ Resources: DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - SST - L4 - RAMSSA - Australia - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_ghrsst_l4_ramssa_1day_multi_sensor_australia.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_nanoplankton_fraction_oc3_1day_aqua.yaml b/datasets/aodn_satellite_nanoplankton_fraction_oc3_1day_aqua.yaml index 834877ef9..454c62d6d 100644 --- a/datasets/aodn_satellite_nanoplankton_fraction_oc3_1day_aqua.yaml +++ b/datasets/aodn_satellite_nanoplankton_fraction_oc3_1day_aqua.yaml @@ -20,6 +20,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: FILL UP MANUALLY - CHECK DOCUMENTATION ManagedBy: FILL UP MANUALLY - CHECK DOCUMENTATION UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -35,12 +39,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Nanoplankton fraction (OC3 model and Brewin et al 2012 algorithm) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_nanoplankton_fraction_oc3_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_nanoplankton_fraction_oc3_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_nanoplankton_fraction_oc3_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_net_primary_productivity_gsm_1day_aqua.yaml b/datasets/aodn_satellite_net_primary_productivity_gsm_1day_aqua.yaml index 95de13c6a..4508e5c89 100644 --- a/datasets/aodn_satellite_net_primary_productivity_gsm_1day_aqua.yaml +++ b/datasets/aodn_satellite_net_primary_productivity_gsm_1day_aqua.yaml @@ -28,6 +28,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -43,12 +47,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Net Primary Productivity (GSM model and Eppley-VGPM algorithm) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_gsm_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_gsm_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_gsm_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_net_primary_productivity_oc3_1day_aqua.yaml b/datasets/aodn_satellite_net_primary_productivity_oc3_1day_aqua.yaml index 8a22541eb..cccbebe11 100644 --- a/datasets/aodn_satellite_net_primary_productivity_oc3_1day_aqua.yaml +++ b/datasets/aodn_satellite_net_primary_productivity_oc3_1day_aqua.yaml @@ -32,6 +32,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -47,12 +51,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Net Primary Productivity (OC3 model and Eppley-VGPM algorithm) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_oc3_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_oc3_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_net_primary_productivity_oc3_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_optical_water_type_1day_aqua.yaml b/datasets/aodn_satellite_optical_water_type_1day_aqua.yaml index 2a6dc519e..668f35298 100644 --- a/datasets/aodn_satellite_optical_water_type_1day_aqua.yaml +++ b/datasets/aodn_satellite_optical_water_type_1day_aqua.yaml @@ -16,6 +16,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -30,12 +34,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Optical Water Type (Moore et al 2009 algorithm) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_optical_water_type_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_optical_water_type_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_optical_water_type_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_satellite_picoplankton_fraction_oc3_1day_aqua.yaml b/datasets/aodn_satellite_picoplankton_fraction_oc3_1day_aqua.yaml index 5d3711950..f2b4de443 100644 --- a/datasets/aodn_satellite_picoplankton_fraction_oc3_1day_aqua.yaml +++ b/datasets/aodn_satellite_picoplankton_fraction_oc3_1day_aqua.yaml @@ -20,6 +20,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: FILL UP MANUALLY - CHECK DOCUMENTATION ManagedBy: FILL UP MANUALLY - CHECK DOCUMENTATION UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - satellite imagery @@ -35,12 +39,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SRS - MODIS - 01 day - Picoplankton fraction (OC3 model and Brewin et al 2012 algorithm) - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_picoplankton_fraction_oc3_1day_aqua.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_picoplankton_fraction_oc3_1day_aqua.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/satellite_picoplankton_fraction_oc3_1day_aqua.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_slocum_glider_delayed_qc.yaml b/datasets/aodn_slocum_glider_delayed_qc.yaml index 8fb0a445f..cb8e4e9d2 100644 --- a/datasets/aodn_slocum_glider_delayed_qc.yaml +++ b/datasets/aodn_slocum_glider_delayed_qc.yaml @@ -31,6 +31,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - ocean currents @@ -50,12 +54,12 @@ Resources: DataAtWork: Tutorials: - Title: Accessing Ocean Gliders - delayed mode glider deployments - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/slocum_glider_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/slocum_glider_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/slocum_glider_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_vessel_air_sea_flux_product_delayed.yaml b/datasets/aodn_vessel_air_sea_flux_product_delayed.yaml index 27b48385e..ab6d85bf4 100644 --- a/datasets/aodn_vessel_air_sea_flux_product_delayed.yaml +++ b/datasets/aodn_vessel_air_sea_flux_product_delayed.yaml @@ -21,6 +21,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - air temperature @@ -36,12 +40,12 @@ Resources: DataAtWork: Tutorials: - Title: 'Accessing IMOS-SOOP-Air Sea Flux: Flux product' - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_product_delayed.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_product_delayed.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_product_delayed.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_vessel_air_sea_flux_sst_meteo_realtime.yaml b/datasets/aodn_vessel_air_sea_flux_sst_meteo_realtime.yaml index 0c94b837e..d2cbcdbe3 100644 --- a/datasets/aodn_vessel_air_sea_flux_sst_meteo_realtime.yaml +++ b/datasets/aodn_vessel_air_sea_flux_sst_meteo_realtime.yaml @@ -21,6 +21,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - air temperature @@ -39,12 +43,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SOOP-Air Sea Flux (ASF) sub-facility - Meteorological and SST Observations - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_sst_meteo_realtime.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_sst_meteo_realtime.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_air_sea_flux_sst_meteo_realtime.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_vessel_co2_delayed_qc.yaml b/datasets/aodn_vessel_co2_delayed_qc.yaml index 6a803d98d..f89739374 100644 --- a/datasets/aodn_vessel_co2_delayed_qc.yaml +++ b/datasets/aodn_vessel_co2_delayed_qc.yaml @@ -19,6 +19,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - climate Tags: - oceans - chemistry @@ -35,12 +39,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SOOP Underway CO2 Measurements Research Group - delayed mode data - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_co2_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_co2_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_co2_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_vessel_fishsoop_realtime_qc.yaml b/datasets/aodn_vessel_fishsoop_realtime_qc.yaml index b9d6dad63..f8d46faad 100644 --- a/datasets/aodn_vessel_fishsoop_realtime_qc.yaml +++ b/datasets/aodn_vessel_fishsoop_realtime_qc.yaml @@ -11,12 +11,12 @@ DataAtWork: NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_fishsoop_realtime_qc.ipynb Title: Accessing IMOS SOOP - Fisheries Vessels as Ships of Opportunity Sub-Facility - Real-time data - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_fishsoop_realtime_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_fishsoop_realtime_qc.ipynb - AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb Description: "Fisheries Vessels as Ships of Opportunities (FishSOOP) is an IMOS Sub-Facility\ \ working with fishers to collect real-time temperature and depth data by installing\ \ equipment on a network of commercial fishing vessels using a range of common fishing\ @@ -47,6 +47,10 @@ Resources: of Opportunity Sub-Facility - Real-time data Region: ap-southeast-2 Type: S3 Bucket +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans UpdateFrequency: As Needed diff --git a/datasets/aodn_vessel_sst_delayed_qc.yaml b/datasets/aodn_vessel_sst_delayed_qc.yaml index db64be654..ec94218ad 100644 --- a/datasets/aodn_vessel_sst_delayed_qc.yaml +++ b/datasets/aodn_vessel_sst_delayed_qc.yaml @@ -25,6 +25,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - air temperature @@ -39,12 +43,12 @@ Resources: DataAtWork: Tutorials: - Title: Accessing IMOS - SOOP Sea Surface Temperature - delayed mode data - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_sst_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_sst_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_sst_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_vessel_trv_realtime_qc.yaml b/datasets/aodn_vessel_trv_realtime_qc.yaml index dc53ea084..539952e17 100644 --- a/datasets/aodn_vessel_trv_realtime_qc.yaml +++ b/datasets/aodn_vessel_trv_realtime_qc.yaml @@ -26,6 +26,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans - chemistry @@ -40,12 +44,12 @@ DataAtWork: Tutorials: - Title: 'Accessing Sensors on Tropical Research Vessels: Enhanced Measurements from Ships of Opportunity (SOOP)' - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_trv_realtime_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_trv_realtime_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_trv_realtime_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_vessel_xbt_delayed_qc.yaml b/datasets/aodn_vessel_xbt_delayed_qc.yaml index 83978f57c..2c5d6753a 100644 --- a/datasets/aodn_vessel_xbt_delayed_qc.yaml +++ b/datasets/aodn_vessel_xbt_delayed_qc.yaml @@ -12,6 +12,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans License: http://creativecommons.org/licenses/by/4.0/ @@ -25,12 +29,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SOOP Expendable Bathythermographs (XBT) Research Group - XBT delayed mode data - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_delayed_qc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_delayed_qc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_delayed_qc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_vessel_xbt_realtime_nonqc.yaml b/datasets/aodn_vessel_xbt_realtime_nonqc.yaml index dfd4a4c7b..27515adee 100644 --- a/datasets/aodn_vessel_xbt_realtime_nonqc.yaml +++ b/datasets/aodn_vessel_xbt_realtime_nonqc.yaml @@ -16,6 +16,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans License: http://creativecommons.org/licenses/by/4.0/ @@ -29,12 +33,12 @@ DataAtWork: Tutorials: - Title: Accessing IMOS - SOOP Expendable Bathythermographs (XBT) Research Group - XBT real-time data - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_realtime_nonqc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_realtime_nonqc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/vessel_xbt_realtime_nonqc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/aodn_wave_buoy_realtime_nonqc.yaml b/datasets/aodn_wave_buoy_realtime_nonqc.yaml index a9b779924..6ca6272e9 100644 --- a/datasets/aodn_wave_buoy_realtime_nonqc.yaml +++ b/datasets/aodn_wave_buoy_realtime_nonqc.yaml @@ -31,6 +31,10 @@ Documentation: https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.sea Contact: info@aodn.org.au ManagedBy: AODN UpdateFrequency: As Needed +Collabs: + ASDI: + Tags: + - oceans Tags: - oceans License: http://creativecommons.org/licenses/by/4.0/ @@ -43,12 +47,12 @@ Resources: DataAtWork: Tutorials: - Title: Accessing Wave buoys Observations - Australia - near real-time - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/wave_buoy_realtime_nonqc.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/wave_buoy_realtime_nonqc.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/wave_buoy_realtime_nonqc.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised - Title: Accessing and search for any AODN dataset - URL: https://nbviewer.org/github/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb + URL: https://github.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb NotebookURL: https://githubtocolab.com/aodn/aodn_cloud_optimised/blob/main/notebooks/GetAodnData.ipynb AuthorName: Laurent Besnard AuthorURL: https://github.com/aodn/aodn_cloud_optimised diff --git a/datasets/apex.yaml b/datasets/apex.yaml new file mode 100644 index 000000000..bb09598ea --- /dev/null +++ b/datasets/apex.yaml @@ -0,0 +1,50 @@ +Name: APEX-CONNECTS +Description: > + The BRAIN Initiative Connectivity Across Scales (CONNECTS) program is working to create detailed maps of brain + wiring across different species and scales, using advanced imaging technologies. + APEX supports this effort by serving as a central hub that brings together and coordinates data and tools + from research focused on brain connectivity in humans and animals. Together, these efforts aim to improve our + understanding of how the brain is structured and functions. +Documentation: https://brainlife.io +Contact: brainlife.io@gmail.com +ManagedBy: "[Brainlife Team](https://brainlife.io/team/)" +UpdateFrequency: New datasets are added monthly +Tags: + - neuroscience + - neuroimaging + - microscopy + - life sciences + - zarr + - metadata + - machine learning + - infrastructure + - json + - imaging + - brain images + - brain models + - analysis ready data + - nifti + - aws-pds +License: '[CC BY](https://creativecommons.org/licenses/by/4.0)' +Citation: +Resources: + - Description: All APEX datasets are available for download + ARN: arn:aws:s3:::apex-connects + Region: us-east-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Brainlife AWS Tutorials + URL: https://brainlife.io/docs/tutorial/aws-brainlife + AuthorName: Brainlife + AuthorURL: https://brainlife.io + Tools & Applications: + - Title: Brainlife Web App + URL: https://brainlife.io + AuthorName: Brainlife + AuthorURL: https://brainlife.io + - Title: Brainlife CLI (Command Line Interface) + URL: https://github.com/brainlife/cli + AuthorName: Brainlife + AuthorURL: https://github.com/brainlife/cli + Publications: diff --git a/datasets/argoverse.yaml b/datasets/argoverse.yaml index 0a5c3b25c..1d399e525 100644 --- a/datasets/argoverse.yaml +++ b/datasets/argoverse.yaml @@ -20,6 +20,10 @@ Documentation: https://argoverse.github.io/user-guide/ Contact: https://github.com/argoverse/av2-api/issues ManagedBy: "[Argoverse](https://argoverse.org)" UpdateFrequency: Infrequently +Collabs: + ASDI: + Tags: + - infrastructure Tags: - aws-pds - autonomous vehicles diff --git a/datasets/asf-event-data.yaml b/datasets/asf-event-data.yaml index 77af4863a..163163126 100644 --- a/datasets/asf-event-data.yaml +++ b/datasets/asf-event-data.yaml @@ -10,6 +10,10 @@ Contact: https://asf.alaska.edu/asf/contact-us/ ManagedBy: "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)" UpdateFrequency: > Irregular, in response to disaster events +Collabs: + ASDI: + Tags: + - disaster response Tags: - aws-pds - disaster response diff --git a/datasets/askap.yaml b/datasets/askap.yaml new file mode 100644 index 000000000..252758039 --- /dev/null +++ b/datasets/askap.yaml @@ -0,0 +1,49 @@ +Name: ASKAP Radio Telescope +Description: | + + ASKAP is the CSIRO’s newest radio telescope. It is situated at the Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy Observatory on Wajarri Yamaji Country in the Murchison region of Western Australia, about 800 km north of Perth. + + ASKAP consists of 36 12m dishes, spread-out as far as 6km apart. It uses a new technology called Phased Array Feeds (PAFs), which allows it to see more of the sky at once. This novel technology allows ASKAP to achieve extremely high survey speed, making it one of the best instruments in the world for mapping the sky at radio wavelengths. + + Initial dataset available - The Rapid ASKAP Continuum Survey (RACS) + + RACS is the first large-area survey completed with ASKAP. This survey is revolutionary as the entire sky was observed in a matter of weeks, doing what previously took telescopes years to do. RACS initially covered the whole sky at 890 MHz (RACS-Low), and has since expanded to ASKAP’s other bands (1.4 and 1.7 GHz). RACS also covers the sky in multiple epochs, with a second epoch of RACS-Low and RACS-Mid obtained and processed. + + RACS provides astronomers with a unique opportunity to study the radio sky and radio populations, in particular supermassive blackholes (active galactic nuclei) and their role in galaxy evolution. The multi-epoch approach also allows a study of the transient sky and testing and verification of calibration methods. The large area allows for cosmological studies, such as a search for anisotropy in the galaxy population, or cosmic dipole. + +Documentation: https://www.atnf.csiro.au/facilities/askap-radio-telescope/ +Contact: atnf-datasup@csiro.au +ManagedBy: "[Australia Telescope National Facility, CSIRO](http://www.atnf.csiro.au/)" +Citation: Please see the [ATNF acknowledgement page](https://www.atnf.csiro.au/resources/publications/atnf-publication-acknowledgement-statements/) for full citation instructions. +UpdateFrequency: Roughly quarterly +Tags: + - aws-pds + - astronomy + - archives +License: CC-BY-4.0. Attribution required for refereed scientific papers. +Resources: + - Description: The Rapid ASKAP Continuum Survey (RACS) Public Data Releases + ARN: arn:aws:s3:::askap-odp/racs-low1/ + Region: ap-southeast-2 + Type: S3 Bucket + RequesterPays: False + - Description: Notifications for new ASKAP data + ARN: arn:aws:sns:ap-southeast-2:336305517014:askap-odp-object_created + Region: ap-southeast-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: CSIRO ASKAP Science Data Archive User Guide + URL: https://research.csiro.au/casda/casda-user-guide/ + AuthorName: CSIRO, ATNF + - Title: Rapid Askap Continuum Survey (RACS) Home Page + URL: https://research.csiro.au/racs/ + AuthorName: CSIRO, ATNF + Tools & Applications: + Publications: + - Title: ASKAP Publication List + URL: https://www.atnf.csiro.au/facilities/askap-radio-telescope/publications/ + AuthorName: various, list maintained by CSIRO, ATNF + - Title: ASKAP System Description paper + URL: https://doi.org/10.1017/pasa.2021.1 + AuthorName: Hotan, A. et al. diff --git a/datasets/asl_1000.yaml b/datasets/asl_1000.yaml new file mode 100644 index 000000000..6a77ccb89 --- /dev/null +++ b/datasets/asl_1000.yaml @@ -0,0 +1,22 @@ +Name: ASL 1000 +Description: This dataset provides a high-fidelity collection of American Sign Language (ASL) videos annotated with 2D landmarks for hands, pose, and face. The data is designed to train advanced research and development in ASL recognition, translation, gesture analysis, and computer animation. The annotations for this dataset were generated using an automated data pipeline to pre-annotate keyframes from the source videos. As a final, critical step, all automated annotations were subsequently reviewed and meticulously corrected by human labellers to ensure the highest level of accuracy and reliability, making it suitable for training production-grade machine learning models. +Documentation: "https://github.com/NVIDIA/Trustworthy-AI/tree/main/ASL%20Developer%20Community" +Contact: trustworthyaiprojects@nvidia.com +ManagedBy: "[NVIDIA Corporation](https://www.nvidia.com/en-us/)" +UpdateFrequency: New data added as soon as it is available. +Tags: + - aws-pds + - video + - machine learning +License: "Please see the [NVIDIA Dataset License](https://github.com/NVIDIA/Trustworthy-AI/blob/main/ASL%20Developer%20Community/NVIDIA%20Data%20License%20for%20ASL%20Project.pdf)" +DataAtWork: + Tutorials: + - Title: ASL Data Pipeline + URL: https://github.com/NVIDIA/Trustworthy-AI/blob/main/ASL%20Developer%20Community/notebooks/asl_data_pipeline.ipynb + AuthorName: "NVIDIA" +Resources: + - Description: ASL 1000 Data + ARN: arn:aws:s3:::trustworthyaiproduct + Region: us-east-2 + Type: S3 Bucket + ControlledAccess: https://www.nvidia.com/en-us/gated-resources/trustworthy-ai-american-sign-language/dataset/ diff --git a/datasets/asset-data-igp-coal-plant.yaml b/datasets/asset-data-igp-coal-plant.yaml index 8da1197bc..86492ebca 100644 --- a/datasets/asset-data-igp-coal-plant.yaml +++ b/datasets/asset-data-igp-coal-plant.yaml @@ -5,6 +5,10 @@ Contact: https://github.com/APAD2024/APAD-Asset-Data/issues ManagedBy: APAD UpdateFrequency: as needed +Collabs: + ASDI: + Tags: + - energy Tags: - air quality - energy diff --git a/datasets/aster-l1t.yaml b/datasets/aster-l1t.yaml index da2b1ce98..351660714 100644 --- a/datasets/aster-l1t.yaml +++ b/datasets/aster-l1t.yaml @@ -19,8 +19,8 @@ Description: | [here](https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t). Documentation: https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t -Contact: opendata@descarteslabs.com -ManagedBy: "[Descartes Labs](https://descarteslabs.com/)" +Contact: support@earthdaily.com +ManagedBy: "[EarthDaily](https://earthdaily.com/)" UpdateFrequency: Daily Collabs: ASDI: @@ -51,14 +51,14 @@ Resources: DataAtWork: Tutorials: - Title: Working with ASTER L1T Visible and Near Infrared (VNIR) Data in R - URL: https://lpdaac.usgs.gov/documents/128/ASTER_L1T_Tutorial.html + URL: https://git.earthdata.nasa.gov/projects/LPDUR/repos/aster-l1t/browse AuthorName: Cole Krehbiel - AuthorURL: https://lpdaac.usgs.gov/ + AuthorURL: https://www.earthdata.nasa.gov/centers/lp-daac Tools & Applications: - - Title: Descartes Labs Platform - URL: https://descarteslabs.com/platform - AuthorName: Descartes Labs Inc. - AuthorURL: https://descarteslabs.com + - Title: EarthDaily EarthOne Platform + URL: https://docs.earthone.earthdaily.com/ + AuthorName: EarthDaily + AuthorURL: https://earthdaily.com - Title: Latitude-Longitude to Path-Row conversion URL: "https://landsat.usgs.gov/landsat_acq#convertPathRow" AuthorName: USGS @@ -79,4 +79,3 @@ DataAtWork: - Title: ASTER L1T Product Specification URL: https://lpdaac.usgs.gov/documents/1401/ASTER_L1T_Product_Specification_v1.pdf AuthorName: USGS EROS Data Center - diff --git a/datasets/aurora_msds.yaml b/datasets/aurora_msds.yaml index 3c1756d67..8fd798bb9 100644 --- a/datasets/aurora_msds.yaml +++ b/datasets/aurora_msds.yaml @@ -12,6 +12,10 @@ Documentation: | Contact: ams-dataset@aurora.tech ManagedBy: Aurora Operations, Inc. UpdateFrequency: This dataset is complete. +Collabs: + ASDI: + Tags: + - infrastructure Tags: - aws-pds - autonomous vehicles diff --git a/datasets/aws-public-blockchain.yaml b/datasets/aws-public-blockchain.yaml index 462949ba2..ec2019e79 100644 --- a/datasets/aws-public-blockchain.yaml +++ b/datasets/aws-public-blockchain.yaml @@ -1,33 +1,31 @@ Name: AWS Public Blockchain Data Description: > - The AWS Public Blockchain Data provides free access to blockchain datasets. Data is transformed into multiple - tables as compressed Parquet files, partitioned by date, to allow efficient access for most common analytics queries. +

The AWS Public Blockchain Data initiative provides free access to blockchain datasets through collaboration with data providers. The data is optimized for analytics by being transformed into compressed Parquet files, partitioned by date for efficient querying.

-

+

Datasets

+ Blockchain dataset - Maintained by - Path:
+ - Bitcoin - AWS - s3://aws-public-blockchain/v1.0/btc/
+ - Ethereum - AWS - s3://aws-public-blockchain/v1.0/eth/
+ - Arbitrum - SonarX - s3://aws-public-blockchain/v1.1/sonarx/arbitrum/
+ - Aptos - SonarX - s3://aws-public-blockchain/v1.1/sonarx/aptos/
+ - Base - SonarX - s3://aws-public-blockchain/v1.1/sonarx/base/
+ - Provenance - SonarX - s3://aws-public-blockchain/v1.1/sonarx/provenance/
+ - XRP Ledger - SonarX - s3://aws-public-blockchain/v1.1/sonarx/xrp/
+ - Stellar(XDR files) - Stellar - s3://aws-public-blockchain/v1.1/stellar/
+ - The Open Network (TON) - TON - s3://aws-public-blockchain/v1.1/ton/
+ - Cronos - Cronos - s3://aws-public-blockchain/v1.1/cronos/
+
- Datasets

- - - - - - - - - - - - - -
Blockchain datasetMaintained byPath
Bitcoin AWS s3://aws-public-blockchain/v1.0/btc/
Ethereum AWS s3://aws-public-blockchain/v1.0/eth/
Arbitrum SonarX s3://aws-public-blockchain/v1.1/sonarx/arbitrum/
Aptos SonarX s3://aws-public-blockchain/v1.1/sonarx/aptos/
Base SonarX s3://aws-public-blockchain/v1.1/sonarx/base/
Provenance SonarX s3://aws-public-blockchain/v1.1/sonarx/provenance/
XRP Ledger SonarX s3://aws-public-blockchain/v1.1/sonarx/xrp/
-
- For full datasets, with support and real-time updates, please visit SonarX. +

Become a Data Provider

+

We welcome additional blockchain data providers to join this initiative. If you're interested in contributing datasets to the AWS Public Blockchain Data program, please contact our team at aws-public-blockchain@amazon.com.

+ Documentation: https://github.com/aws-samples/digital-assets-examples/blob/main/analytics/ Contact: aws-blockchain-data@amazon.com ManagedBy: "[Amazon Web Services](https://aws.amazon.com/)" UpdateFrequency: New data is delivered daily to the current date folders Parquet files. Tags: + - aws-pds - blockchain - web3 License: https://github.com/aws-samples/digital-assets-examples/blob/main/LICENSE @@ -36,11 +34,23 @@ Resources: ARN: arn:aws:s3:::aws-public-blockchain Region: us-east-2 Type: S3 Bucket + Explore: + - '[Browse Bucket](https://aws-public-blockchain.s3.us-east-2.amazonaws.com/index.html)' + DataAtWork: Publications: + - Title: "Exploring Arbitrum Data: Analyze L2 Activity with AWS Public Blockchain Datasets" + URL: https://repost.aws/articles/ARpnBONglsT2e6D-hZZmxVvA/exploring-arbitrum-data-analyze-l2-activity-with-aws-public-blockchain-datasets + AuthorName: Simon Goldberg, Everton Fraga + - Title: "Unlocking XRP Ledger Data: Comprehensive Analysis with AWS Public Blockchain Datasets" + URL: https://repost.aws/articles/ARg_zMIXlhTG2hSDFZDfF6hQ/unlocking-xrp-ledger-data-comprehensive-analysis-with-aws-public-blockchain-datasets + AuthorName: Simon Goldberg, Everton Fraga - Title: New datasets added to the AWS Public Blockchain Datasets — available for analytics and research URL: https://repost.aws/articles/AR3gztQGeSS8CfaKNNeyYwsQ AuthorName: Everton Fraga, Simon Goldberg + - Title: FEDS Notes - Primary and Secondary Markets for Stablecoins + URL: https://www.federalreserve.gov/econres/notes/feds-notes/primary-and-secondary-markets-for-stablecoins-20240223.html + AuthorName: Cy Watsky, Jeffrey Allen, Hamzah Daud, Jochen Demuth, Daniel Little, Megan Rodden, Amber Seira - Title: Access Bitcoin and Ethereum open datasets for cross-chain analytics URL: https://aws.amazon.com/blogs/database/access-bitcoin-and-ethereum-open-datasets-for-cross-chain-analytics/ AuthorName: Oliver Steffmann, Bhaskar Ravat, Sreeji Gopal, and Stefan Dicker diff --git a/datasets/bhl-open-data.yaml b/datasets/bhl-open-data.yaml index 42a2a33de..9840e4d82 100644 --- a/datasets/bhl-open-data.yaml +++ b/datasets/bhl-open-data.yaml @@ -4,6 +4,10 @@ Documentation: Documentation can be found at +
+ Le modèle numérique d’élévation (MNE) canadien représente la couverture actuelle des données d’élévation disponibles. Ce jeu de données comprend un Modèle Numérique de Terrain (MNT), un Modèle Numérique de Surface (MNS) et d’autres produits dérivés. Ce jeu de données propose des MNE de résolution 1 m, 2 m et 30 m. Les produits 1 m et 2 m sont issus d’une combinaison de données MNE générées à partir de LiDAR aéroporté et d’images numériques optiques. Le MNE de 30 m intègre des données provenant du MNE Copernicus acquis lors de la mission TanDEM-X, ainsi que les données MNE issues du LiDAR aéroporté, ce qui permet d’assurer une couverture complète du Canada. +Documentation: "[Medium Resolution Digital Elevation Model - MRDEM](https://open.canada.ca/data/en/dataset/18752265-bda3-498c-a4ba-9dfe68cb98da) [High Resolution Digital Elevation Model Mosaic](https://open.canada.ca/data/en/dataset/0fe65119-e96e-4a57-8bfe-9d9245fba06b) " +Contact: geoinfo@nrcan-rncan.gc.ca +ManagedBy: "[Natural Resources Canada](https://nrcan.gc.ca/)" +UpdateFrequency: | + The dataset is updated as new DEM models becomes available. +
+ L'ensemble de données est mis à jour à mesure que des nouveaux modèles numérique d'élévation deviennent disponibles. +Tags: + - aws-pds + - canada + - elevation + - geospatial + - stac + - land + - dsm + - dtm + - dem +License: "[Open Government License (OGL)](https://open.canada.ca/en/open-government-licence-canada)" +Resources: + - Description: Mosaic of High Resolution Digital Elevation Model (HRDEM) at 1m / Mosaïque de Modèle numérique d'élévation de haute résolution (MNEHR) à 1m + ARN: arn:aws:s3:::canelevation-dem/hrdem-mosaic-1m/ + Region: ca-central-1 + Type: S3 Bucket + Explore: + - '[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=hrdem-mosaic-1m)' + - '[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/hrdem-mosaic-1m)' + - '[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)' + - Description: Mosaic of High Resolution Digital Elevation Model (HRDEM) at 2m / Mosaïque de Modèle numérique d'élévation de haute résolution (MNEHR) à 2m + ARN: arn:aws:s3:::canelevation-dem/hrdem-mosaic-2m/ + Region: ca-central-1 + Type: S3 Bucket + Explore: + - '[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=hrdem-mosaic-2m)' + - '[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/hrdem-mosaic-2m)' + - '[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)' + - Description: Medium Resolution Digital Elevation Model (MRDEM). Modèle numérique d'élévation de moyenne résolution (MNEMR) + ARN: arn:aws:s3:::canelevation-dem/mrdem-30/ + Region: ca-central-1 + Type: S3 Bucket + Explore: + - '[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=mrdem-30)' + - '[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/mrdem-30)' + - '[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)' + - Description: Mosaic of High Resolution Digital Elevation Model (HRDEM) by LiDAR acquisition project. Mosaïque de Modèle numérique d'élévation de haute résolution (MNEHR) par project d'acquisition LiDAR. + ARN: arn:aws:s3:::canelevation-dem/hrdem-lidar/ + Region: ca-central-1 + Type: S3 Bucket + Explore: + - '[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=hrdem-lidar)' + - '[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/hrdem-lidar)' + - '[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)' + - Description: High-Resolution Digital Elevation Model (HRDEM) generated from optical stereo imagery for Northern Canada / Modèle numérique d'élévation haute résolution (MNEHR) généré à partir de couple stéréo d'imagerie optique pour le nord du Canada + ARN: arn:aws:s3:::canelevation-dem/hrdem-arcticdem/ + Region: ca-central-1 + Type: S3 Bucket + Explore: + - '[STAC catalog](https://datacube.services.geo.ca/stac/api/search?collections=hrdem-arcticdem)' + - '[STAC Browser by Radiant Earth](https://radiantearth.github.io/stac-browser/#/external/datacube.services.geo.ca/stac/api/collections/hrdem-arcticdem)' + - '[Browse Bucket](https://canelevation-dem.s3.ca-central-1.amazonaws.com/index.html)' + - Description: Notifications for Canada Digital Elevation Models. + ARN: arn:aws:sns:ca-central-1:675987781521:canelevation-dem-create-object + Region: ca-central-1 + Type: SNS Topic + - Description: Notifications for mosaic of High Resolution Digital Elevation Model (HRDEM) at 1m. + ARN: arn:aws:sns:ca-central-1:675987781521:canelevation-dem-hrdem-mosaic-1m-create-object + Region: ca-central-1 + Type: SNS Topic + - Description: Notifications for mosaic of High Resolution Digital Elevation Model (HRDEM) at 2m. + ARN: arn:aws:sns:ca-central-1:675987781521:canelevation-dem-hrdem-mosaic-2m-create-object + Region: ca-central-1 + Type: SNS Topic + - Description: Notifications for Medium Resolution Digital Elevation Model (MRDEM). + ARN: arn:aws:sns:ca-central-1:675987781521:canelevation-dem-mrdem-30-create-object + Region: ca-central-1 + Type: SNS Topic + - Description: Notifications for High Resolution Digital Elevation Model (HRDEM) by LiDAR acquisition project. + ARN: arn:aws:sns:ca-central-1:675987781521:canelevation-dem-hrdem-lidar-create-object + Region: ca-central-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Cloud-Optimized Geospatial Data Access + URL: https://nrcan.github.io/cloud-optimized-geospatial/ + AuthorName: NRCan + Publications: + - Title: "Descriptor: Medium Resolution Digital Elevation Model From Natural Resources Canada’s CanElevation Series (MRDEM-30)" + URL: https://doi.org/10.1109/IEEEDATA.2025.3576318 + AuthorName: H. McGrath et al. diff --git a/datasets/canelevation-pointcloud.yaml b/datasets/canelevation-pointcloud.yaml index 7ccdd3acd..0eb3d890e 100644 --- a/datasets/canelevation-pointcloud.yaml +++ b/datasets/canelevation-pointcloud.yaml @@ -45,17 +45,18 @@ Resources: Type: S3 Bucket Explore: - '[LiDAR Data on Open Canada](https://open.canada.ca/data/en/dataset/7069387e-9986-4297-9f55-0288e9676947)' + - '[Browse Bucket](https://canelevation-lidar-point-clouds.s3.ca-central-1.amazonaws.com/pointclouds_nuagespoints/index.html#pointclouds_nuagespoints/)' DataAtWork: Tutorials: - Title: How to generate a digital elevation model (DEM) from a lidar point cloud in COPC LAZ format | Comment générer un modèle numérique d'élévation (MNE) à partir d'un nuage de point lidar en format COPC LAZ - URL: https://github.com/NRCan/CanElevation/blob/main/pointclouds_nuagespoints/DEM_from_COPC_lidar_EN.ipynb - NotebookURL: https://github.com/NRCan/CanElevation/blob/main/pointclouds_nuagespoints/DEM_from_COPC_lidar_EN.ipynb + URL: https://nrcan.github.io/CanElevation/pointclouds/dem-from-copc-lidar/ + NotebookURL: https://nrcan.github.io/CanElevation/pointclouds/DEM_from_COPC_lidar_EN.ipynb AuthorName: NRCan - Title: Identify projects and lidar tiles covering a region of interest | Déterminer les projets et les tuiles lidars couvrant une région d'intérêt - URL: https://github.com/NRCan/CanElevation/blob/main/pointclouds_nuagespoints/Get_Projects_Tiles_by_AOI_EN.ipynb - NotebookURL: https://github.com/NRCan/CanElevation/blob/main/pointclouds_nuagespoints/Get_Projects_Tiles_by_AOI_EN.ipynb + URL: https://nrcan.github.io/CanElevation/pointclouds/projects-tiles-by-aoi/ + NotebookURL: https://nrcan.github.io/CanElevation/pointclouds/Get_Projects_Tiles_by_AOI_EN.ipynb AuthorName: NRCan - Title: Using the LiDAR Point Clouds - CanElevation Series product in QGIS | Utilisation du produit Nuages de points lidar - Série CanÉlévation dans QGIS - URL: https://github.com/jsmoreau/CanElevation/blob/main/pointclouds_nuagespoints/QGIS_interactive_EN.md + URL: https://nrcan.github.io/CanElevation/pointclouds/qgis-interactive/ AuthorName: NRCan diff --git a/datasets/carbonpdf.yaml b/datasets/carbonpdf.yaml new file mode 100644 index 000000000..8e5d4b795 --- /dev/null +++ b/datasets/carbonpdf.yaml @@ -0,0 +1,28 @@ +Name: CarbonPDF +Description: A carbon question-answering (QA) dataset specifically designed to facilitate the extraction and analysis of data from real-world carbon reports of computing products. The dataset features annotated metadata, a variety of numerical reasoning tasks, and structured derivations to ensure accurate processing of fragmented and inconsistent information. +Documentation: https://github.com/pittcps/carbonpdf-dataset +Contact: kaz81@pitt.edu +ManagedBy: Pittcps lab +UpdateFrequency: Data for a new company is added once collected. +Collabs: + ASDI: + Tags: + - climate +Tags: + - aws-pds + - environmental + - product comparison + - csv + - information retrieval + - industry +License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) +Resources: + - Description: A component-level product carbon footprint dataset and a corresponding question-answering dataset based on it + ARN: arn:aws:s3:::carbonpdf + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Explore](https://github.com/pittcps/carbonpdf-dataset)' +ADXCategories: + - Environmental Data + - Manufacturing Data diff --git a/datasets/cartostore.yaml b/datasets/cartostore.yaml new file mode 100644 index 000000000..5bacbbd79 --- /dev/null +++ b/datasets/cartostore.yaml @@ -0,0 +1,34 @@ +Name: CartoStore +Description: | + Cross-Platform Repository for High-resolution Spatial Transcriptomics Datasets. +Documentation: "[CartoStore Overview](https://github.com/seqscope/cartostore)" +Contact: hmkang@umich.edu +ManagedBy: "[Hyun Min Kang](https://scholar.google.com/citations?user=8e0jy0IAAAAJ&hl=en)" +UpdateFrequency: Monthly +Tags: + - spatial transcriptomics + - spatial omics + - genomic + - bioinformatics + - life sciences +License: | + "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)" +Citation: | + CartoStore by Hyun Min Kang's lab at the University of Michigan School of Public Health. + Provided by Kang lab and accessed [DAY MONTH YEAR]. +Resources: + - Description: PMTile, YAML, JSON, and TSV files + ARN: arn:aws:s3:::cartostore + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: CartoStore Overview + URL: https://github.com/seqscope/cartostore/blob/main/README.md + AuthorName: Hyun Min Kang and Weiqiu Cheng + - Title: Cartloader Documentation + URL: https://seqscope.github.io/cartloader + AuthorName: Hyun Min Kang and Weiqiu Cheng + - Title : Example CartoStore Repository for Xenium Breast Cancer Dataset + URL: https://zenodo.org/records/15649152 + AuthorName: Hyun Min Kang and Weiqiu Cheng diff --git a/datasets/catalyst-cooperative-pudl.yaml b/datasets/catalyst-cooperative-pudl.yaml index 0ba6ed4d6..a53224b66 100644 --- a/datasets/catalyst-cooperative-pudl.yaml +++ b/datasets/catalyst-cooperative-pudl.yaml @@ -25,6 +25,10 @@ ManagedBy: "[Catalyst Cooperative](https://catalyst.coop/)" UpdateFrequency: | The federal agencies that publish the raw data PUDL processes release new data, monthly, quarterly and yearly. PUDL is continuously improving the data and tries to release new versions of the data quarterly. +Collabs: + ASDI: + Tags: + - energy Tags: - aws-pds - climate diff --git a/datasets/ccic.yaml b/datasets/ccic.yaml index 93ec8f402..2c5944c52 100644 --- a/datasets/ccic.yaml +++ b/datasets/ccic.yaml @@ -4,6 +4,10 @@ Documentation: https://ccic.readthedocs.io Contact: https://github.com/see-geo/ccic ManagedBy: "[Geoscience and Remote Sensing at Chalmers University of Technology](https://www.chalmers.se/en/departments/see/research/geo)" UpdateFrequency: Quarterly +Collabs: + ASDI: + Tags: + - climate Tags: - atmosphere - aws-pds diff --git a/datasets/ccrsmodisalbedo.yaml b/datasets/ccrsmodisalbedo.yaml new file mode 100644 index 000000000..c2dbdfd9b --- /dev/null +++ b/datasets/ccrsmodisalbedo.yaml @@ -0,0 +1,107 @@ +Name: CCRS MODIS albedo over Canada | Albédo CCRS MODIS au-dessus du Canada +Description: | + Times series of 10-day spectral and broadband albedo products derived at 250-m spatial resolution over Canadian territory and neighboring areas produced at the Canada Centre for Remote Sensing (CCRS) since February 2000 using MODIS L1B C6.1 swath imagery as input. The imagery for all spectral bands was downscaled and re-projected into the Lambert Conformal Conic (LCC) projection at 250-m spatial resolution. The area size is 5,700 km x 4,800 km (22,800 pixel x 19,200 lines). + Séries temporelles de produits d’albédo spectral et à large bande générés à des intervalles de 10 jours avec une résolution spatiale de 250 m, couvrant le territoire canadien et les régions voisines. Ces produits sont élaborés par le Centre canadien de télédétection (CCT) depuis février 2000 à partir des images MODIS L1B C6.1. Les images de toutes les bandes spectrales ont été rééchantillonnées et reprojetées en projection conforme de Lambert (LCC) à une résolution spatiale de 250 m. La zone couverte est d’environ 5 700 km par 4 800 km (22 800 pixels par 19 200 lignes). +Documentation: https://data.eodms-sgdot.nrcan-rncan.gc.ca/public/CCRS/Trishchenko_MODIS_Albedo +Contact: alexander.trichtchenko@nrcan-rncan.gc.ca +ManagedBy: Canada Centre for Remote Sensing (CCRS), Canada Centre for Mapping and Earth Observation (CCMEO), Department of Natural Resources Canada (NRCan) https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-mapping-earth-observation +UpdateFrequency: | + Semi-annually, until the end of MODIS operations + Deux fois par an, jusqu'à la fin des opérations MODIS +Tags: + - aws-pds + - analysis ready data + - broadband + - cog + - earth observation + - satellite imagery +License: Creative Commons Licence. Creative Commons BY 4.0 https://creativecommons.org/licenses/by/4.0/ +Citation: Trishchenko, Alexander P. 2025. CCRS MODIS albedo over Canada at 250-m resolution and 10-day intervals. +Resources: + - Description: CCRS MODIS Albedo, Cloud Optimized GeoTIFF (COG) images + ARN: arn:aws:s3:::ccrs-modis-albedo + Region: ca-central-1 + Type: S3 Bucket + - Description: Notifications for new CCRS MODIS Albedo data + ARN: arn:aws:sns:ca-central-1:675987781521:ccrs-modis-albedo-object_created + Region: ca-central-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Get To Know A Dataset - CCRS MODIS Albedo at 250-m resolution and 10-day intervals + URL: https://github.com/OpsCCRS/AWS-Open-Data-Registry-Preparation/blob/main/CCRSMODISAlbedo/get-to-know-a-dataset-CCRSMODISAlbedo.ipynb + AuthorName: Alexander Trichtchenko + AuthorURL: https://profils-profiles.science.gc.ca/en/profile/alexander-p-trishchenko + NotebookURL: https://github.com/OpsCCRS/AWS-Open-Data-Registry-Preparation/blob/main/CCRSMODISAlbedo/get-to-know-a-dataset-CCRSMODISAlbedo.ipynb + Publications: + - Title: "Boreal lichen woodlands: a possible negative feedback to climate change in eastern North America" + URL: https://doi.org/10.1016/j.agrformet.2010.12.013 + AuthorName: Bernier, P.Y., Desjardins, R.L., Karimi-Zindashty, Y., Worth, D., Beaudoin, A., Luo, Y., Wang, S. + - Title: Detection of North American land cover change between 2005 and 2010 with 250m MODIS data + URL: https://www.researchgate.net/publication/286156544_Detection_of_North_American_land_cover_change_between_2005_and_2010_with_250m_MODIS_Data + AuthorName: Colditz, R.R., Pouliot, D., Llamas, R.M., Homer, C., Latifovic, R., Ressl, R.A., Tovar, C.M., Hern�ndez, A.V., Richardson, K. + - Title: "Annual mapping of large Forest disturbances across Canada's forests using 250 m MODIS imagery from 2000 to 2011" + URL: https://doi.org/10.1139/cjfr-2014-0229 + AuthorName: Guindon, L., Bernier, P.Y., Beaudoin, A., Pouliot, D., Villemaire, P., Hall, R.J., Latifovic, R., St-Amant, R. + - Title: Perennial snow and ice variations (2000-2008) in the Arctic circumpolar land area from satellite observations + URL: https://doi.org/10.1029/2010JF001664 + AuthorName: Fontana F.M.A., Trishchenko A.P., Luo Y., Khlopenkov K.V., Nussbaumer S.U., Wunderle S. + - Title: Influence of two management practices in the Canadian Prairies on radiative forcing + URL: https://doi.org/10.1016/j.scitotenv.2020.142701 + AuthorName: Liu, J., Worth, D.E., Desjardins, R.L., Haak, D., McConkey, B., Cerkowniak, D. + - Title: Implementation and Evaluation of Concurrent Gradient Search Method for Reprojection of MODIS Level 1B Imagery + URL: https://doi.org/10.1109/TGRS.2008.916633 + AuthorName: Khlopenkov, K.V., and Trishchenko, A.P. + - Title: "Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America" + URL: https://doi.org/10.1016/j.rse.2008.06.010 + AuthorName: Luo, Y., Trishchenko, A.P., Khlopenkov, K.V. + - Title: Surface bidirectional reflectance and albedo properties derived by a land cover based approach from the MODIS observations. + URL: https://doi.org/10.1029/2004JD004741 + AuthorName: Luo, Y., Trishchenko, Alexander P., Latifovic, R., Li, Z. + - Title: An approach for developing surface albedo product from seven MODIS land bands at 250m spatial resolution over Canada and the Arctic circumpolar region + URL: https://lpvs.gsfc.nasa.gov/LPV_meetings/Beijing09/Luo_MODIS_Albedo_Product.pdf + AuthorName: Luo, Y., Trishchenko, A.P., Khlopenkov, K.V. + - Title: "A raster version of the circumpolar Arctic vegetation map (CAVM)" + URL: https://doi.org/10.1016/J.RSE.2019.111297 + AuthorName: Raynolds, M.K., Walker, D.A., Balser, A., Bay, C., Campbell, M., Cherosov, M.M., Dani�ls, F.J.A., Eidesen, P.B., Ermokhina, K.A., Frost, G.V., Jedrzejek, B., Jorgenson, M.T., Kennedy, B.E., Kholod, S.S., Lavrinenko, I.A., Lavrinenko, O.V., Magn�sson, B., Matveyeva, N.V., Met�salemsson, S., Nilsen, L., Olthof, I., Pospelov, I.N., Pospelova, E.B., Pouliot, D., Razzhivin, V., Schaepman-Strub, G., ?Sib�k, J., Telyatnikov, M.Y., Troeva, E. + - Title: Cumulative changes in minimum snow/ice extent over Canada and Northern USA for 2000-2023 + URL: https://doi.org/10.1080/07038992.2024.2371359 + AuthorName: Trishchenko, A.P., Ungureanu, C. + - Title: "Annual minimum snow/ice extent variations over Greenland since 2000:ice sheet, peripheral areas, and relation to ice mass balance" + URL: https://doi.org/10.1175/BAMS-D-22-0244.1 + AuthorName: Trishchenko, A.P., Ungureanu, C. + - Title: Landfast ice properties over the Beaufort Sea region in 2000-2019 from MODIS and Canadian Ice Service data + URL: https://doi.org/10.1139/cjes-2021-0011 + AuthorName: Trishchenko, A.P., Kostylev, V.E., Luo, Y., Ungureanu, C., Whalen, D., Li, J. + - Title: Landfast ice mapping using MODIS clear-sky composites:application for the Banks Island coastline in Beaufort Sea and comparison with Canadian Ice Service data + URL: https://doi.org/10.1080/07038992.2021.1909466 + AuthorName: Trishchenko, A.P., Luo, Y. + - Title: "Minimum snow/ice extent over the Northern circumpolar landmass in 2000-19:how much snow survives the summer melt?" + URL: https://doi.org/10.1175/BAMS-D-20-0177.1 + AuthorName: Trishchenko, A.P., Ungureanu, C. + - Title: Variations of annual minimum snow and ice extent over Canada and neighbouring landmass derived from MODIS 250-m imagery for 2000-2014 + URL: https://doi.org/10.1080/07038992.2016.1166043 + AuthorName: Trishchenko, A.P., Leblanc, S.G., Wang, S., Li, J., Ungureanu, C., Luo, Y., Khlopenkov, K.V., Fontana, F., 2016 + - Title: "A method for downscaling MODIS land channels to 250-m spatial resolution using adaptive regression and normalization" + URL: https://doi.org/10.1117/12.689157 + AuthorName: Trishchenko, A.P., Luo, Y., Khlopenkov, K.V. + - Title: "Arctic circumpolar mosaic at 250m spatial resolution for IPY by fusion of MODIS/TERRA land bands B1-B7" + URL: https://doi.org/10.1080/01431160802348119 + AuthorName: Trishchenko, A.P., Luo, Y., Khlopenkov, K.V., Park, W.M., Wang, S. + - Title: Clear-Sky Composites over Canada from Visible Infrared Imaging Radiometer Suite:Continuing MODIS Time Series into the Future + URL: https://doi.org/10.1080/07038992.2019.1601006 + AuthorName: Trishchenko, A.P. + - Title: "MODIS Surface Albedo and Surface Reflectance Dataset. Format Description." + URL: https://data.eodms-sgdot.nrcan-rncan.gc.ca/public/CCRS/Trishchenko_MODIS_Albedo/ + AuthorName: Trishchenko, Alexander P., Ungureanu, Calin + - Title: "Warm season snow/ice probability maps from modis and viirs sensors over Canada" + URL: https://doi.org/10.1109/IGARSS.2018.8519558 + AuthorName: Trishchenko, Alexander P., Ungureanu, Calin + - Title: Probability of the annual minimum snow and ice (MSI) presence over Canada + URL: https://open.canada.ca/data/en/dataset/808b84a1-6356-4103-a8e9-db46d5c20fcf + AuthorName: Trishchenko, Alexander P. + + + + + diff --git a/datasets/cesm-hr.yaml b/datasets/cesm-hr.yaml index 32f430fd2..e5e257b07 100644 --- a/datasets/cesm-hr.yaml +++ b/datasets/cesm-hr.yaml @@ -23,6 +23,10 @@ UpdateFrequency: >- any issues with the copy of the data on the AWS is reported in the future. Other experiments will be shared in the future according to the availability of resources. +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - climate diff --git a/datasets/chammi.yaml b/datasets/chammi.yaml new file mode 100644 index 000000000..e2f91a729 --- /dev/null +++ b/datasets/chammi.yaml @@ -0,0 +1,53 @@ +Name: CHAMMI-75 +Description: | + Quantifying cell morphology using images and machine learning models has proven to be a powerful tool to study the response of cells to treatments. + However, the models used to quantify cellular morphology are typically trained with a single microscopy imaging type and under controlled experimental conditions. + This results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels), + or because the target experimental conditions are out of distribution. We have created CHAMMI-75, a large-scale dataset containing 2.8 million multi-channel, + high-resolution images curated from 75 diverse, publicly available biological studies. This dataset is useful to investigate and develop channel-adaptive models, + which could process microscopy images of varying technical specifications and regardless of the number of channels. By breaking the limitations of existing models, + CHAMMI-75 is an invaluable resource for creating the next generation of foundation models for image-based biological research. +Documentation: https://github.com/CaicedoLab/CHAMMI-75 +Contact: Juan Caicedo, juan.caicedo@wisc.edu +ManagedBy: Morgridge Institute for Research +UpdateFrequency: Every 2 years +Tags: + - microscopy + - machine learning + - biology + - life sciences + - imaging + - high-throughput imaging + - cell imaging + - fluorescence imaging + - aws-pds +License: CC BY 4.0 License +Citation: +Resources: + - Description: "CHAMMI-75 Dataset: Images, training set and evaluation set available in an S3 bucket" + ARN: arn:aws:s3:::chammi-data + Region: us-west-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: "Get To Know A Dataset: CHAMMI-75" + URL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/ + NotebookURL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/get-to-know-a-dataset-template.ipynb + AuthorName: Vidit Agrawal, Juan Caicedo + - Title: Running CHAMMI-75 Evaluation Benchmarks + URL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/ + NotebookURL: https://github.com/CaicedoLab/CHAMMI-75/blob/main/aws-tutorials/running-benchmarks.ipynb + AuthorName: Vidit Agrawal, Juan Caicedo + Tools & Applications: + - Title: CHAMMI-75 Source Code + URL: https://github.com/CaicedoLab/CHAMMI-75 + AuthorName: Vidit Agrawal + - Title: CHAMMI Benchmarking Source Code + URL: https://github.com/chaudatascience/channel_adaptive_models + AuthorName: Chau Pham + Publications: + - Title: "CHAMMI: A benchmark for channel-adaptive models in microscopy imaging" + URL: https://neurips.cc/virtual/2023/poster/73620 + AuthorName: Zitong Sam Chen, Chau Pham, Siqi Wang, Michael Doron, Nikita Moshkov, Bryan Plummer, Juan C. Caicedo +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/citrus-farm.yaml b/datasets/citrus-farm.yaml index e0207b465..f380a2f7c 100644 --- a/datasets/citrus-farm.yaml +++ b/datasets/citrus-farm.yaml @@ -9,6 +9,10 @@ Documentation: https://ucr-robotics.github.io/Citrus-Farm-Dataset/ Contact: Hanzhe Teng (hteng007@ucr.edu), Konstantinos Karydis (kkarydis@ece.ucr.edu) ManagedBy: "[Autonomous Robots and Control Systems Lab](https://sites.google.com/view/arcs-lab)" UpdateFrequency: NA +Collabs: + ASDI: + Tags: + - agriculture Tags: - aws-pds - robotics diff --git a/datasets/clay-model-v0-embeddings.yaml b/datasets/clay-model-v0-embeddings.yaml new file mode 100644 index 000000000..8bff0851f --- /dev/null +++ b/datasets/clay-model-v0-embeddings.yaml @@ -0,0 +1,24 @@ +Name: Clay Model v0 Embeddings +Description: Machine learning model embeddings dataset providing pre-computed feature representations for satellite and aerial imagery analysis. +Documentation: https://source.coop/repositories/clay/clay-model-v0-embeddings/description +Contact: contact@madewithclay.org +ManagedBy: "[Source Cooperative](https://source.coop/)" +UpdateFrequency: As new model versions become available +Tags: + - machine learning + - computer vision + - satellite imagery + - aerial imagery + - earth observation + - imaging +License: Creative Commons Attribution 4.0 International License +Citation: "Clay Model v0 Embeddings. Source Cooperative. https://source.coop/repositories/clay/clay-model-v0-embeddings/description" +Resources: + - Description: Clay Model v0 Embeddings S3 Bucket + ARN: arn:aws:s3:::us-west-2.opendata.source.coop/clay/clay-model-v0-embeddings + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://source.coop/clay/clay-model-v0-embeddings/)' +ADXCategories: + - Environmental Data \ No newline at end of file diff --git a/datasets/clay-v1-5-naip-2.yaml b/datasets/clay-v1-5-naip-2.yaml new file mode 100644 index 000000000..03bd8ae35 --- /dev/null +++ b/datasets/clay-v1-5-naip-2.yaml @@ -0,0 +1,23 @@ +Name: Clay v1.5 NAIP-2 +Description: National Agriculture Imagery Program (NAIP) dataset providing high-resolution aerial imagery for agricultural monitoring, land use analysis, and natural resource management. +Documentation: https://source.coop/repositories/clay/clay-v1-5-naip-2/description +Contact: contact@madewithclay.org +ManagedBy: "[Source Cooperative](https://source.coop/)" +UpdateFrequency: As new NAIP data becomes available +Tags: + - aerial imagery + - agriculture + - land use + - natural resource + - environmental +License: Creative Commons Attribution 4.0 International License +Citation: "Clay v1.5 NAIP-2. Source Cooperative. https://source.coop/repositories/clay/clay-v1-5-naip-2/description" +Resources: + - Description: Clay v1.5 NAIP-2 S3 Bucket + ARN: arn:aws:s3:::us-west-2.opendata.source.coop/clay/clay-v1-5-naip-2 + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://source.coop/clay/clay-v1-5-naip-2/)' +ADXCategories: + - Environmental Data \ No newline at end of file diff --git a/datasets/clay-v1-5-sentinel2.yaml b/datasets/clay-v1-5-sentinel2.yaml new file mode 100644 index 000000000..9efab89c4 --- /dev/null +++ b/datasets/clay-v1-5-sentinel2.yaml @@ -0,0 +1,25 @@ +Name: Clay v1.5 Sentinel-2 +Description: Sentinel-2 satellite imagery dataset providing high-resolution optical data for land monitoring, agriculture, and environmental applications. +Documentation: https://source.coop/repositories/clay/clay-v1-5-sentinel2/description +Contact: contact@madewithclay.org +ManagedBy: "[Source Cooperative](https://source.coop/)" +UpdateFrequency: As new Sentinel-2 data becomes available +Tags: + - satellite imagery + - earth observation + - agriculture + - land use + - environmental +License: Creative Commons Attribution 4.0 International License +Citation: "Clay v1.5 Sentinel-2. Source Cooperative. https://source.coop/repositories/clay/clay-v1-5-sentinel2/description" +Resources: + - Description: Clay v1.5 Sentinel-2 S3 Bucket + ARN: arn:aws:s3:::us-west-2.opendata.source.coop/clay/clay-v1-5-sentinel2 + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://source.coop/repositories/clay/clay-v1-5-sentinel2/description)' +ADXCategories: + - Environmental Data + + diff --git a/datasets/clinical-ultrasound-image-data.yaml b/datasets/clinical-ultrasound-image-data.yaml new file mode 100644 index 000000000..4c92184b4 --- /dev/null +++ b/datasets/clinical-ultrasound-image-data.yaml @@ -0,0 +1,20 @@ +Name: Clinical Ultrasound Image Repository +Description: Generic Clinical Ultrasound Data from Random Subjects acquired for Clinical Reasons, to be used for Developing Artificial Intelligence Applications. This dataset is complete with 2000 studies from 2000 subjects (one third each from abdominal, cardiac, and OB/GYN cases) +Documentation: https://clinical-ultrasound-image-repository.s3.amazonaws.com/index.html +Contact: shuver@nvidia.com +ManagedBy: "[MONAI Development Team](https://github.com/Project-MONAI/MONAI)" +UpdateFrequency: This is a static dataset; however, tutorials and resources will be updated as they are developed. +Tags: + - medicine + - medical imaging + - machine learning + - life sciences + - aws-pds +License: "[CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)" +Resources: + - Description: Clinical Ultrasound Image Repository + ARN: arn:aws:s3:::clinical-ultrasound-image-repository + Region: us-west-2 + Type: S3 Bucket + Explore: + - "[Browse Bucket](https://clinical-ultrasound-image-repository.s3.amazonaws.com/download.html)" diff --git a/datasets/cmas-data-warehouse.yaml b/datasets/cmas-data-warehouse.yaml index 2cc8125a6..fe3372d6a 100644 --- a/datasets/cmas-data-warehouse.yaml +++ b/datasets/cmas-data-warehouse.yaml @@ -60,18 +60,44 @@ Resources: Type: S3 Bucket Explore: - '[Browse Bucket](https://cmas-equates.s3.amazonaws.com/index.html)' + - Description: Community Multiscale Air Quality (CMAQ) 2019 3D Gridded and Column data from the EPA's Air Quality Time Series (EQUATES) Project + ARN: arn:aws:s3:::epa-equates-v1 + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://epa-equates-v1.s3.amazonaws.com/index.html)' + - '[EPA CMAQ 2019 3D Gridded and Column data from EQUATES Project](https://aws.amazon.com/marketplace/pp/prodview-kziefcewnxcxe?sr=0-5&ref_=beagle&applicationId=AWSMPContessa)' + - Description: CMAQ 2023 12US4 CRACMM3 Modeling Platform + ARN: arn:aws:s3::::::cmaq-12us4-cracmm3-modeling-platform-2023 + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://cmaq-12us4-cracmm3-modeling-platform-2023.s3.amazonaws.com/index.html)' + - Description: CMAQ Model Versions 5.5 CRACMM2 Input Data (2022r1) -- 12/22/2021 - 12/31/2022 12km CONUS + ARN: arn:aws:s3::::::cmaq-12us1-cracmm2-modeling-platform-2022 + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://cmaq-12us1-cracmm2-modeling-platform-2022.s3.amazonaws.com/index.html)' + - Description: EPA 2022 Modeling Platform + ARN: arn:aws:s3:::epa-2022-modeling-platform + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://epa-2022-modeling-platform.s3.amazonaws.com/index.html)' + - '[OAQPS 2022 Modeling Platform](https://registry.opendata.aws/epa-2022-modeling-platform/)' - Description: CMAQ 2021 Modeling Platform ARN: arn:aws:s3:::2021platform Region: us-east-1 Type: S3 Bucket Explore: - - '[Browse Bucket](https://2021platform.s3.amazonaws.com/readme.html)' + - '[Browse Bucket](https://2021platform.s3.amazonaws.com/index.html)' - Description: CMAQ 2019 Modeling Platform ARN: arn:aws:s3:::cmaq-2019-modeling-platform Region: us-east-1 Type: S3 Bucket Explore: - - '[Browse Bucket](https://cmaq-2019-modeling-platform.s3.amazonaws.com/readme.html)' + - '[Browse Bucket](https://cmaq-2019-modeling-platform.s3.amazonaws.com/index.html)' - Description: CMAQ 2018 Modeling Platform ARN: arn:aws:s3:::cmas-cmaq-modeling-platform-2018 Region: us-east-1 diff --git a/datasets/cmip6-era5-hybrid-southeast-asia.yaml b/datasets/cmip6-era5-hybrid-southeast-asia.yaml index 693df9a70..56a47ddc6 100644 --- a/datasets/cmip6-era5-hybrid-southeast-asia.yaml +++ b/datasets/cmip6-era5-hybrid-southeast-asia.yaml @@ -1,27 +1,31 @@ -Name: Hybrid statistical-dynamic downscaling based on multi-model ensembles in Southeast Asia -Description: | - GCMs under CMIP6 have been widely used to investigate climate change impacts and put forward associated adaptation and mitigation strategies. However, the relatively coarse spatial resolutions (usually 100~300km) preclude their direct applications at regional scales, which are exactly where the analysis (e.g., hydrological model simulation) is performed. To bridge this gap, a typical approach is to ‘refine’ the information from GCMs through regional climate downscaling experiments, which can be conducted statistically, dynamically, or a combination thereof. Statistical downscaling establishes relationships between large-scale climate indicators and small-scale climate variables in the reference (historical) period. Subsequently, these relationships are kept unchanged in the future and used to predict the future variables. On the other hand, dynamical downscaling operates based on the physical processes and the associated interactions in the climate systems and thus can produce a full set of regional climate simulations (e.g., temperature and precipitation fields) that are dynamically consistent. However, traditional dynamical downscaling contains significant biases that are transferred from GCMs and may be enhanced during the process of downscaling, thus degrading the downscaled results. One promising approach to remove these biases is the hybrid statistical-dynamical downscaling method, where GCMs are firstly bias-corrected, and subsequently used as lower and lateral boundary conditions to drive the regional climate models (RCMs). - - In this work, we apply a hybrid statistical-dynamical downscaling method, following the approach of Xu et al. 2021. The bias-corrected dataset is adjusted to resemble ERA5-based mean climate and interannual variance, and with a non-linear trend from the ensemble mean of the 14 CMIP6 models. The dataset spans a historical period of 1979–2014 and future scenarios (SSP585) of 2015–2100, with a temporal scale of six-hour. - - The main contributions of this dataset are twofold. First, we provide the open-source and high-resolution (12.5km: Southeast Asia; 2.5km:Southern Malay Peninsula; 500m: Singapore, as shown in the following Figures) datasets, including precipitation, wind, temperature, radiation, etc. Second, through our experiment, this bias-corrected and downscaled dataset is of exceptional quality compared to that of the existing dynamical scaling work (e.g., CORDEX) in southeast Asia in terms of its ability to reproduce regional climate extremes, spatial patterns, etc. This dataset will be useful for policy-makers and researchers to establish the necessary pathways for resilient planning in order to mitigate the dire impacts of climate change. -Documentation: https://sgcale.github.io/resource/data/ -Contact: For any questions regarding dataset, email Professor Xiaogang He at hexg@nus.edu.sg. -ManagedBy: "[PREP-NexT Lab](https://github.com/PREP-NexT)" -UpdateFrequency: Update when needed. -Tags: - - climate - - netcdf - - precipitation - - aws-pds -License: - "All the code in this repository is [MIT](https://choosealicense.com/licenses/mit/) licensed, but we request that you please provide attribution if reusing any of our digital content (graphics, logo, copy, etc.)." -Resources: - - Description: | - We are releasing a bias-corrected and downscaled dataset based on 14 Coupled Model Intercomparison Project 6 (CMIP6) global climate models (GCMs) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. More details please refer to [this link](https://sgcale.github.io/research/climate-downscaling/). - ARN: arn:aws:s3:::arn:aws:s3:::cmip6-wrf-southeastasia - Region: us-west-2 - Type: S3 Bucket - RequesterPays: false - Explore: - - "[Browse Bucket](https://cmip6-wrf-southeastasia.s3.us-west-2.amazonaws.com/index.html)" +Name: Hybrid statistical-dynamic downscaling based on multi-model ensembles in Southeast Asia +Description: | + GCMs under CMIP6 have been widely used to investigate climate change impacts and put forward associated adaptation and mitigation strategies. However, the relatively coarse spatial resolutions (usually 100~300km) preclude their direct applications at regional scales, which are exactly where the analysis (e.g., hydrological model simulation) is performed. To bridge this gap, a typical approach is to ‘refine’ the information from GCMs through regional climate downscaling experiments, which can be conducted statistically, dynamically, or a combination thereof. Statistical downscaling establishes relationships between large-scale climate indicators and small-scale climate variables in the reference (historical) period. Subsequently, these relationships are kept unchanged in the future and used to predict the future variables. On the other hand, dynamical downscaling operates based on the physical processes and the associated interactions in the climate systems and thus can produce a full set of regional climate simulations (e.g., temperature and precipitation fields) that are dynamically consistent. However, traditional dynamical downscaling contains significant biases that are transferred from GCMs and may be enhanced during the process of downscaling, thus degrading the downscaled results. One promising approach to remove these biases is the hybrid statistical-dynamical downscaling method, where GCMs are firstly bias-corrected, and subsequently used as lower and lateral boundary conditions to drive the regional climate models (RCMs). + + In this work, we apply a hybrid statistical-dynamical downscaling method, following the approach of Xu et al. 2021. The bias-corrected dataset is adjusted to resemble ERA5-based mean climate and interannual variance, and with a non-linear trend from the ensemble mean of the 14 CMIP6 models. The dataset spans a historical period of 1979–2014 and future scenarios (SSP585) of 2015–2100, with a temporal scale of six-hour. + + The main contributions of this dataset are twofold. First, we provide the open-source and high-resolution (12.5km: Southeast Asia; 2.5km:Southern Malay Peninsula; 500m: Singapore, as shown in the following Figures) datasets, including precipitation, wind, temperature, radiation, etc. Second, through our experiment, this bias-corrected and downscaled dataset is of exceptional quality compared to that of the existing dynamical scaling work (e.g., CORDEX) in southeast Asia in terms of its ability to reproduce regional climate extremes, spatial patterns, etc. This dataset will be useful for policy-makers and researchers to establish the necessary pathways for resilient planning in order to mitigate the dire impacts of climate change. +Documentation: https://sgcale.github.io/resource/data/ +Contact: For any questions regarding dataset, email Professor Xiaogang He at hexg@nus.edu.sg. +ManagedBy: "[PREP-NexT Lab](https://github.com/PREP-NexT)" +UpdateFrequency: Update when needed. +Collabs: + ASDI: + Tags: + - climate +Tags: + - climate + - netcdf + - precipitation + - aws-pds +License: + "All the code in this repository is [MIT](https://choosealicense.com/licenses/mit/) licensed, but we request that you please provide attribution if reusing any of our digital content (graphics, logo, copy, etc.)." +Resources: + - Description: | + We are releasing a bias-corrected and downscaled dataset based on 14 Coupled Model Intercomparison Project 6 (CMIP6) global climate models (GCMs) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. More details please refer to [this link](https://sgcale.github.io/research/climate-downscaling/). + ARN: arn:aws:s3:::arn:aws:s3:::cmip6-wrf-southeastasia + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + Explore: + - "[Browse Bucket](https://cmip6-wrf-southeastasia.s3.us-west-2.amazonaws.com/index.html)" diff --git a/datasets/coawst.yaml b/datasets/coawst.yaml index de87d9a09..2820336d0 100644 --- a/datasets/coawst.yaml +++ b/datasets/coawst.yaml @@ -5,6 +5,10 @@ Contact: jbzambon@fathomscience.com ManagedBy: Fathom Science UpdateFrequency: None Citation: Warner, J.C., and Kalra, T.S., 2022, Collection of COAWST model forecast for the US East Coast and Gulf of Mexico, U.S. Geological Survey data release, https://doi.org/10.5066/P903KPBJ +Collabs: + ASDI: + Tags: + - oceans Tags: - aws-pds - oceans diff --git a/datasets/colorado-elevation-data.yaml b/datasets/colorado-elevation-data.yaml new file mode 100644 index 000000000..8cba77ce4 --- /dev/null +++ b/datasets/colorado-elevation-data.yaml @@ -0,0 +1,33 @@ +Name: State of Colorado Elevation Data +Description: The State of Colorado has gathered public historical elevation data. +Documentation: https://docs.google.com/document/d/1HMO-d4cCrBvFa2F6-N3lhP6rkezlvBmSUFA5S8t_ekQ/edit?usp=sharing +Contact: oit_gis@state.co.us +ManagedBy: State of Colorado Governors Office of Information Technology OIT GIS team +UpdateFrequency: Periodically +Tags: + - aws-pds + - geospatial + - imaging + - mapping +License: https://creativecommons.org/publicdomain/zero/1.0/legalcode +Resources: + - Description: Colorado Elevation Data (LiDAR) + ARN: arn:aws:s3:::colorado-public-elevation-data + Region: us-west-2 + Type: S3 Bucket + - Description: Notifications for new Colorado Elevation data + ARN: arn:aws:sns:us-west-2:180294215083:colorado-public-elevation-data-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Colorado AWS Open Data Elevation Data Guide + URL: https://docs.google.com/document/d/1pAHZB6SgSE4QTawEbSnIIHpxVCTBg-IjQ6X9KJP28BM/edit?usp=sharing + AuthorName: State of Colorado OIT-GIS + AuthorURL: https://geodata.colorado.gov/ + - Title: Colorado Public Elevation Data s3 Browser + URL: https://colorado-public-elevation-data.s3.amazonaws.com/index.html + AuthorName: State of Colorado OIT-GIS + AuthorURL: https://geodata.colorado.gov/ +ADXCategories: + - Public Sector Data diff --git a/datasets/colorado-imagery.yaml b/datasets/colorado-imagery.yaml index 1d3e29bba..be4d24005 100644 --- a/datasets/colorado-imagery.yaml +++ b/datasets/colorado-imagery.yaml @@ -1,35 +1,43 @@ -Name: State of Colorado Imagery -Description: The State of Colorado has gathered public historical imagery ranging from 2005 to 2021. -Documentation: https://docs.google.com/document/d/1YDHignUj9lQTMw2J-SqA96MTP8KmJYtk2ZKKC2ZYuPE/edit?usp=sharing -Contact: oit_gis@state.co.us -ManagedBy: State of Colorado Governor's Office of Information Technology (OIT) GIS team -UpdateFrequency: Periodically -Tags: - - aws-pds - - aerial imagery - - geospatial - - imaging - - mapping -License: https://creativecommons.org/publicdomain/zero/1.0/legalcode -Resources: - - Description: The State of Colorado historic public aerial imagery. Currently, NAIP is available from 2005 and 2009-2021. The National Agriculture Imagery Program is a project managed by the U.S. Department of Agriculture created to collect leaf-on imagery for the United States during peak growing seasons. The files are available as GeoTIFFs. From 2005-2017 they have a one meter resolution. After that, it is a 60cm resolution. DRAPP (Denver Regional Aerial Photgraphy Project) is available from 2010-2020. It is availble in 3, 6, and 12in resolutions (except 2012). - ARN: arn:aws:s3:::colorado-public-imagery - Region: us-west-2 - Type: S3 Bucket -DataAtWork: - Tutorials: - - Title: Colorado AWS Open Imagery Guide - URL: https://docs.google.com/document/d/15GjCSWSzst82FZMqBqdGV0rt6FKJzt03NlQYdWwsLGE/edit?usp=sharing - AuthorName: State of Colorado OIT-GIS - AuthorURL: https://geodata.colorado.gov/ - Tools & Applications: - - Title: Colorado Public Imagery Dowloader - URL: https://gis.colorado.gov/imagery/ - AuthorName: State of Colorado OIT-GIS - AuthorURL: https://geodata.colorado.gov/ - - Title: Colorado Public Imagery s3 Browser - URL: https://colorado-public-imagery.s3.amazonaws.com/index.html - AuthorName: State of Colorado OIT-GIS - AuthorURL: https://geodata.colorado.gov/ -ADXCategories: - - Public Sector Data +Name: State of Colorado Imagery +Description: The State of Colorado has gathered public historical imagery ranging from 2005 to 2021. +Documentation: https://docs.google.com/document/d/1YDHignUj9lQTMw2J-SqA96MTP8KmJYtk2ZKKC2ZYuPE/edit?usp=sharing +Contact: oit_gis@state.co.us +ManagedBy: State of Colorado Governors Office of Information Technology OIT GIS team +UpdateFrequency: Periodically +Collabs: + ASDI: + Tags: + - satellite imagery +Tags: + - aws-pds + - aerial imagery + - geospatial + - imaging + - mapping +License: https://creativecommons.org/publicdomain/zero/1.0/legalcode +Resources: + - Description: The State of Colorado historic public aerial imagery. Currently, NAIP is available from 2005 and 2009-2021. The National Agriculture Imagery Program is a project managed by the U.S. Department of Agriculture created to collect leaf-on imagery for the United States during peak growing seasons. The files are available as GeoTIFFs. From 2005-2017 they have a one meter resolution. After that, it is a 60cm resolution. DRAPP (Denver Regional Aerial Photgraphy Project) is available from 2010-2020. It is availble in 3, 6, and 12in resolutions (except 2012). + ARN: arn:aws:s3:::colorado-public-imagery + Region: us-west-2 + Type: S3 Bucket + - Description: Notifications for real-time data updates + ARN: arn:aws:sns:us-west-2:180294215083:colorado-public-imagery-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Colorado AWS Open Imagery Guide + URL: https://docs.google.com/document/d/15GjCSWSzst82FZMqBqdGV0rt6FKJzt03NlQYdWwsLGE/edit?usp=sharing + AuthorName: State of Colorado OIT-GIS + AuthorURL: https://geodata.colorado.gov/ + Tools & Applications: + - Title: Colorado Public Imagery Dowloader + URL: https://gis.colorado.gov/imagery/ + AuthorName: State of Colorado OIT-GIS + AuthorURL: https://geodata.colorado.gov/ + - Title: Colorado Public Imagery s3 Browser + URL: https://colorado-public-imagery.s3.amazonaws.com/index.html + AuthorName: State of Colorado OIT-GIS + AuthorURL: https://geodata.colorado.gov/ +ADXCategories: + - Public Sector Data diff --git a/datasets/commoncrawl.yaml b/datasets/commoncrawl.yaml index 25791b984..f33198846 100644 --- a/datasets/commoncrawl.yaml +++ b/datasets/commoncrawl.yaml @@ -1,5 +1,5 @@ Name: Common Crawl -Description: A corpus of web crawl data composed of over 50 billion web pages. +Description: A corpus of web crawl data composed of over 300 billion web pages. Documentation: https://commoncrawl.org/get-started Contact: https://commoncrawl.org/contact-us ManagedBy: "[Common Crawl](https://commoncrawl.org/)" @@ -19,6 +19,11 @@ Resources: AccountRequired: True DataAtWork: Tutorials: + - Title: Get To Know A Dataset - Common Crawl + URL: https://github.com/commoncrawl/whirlwind-python-notebook + NotebookURL: https://github.com/commoncrawl/whirlwind-python-notebook/blob/main/aws-ccf-dataset.ipynb + AuthorName: Common Crawl Foundation + AuthorURL: https://commoncrawl.org/ - Title: Analysing Petabytes of Websites URL: http://tech.marksblogg.com/petabytes-of-website-data-spark-emr.html AuthorName: Mark Litwintschik diff --git a/datasets/coralreef-image-classification-training.yaml b/datasets/coralreef-image-classification-training.yaml new file mode 100644 index 000000000..8716dd5a9 --- /dev/null +++ b/datasets/coralreef-image-classification-training.yaml @@ -0,0 +1,48 @@ +Name: Community coral reef image classification training data +Description: "Community-sourced repository of coral reef image classification training data, including continually updated confirmed annotations from [MERMAID](https://datamermaid.org/)" +Documentation: https://github.com/data-mermaid/image-classification-open-data +Contact: contact@datamermaid.org +ManagedBy: "[MERMAID](https://datamermaid.org/)" +UpdateFrequency: Each partner organization updates on their own cadence. MERMAID updates once per day. +Tags: + - aws-pds + - coastal + - conservation + - coral reef + - csv + - global + - machine learning + - marine + - parquet + - survey +License: "[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)" +Resources: + - Description: "The coral-reef-training AWS S3 bucket provides a single, open, well-structured, growing, community-sourced repository of coral reef image classification training data. Hosted at s3://coral-reef-training, this bucket supports global efforts in coral reef conservation through standardized, machine-learning-ready imagery and annotations. + +The bucket serves as the image storage backend for MERMAID’s image classification workflows and to distribute confirmed and scrubbed MERMAID coral reef image data, but it also provides a shared location where partners including CoralNet can contribute to and benefit from collective ML model development, each according to its own data structures and policies. Data in the bucket is free and open for public access; only contributing organizations have write access to their own data prefixes. + +By centralizing and standardizing coral reef image data, this initiative accelerates collaboration across scientific, conservation, and machine learning communities and facilitates the creation of a common, evolving image classification model for coral reefs worldwide." + ARN: arn:aws:s3:::coral-reef-training + Region: us-east-1 + Type: S3 Bucket + Explore: + - "[Browse Bucket](https://coral-reef-training.s3.amazonaws.com/index.html)" +DataAtWork: + Tutorials: + - Title: MERMAID Image Classification Open Data Tutorial - Python version + URL: https://data-mermaid.github.io/image-classification-open-data/image-classification-open-data-tutorial_Python.html + AuthorName: Domazetoski V, Caldwell I + AuthorURL: https://github.com/ViktorDomazetoski, https://github.com/ircaldwell + - Title: MERMAID Image Classification Open Data Tutorial - R version + URL: https://data-mermaid.github.io/image-classification-open-data/image-classification-open-data-tutorial_R.html + AuthorName: Caldwell I + AuthorURL: https://github.com/ircaldwell + Tools & Applications: + - Title: MERMAID Collect + URL: https://app.datamermaid.org/ + AuthorName: MERMAID + AuthorURL: https://datamermaid.org/ + - Title: MERMAID Explore + URL: https://explore.datamermaid.org/ + AuthorName: MERMAID + AuthorURL: https://datamermaid.org/ diff --git a/datasets/cropland_partitioining.yaml b/datasets/cropland_partitioining.yaml index 6ce1ecba3..3683e9b9e 100644 --- a/datasets/cropland_partitioining.yaml +++ b/datasets/cropland_partitioining.yaml @@ -1,45 +1,49 @@ -Name: IWMI DIWASA Rainfed and Irrigated Cropland Map for Africa -Description: A framework integrating the Budyko model has been developed to distinguish between rainfed and irrigated cropland areas across Africa. This expands on remote sensing land cover products available for agricultural water studies in Africa and thereby helps address the need for deeper insights into cropland patterns. Validation against an independent dataset revealed an overall accuracy of 73% with high precision and specificity scores. These results validate the framework’s effectiveness in identifying irrigated areas while minimizing errors in misclassifying rainfed areas as irrigated. -Documentation: https://github.com/iwmiwaplus/ODR/tree/master/Partitioned%20Croplands -Contact: iwmiwaplus@gmail.com -ManagedBy: "[IWMI](https://www.iwmi.org/)" -UpdateFrequency: None -Tags: - - cropland partitioning - - irrigated cropland - - rainfed cropland - - agriculture - - land use - - land cover -License: There are no restrictions on the use of this data. -Resources: - - Description: high-confidence cropland map (HCCM) - ARN: arn:aws:s3:::iwmi-datasets/Cropland_partition/HCCM/ - Region: af-south-1 - Type: S3 Bucket - Explore: - - '[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)' - - Description: Cropland partitioning all data - ARN: arn:aws:s3:::iwmi-datasets/Cropland_partition/ - Region: af-south-1 - Type: S3 Bucket - Explore: - - '[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)' -DataAtWork: - Tutorials: - - Title: Cropland percentage - URL: https://github.com/iwmiwaplus/ODR/tree/master/Partitioned%20Croplands/Tutorials - AuthorName: iwmiwaplus - AuthorURL: https://github.com/iwmiwaplus - Tools & Applications: - - Title: Water use in Awash basin - URL: https://github.com/iwmiwaplus/ODR/blob/master/Partitioned%20Croplands/Applications/Awash_cropland%20partitioning.pdf - AuthorName: A. Owusu, K. Akpoti, M. Leh, N. Velpuri - AuthorURL: https://github.com/iwmiwaplus - Publications: - - Title: A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale - URL: https://doi.org/10.1016/J.JAG.2023.103607 - AuthorName: Owusu, A., Kagone, S., Leh, M., Velpuri, N. M., Gumma, M. K., Ghansah, B., Thilina-Prabhath, P., Akpoti, K., Mekonnen, K., Tinonetsana, P., & Mohammed, I. - - Title: Rainfed and Irrigated Cropland Areas for Africa - URL: https://doi.org/10.5066/P9N4R7SF - AuthorName: Owusu, A., Kagone, S., Leh, M., and Velpuri, N.M. +Name: IWMI DIWASA Rainfed and Irrigated Cropland Map for Africa +Description: A framework integrating the Budyko model has been developed to distinguish between rainfed and irrigated cropland areas across Africa. This expands on remote sensing land cover products available for agricultural water studies in Africa and thereby helps address the need for deeper insights into cropland patterns. Validation against an independent dataset revealed an overall accuracy of 73% with high precision and specificity scores. These results validate the framework’s effectiveness in identifying irrigated areas while minimizing errors in misclassifying rainfed areas as irrigated. +Documentation: https://github.com/iwmiwaplus/ODR/tree/master/Partitioned%20Croplands +Contact: iwmiwaplus@gmail.com +ManagedBy: "[IWMI](https://www.iwmi.org/)" +UpdateFrequency: None +Collabs: + ASDI: + Tags: + - agriculture +Tags: + - cropland partitioning + - irrigated cropland + - rainfed cropland + - agriculture + - land use + - land cover +License: There are no restrictions on the use of this data. +Resources: + - Description: high-confidence cropland map (HCCM) + ARN: arn:aws:s3:::iwmi-datasets/Cropland_partition/HCCM/ + Region: af-south-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)' + - Description: Cropland partitioning all data + ARN: arn:aws:s3:::iwmi-datasets/Cropland_partition/ + Region: af-south-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)' +DataAtWork: + Tutorials: + - Title: Cropland percentage + URL: https://github.com/iwmiwaplus/ODR/tree/master/Partitioned%20Croplands/Tutorials + AuthorName: iwmiwaplus + AuthorURL: https://github.com/iwmiwaplus + Tools & Applications: + - Title: Water use in Awash basin + URL: https://github.com/iwmiwaplus/ODR/blob/master/Partitioned%20Croplands/Applications/Awash_cropland%20partitioning.pdf + AuthorName: A. Owusu, K. Akpoti, M. Leh, N. Velpuri + AuthorURL: https://github.com/iwmiwaplus + Publications: + - Title: A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale + URL: https://doi.org/10.1016/J.JAG.2023.103607 + AuthorName: Owusu, A., Kagone, S., Leh, M., Velpuri, N. M., Gumma, M. K., Ghansah, B., Thilina-Prabhath, P., Akpoti, K., Mekonnen, K., Tinonetsana, P., & Mohammed, I. + - Title: Rainfed and Irrigated Cropland Areas for Africa + URL: https://doi.org/10.5066/P9N4R7SF + AuthorName: Owusu, A., Kagone, S., Leh, M., and Velpuri, N.M. diff --git a/datasets/ctrees-california-vhr-tree-height.yaml b/datasets/ctrees-california-vhr-tree-height.yaml index d47f037ec..b2384cc86 100644 --- a/datasets/ctrees-california-vhr-tree-height.yaml +++ b/datasets/ctrees-california-vhr-tree-height.yaml @@ -5,6 +5,10 @@ Documentation: "[Project overview](https://ctrees.org/products/tree-level)" Contact: info@ctrees.org ManagedBy: "[CTrees](https://ctrees.org/)" UpdateFrequency: TBD +Collabs: + ASDI: + Tags: + - biodiversity Tags: - aws-pds - cog diff --git a/datasets/cwa_opendata.yaml b/datasets/cwa_opendata.yaml index 5cfd2550b..eabfc4faf 100644 --- a/datasets/cwa_opendata.yaml +++ b/datasets/cwa_opendata.yaml @@ -1,19 +1,23 @@ -Name: Central Weather Administration OpenData -Description: Various kinds of weather raw data and charts from Central Weather Administration. -Documentation: https://opendata.cwa.gov.tw/devManual/insrtuction -Contact: od@cwa.gov.tw -ManagedBy: "[Central Weather Administration](https://www.cwa.gov.tw/)" -UpdateFrequency: Data is updated as soon as newer one is available. -Tags: - - aws-pds - - climate - - earth observation - - earthquakes - - satellite imagery - - weather -License: http://data.gov.tw/license -Resources: - - Description: CWA data lake - ARN: arn:aws:s3:::cwaopendata - Region: ap-northeast-1 +Name: Central Weather Administration OpenData +Description: Various kinds of weather raw data and charts from Central Weather Administration. +Documentation: https://opendata.cwa.gov.tw/devManual/insrtuction +Contact: od@cwa.gov.tw +ManagedBy: "[Central Weather Administration](https://www.cwa.gov.tw/)" +UpdateFrequency: Data is updated as soon as newer one is available. +Collabs: + ASDI: + Tags: + - climate +Tags: + - aws-pds + - climate + - earth observation + - earthquakes + - satellite imagery + - weather +License: http://data.gov.tw/license +Resources: + - Description: CWA data lake + ARN: arn:aws:s3:::cwaopendata + Region: ap-northeast-1 Type: S3 Bucket \ No newline at end of file diff --git a/datasets/dandiarchive.yaml b/datasets/dandiarchive.yaml index 516561178..70dbb1296 100644 --- a/datasets/dandiarchive.yaml +++ b/datasets/dandiarchive.yaml @@ -8,7 +8,7 @@ Description: > [BIDS - Brain Imaging Data Structure](https://bids.neuroimaging.io/), and [NIDM - Neuro Imaging Data Model](http://nidm.nidash.org/). Development of DANDI is supported by the National Institute of Mental Health. -Documentation: http://dandiarchive.org +Documentation: https://dandiarchive.org Contact: '[DANDI Archive Help Desk](https://github.com/dandi/helpdesk/issues/new/choose)' ManagedBy: '[DANDI Archive](https://about.dandiarchive.org/team)' UpdateFrequency: New datasets deposited every month @@ -43,4 +43,3 @@ DataAtWork: URL: https://hub.dandiarchive.org/ AuthorName: DANDI Project AuthorURL: https://dandiarchive.org/ - Publications: diff --git a/datasets/deafrica-clgm-lwq.yaml b/datasets/deafrica-clgm-lwq.yaml new file mode 100644 index 000000000..f5e9a1d6a --- /dev/null +++ b/datasets/deafrica-clgm-lwq.yaml @@ -0,0 +1,104 @@ +Name: Digital Earth Africa - Copernicus Global Land Service - Lake Water Quality +Description: | + The Copernicus Global Land Service – Lake Water Quality products offer a comprehensive, satellite-derived monitoring system for assessing key water quality indicators in major large lakes, typically those greater than 50 hectares. These datasets are generated using optical satellite sensors, primarily Sentinel-2 MSI and Sentinel-3 OLCI, with earlier archives derived from Envisat MERIS. Spanning multiple spatial resolutions (100 m and 300 m) and temporal scales (10-day composites), they support both near-real-time and retrospective assessments of inland water quality. + + Key parameters include surface reflectance, turbidity, total suspended matter (TSM), chlorophyll-a concentration, trophic state index, and floating cyanobacteria risk—all essential for monitoring eutrophication, ecological health, and harmful algal blooms (HABs). The datasets cover the period from 2002 to the present, providing long-term continuity for environmental monitoring and scientific research, with focused coverage in Europe and Africa. + + All products are delivered using standardized geospatial grids (EPSG:4326) and include quality flags, detailed metadata, and validation against in situ observations to ensure reliability. Continuous improvements across product versions—such as enhanced atmospheric correction and updated retrieval algorithms—have significantly improved accuracy and usability. In addition, comprehensive user manuals, technical documentation, and support materials are available, making the data highly accessible to researchers, policymakers, and environmental managers. + + Digital Earth Africa (DE Africa) hosts these datasets for the African region, providing free and open access to both the data and associated tools. + +Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/CGLM_Lake_Water_Quality_specs.html +Contact: helpdesk@digitalearthafrica.org +ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" +UpdateFrequency: New scene-level data is added regularly, as the Lake Water Quality (LWQ) datasets are updated every 10 days (dekadal composites), with near-real-time versions typically available within 3 to 4 days after satellite acquisition. +Collabs: + ASDI: + Tags: + - satellite imagery +Tags: + - aws-pds + - agriculture + - disaster response + - earth observation + - geospatial + - natural resource + - satellite imagery + - water + - deafrica + - stac + - cog +License: | + DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/. +Resources: + - Description: Lake Water Quality 2019-2024 (raster 100 m), 10-daily – version 1 + ARN: arn:aws:s3:::deafrica-input-datasets/cgls_lwq100_2019_2024 + Region: af-south-1 + Type: S3 Bucket + RequesterPays: False + Explore: + - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq100_2019_2024)' + - Description: Lake Water Quality 2024 - present (raster 100 m), 10-daily – version 2 + ARN: arn:aws:s3:::deafrica-input-datasets/cgls_lwq100_2024_nrt + Region: af-south-1 + Type: S3 Bucket + RequesterPays: False + Explore: + - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq100_2024_nrt)' + - Description: Lake Water Quality 2002-2012 (raster 300 m), 10-daily – version 1 + ARN: arn:aws:s3:::deafrica-services/cgls_lwq300_2002_2012 + Region: af-south-1 + Type: S3 Bucket + RequesterPays: False + Explore: + - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq300_2002_2012)' + - Description: Lake Water Quality 2016-2024 (raster 300 m), 10-daily – version 1 + ARN: arn:aws:s3:::deafrica-services/cgls_lwq300_2016_2024 + Region: af-south-1 + Type: S3 Bucket + RequesterPays: False + Explore: + - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq300_2016_2024)' + - Description: Lake Water Quality 2024 - present (raster 300 m), 10-daily – version 2 + ARN: arn:aws:s3:::deafrica-services/cgls_lwq300_2024_nrt + Region: af-south-1 + Type: S3 Bucket + RequesterPays: False + Explore: + - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/cgls_lwq300_2024_nrt)' +DataAtWork: + Tutorials: + - Title: Digital Earth Africa Training + URL: http://learn.digitalearthafrica.org/ + AuthorName: Digital Earth Africa Contributors + Tools & Applications: + - Title: "Digital Earth Africa Explorer (Lake Water Quality 2019-2024 (raster 100 m), 10-daily – version 1)" + URL: https://explorer.digitalearth.africa/products/cgls_lwq100_2019_2024 + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Explorer ( Lake Water Quality 2024 - present (raster 100 m), 10-daily – version 2)" + URL: https://explorer.digitalearth.africa/products/cgls_lwq100_2024_nrt + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Explorer ( Lake Water Quality 2002-2012 (raster 300 m), 10-daily – version 1)" + URL: https://explorer.digitalearth.africa/products/cgls_lwq300_2002_2012 + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Explorer ( Lake Water Quality 2016-2024 (raster 300 m), 10-daily – version 1)" + URL: https://explorer.digitalearth.africa/products/cgls_lwq300_2016_2024 + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Explorer ( Lake Water Quality 2024 - present (raster 300 m), global, 10-daily – version 2)" + URL: https://explorer.digitalearth.africa/products/cgls_lwq300_2024_nrt + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa web services" + URL: https://ows.digitalearth.africa + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Map" + URL: https://maps.digitalearth.africa/ + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Sandbox" + URL: https://sandbox.digitalearth.africa/ + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Notebook Repo" + URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Geoportal" + URL: https://www.africageoportal.com/pages/digital-earth-africa + AuthorName: Digital Earth Africa Contributors diff --git a/datasets/deafrica-sentinel-1-mosaic.yaml b/datasets/deafrica-sentinel-1-mosaic.yaml new file mode 100644 index 000000000..b98b10ee7 --- /dev/null +++ b/datasets/deafrica-sentinel-1-mosaic.yaml @@ -0,0 +1,55 @@ +Name: Sentinel-1 Monthly Mosaic +Description: | + Synthetic Aperture Radar (SAR) sensor have the advantage of operating at wavelengths not impeded by cloud cover and can acquire data over a site during the day or night. The Sentinel-1 mission, part of the Copernicus joint initiative by the European Commission (EC) and the European Space Agency (ESA), provides reliable and repeated wide-area monitoring using its SAR instrument.Sentinel-1 Monthly Mosaics are analysis-ready product of individual Sentinel-1 acquisitions. Sentinel-1 monthly mosaics are generated from Radiometric Terrain Corrected (RTC) backscatter data, with variations from changing observation geometries mitigated. RTC images acquired within a calendar month are combined using a multitemporal compositing algorithm. This algorithm calculates a weighted average of valid pixels, assigning higher weights to pixels with higher local resolution (e.g., slopes facing away from the sensor). This local resolution weighting approach minimizes noise and improves spatial homogeneity in the composites. Sinergise (Planet Labs) processed and indexed the product on the DE Africa platform + +Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_Monthly_Mosaic_specs.html +Contact: helpdesk@digitalearthafrica.org +ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" +UpdateFrequency: N/A. +Collabs: + ASDI: + Tags: + - satellite imagery +Tags: + - aws-pds + - agriculture + - earth observation + - satellite imagery + - geospatial + - natural resource + - disaster response + - deafrica + - stac + - cog + - synthetic aperture radar +License: | + Access to S1 Monthly Mosaic data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions). +Resources: + - Description: S1 Monthly Mosaic tiles and metadata + ARN: arn:aws:s3:::deafrica-sentinel-1 + Region: af-south-1 + Type: S3 Bucket + RequesterPays: False + Explore: + - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/products/s1_monthly_mosaic)' +DataAtWork: + Tools & Applications: + - Title: "Digital Earth Africa Explorer" + URL: https://explorer.digitalearth.africa/products/s1_monthly_mosaic/extents + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa web services" + URL: https://ows.digitalearth.africa + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Map" + URL: https://maps.digitalearth.africa/ + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Sandbox" + URL: https://sandbox.digitalearth.africa/ + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Notebook Repo" + URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Geoportal" + URL: https://www.africageoportal.com/pages/digital-earth-africa + AuthorName: Digital Earth Africa Contributors + diff --git a/datasets/deafrica-sentinel-2-c1.yaml b/datasets/deafrica-sentinel-2-c1.yaml new file mode 100644 index 000000000..6de6ef0ab --- /dev/null +++ b/datasets/deafrica-sentinel-2-c1.yaml @@ -0,0 +1,87 @@ +Name: Digital Earth Africa Sentinel-2 Level-2A Surface Reflectance Collection 1 +Description: | + The Sentinel-2 mission is part of the European Union Copernicus programme for Earth observations. Sentinel-2 consists of twin satellites, Sentinel-2A (launched 23 June 2015) and Sentinel-2B (launched 7 March 2017). The two satellites have the same orbit, but 180° apart for optimal coverage and data delivery. Their combined data is used in the Digital Earth Africa Sentinel-2 product. + Together, they cover all Earth’s land surfaces, large islands, inland and coastal waters every 3-5 days. + Sentinel-2 data is tiered by level of pre-processing. Level-0, Level-1A and Level-1B data contain raw data from the satellites, with little to no pre-processing. Level-1C data is surface reflectance measured at the top of the atmosphere. This is processed using the Sen2Cor algorithm to give Level-2A, the bottom-of-atmosphere reflectance (Obregón et al, 2019). Level-2A data is the most ideal for research activities as it allows further analysis without applying additional atmospheric corrections. + Digital Earth Africa Sentinel-2 Level-2A Surface Reflectance Collection 1 is the Sentinel-2 product processed for enhanced calibration and consistent time series between Sentinel-2A and Sentinel-2B. Digital Earth Africa does not host any lower-level Sentinel-2 data. + + + Note that this data is a subset of the Sentinel-2 COGs dataset. +Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_specs.html +Contact: helpdesk@digitalearthafrica.org +ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" +UpdateFrequency: New Sentinel-2 scenes are added regularly, usually within few hours after they are available on Copernicus OpenHub. +Collabs: + ASDI: + Tags: + - satellite imagery +Tags: + - aws-pds + - agriculture + - earth observation + - satellite imagery + - geospatial + - natural resource + - disaster response + - deafrica + - stac + - cog +License: | + Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions). +Resources: + - Description: Sentinel-2 scenes and metadata + ARN: arn:aws:s3:::deafrica-sentinel-2-l2a-c1/sentinel-2-c1-l2a + Region: af-south-1 + Type: S3 Bucket + RequesterPays: False + Explore: + - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s2_l2a_c1)' + - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" + ARN: arn:aws:s3:::deafrica-sentinel-2-l2a-c1/sentinel-2-c1-l2a-inventory + Region: af-south-1 + Type: S3 Bucket + - Description: New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item. + ARN: arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-l2a-c1-scene-topic + Region: af-south-1 + Type: SNS Topic + - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by deafrica-sentinel-2-l2a-c1 s3 bucket all object create events. + ARN: arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-topic + Region: af-south-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Use Sentinel-2-C1 data in the Open Data Cube + URL: https://github.com/opendatacube/cube-in-a-box + AuthorName: Alex Leith + - Title: Digital Earth Africa Training + URL: http://learn.digitalearthafrica.org/ + AuthorName: Digital Earth Africa Contributors + - Title: Downloading and streaming data using STAC metadata + URL: https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Frequently_used_code/Downloading_data_with_STAC.html + AuthorName: Digital Earth Africa Contributors + Tools & Applications: + - Title: "Digital Earth Africa Explorer" + URL: https://explorer.digitalearth.africa/products/s2_l2a_c1/extents + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa web services" + URL: https://ows.digitalearth.africa + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Map" + URL: https://maps.digitalearth.africa/ + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Sandbox" + URL: https://sandbox.digitalearth.africa/ + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Notebook Repo" + URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks + AuthorName: Digital Earth Africa Contributors + - Title: "Digital Earth Africa Geoportal" + URL: https://www.africageoportal.com/pages/digital-earth-africa + AuthorName: Digital Earth Africa Contributors + Publications: + - Title: "Introduction to DE Africa" + URL: https://youtu.be/Wkf7N6O9jJQ + AuthorName: Dr Fang Yuan + - Title: "S2 Processing" + URL: https://sentiwiki.copernicus.eu/web/s2-processing#S2Processing-Collection-1ProcessingBaselineS2-Processing-Collection-Processing-Baseline + AuthorName: Sentiwiki diff --git a/datasets/deepdrug-dpeb.yaml b/datasets/deepdrug-dpeb.yaml new file mode 100644 index 000000000..30779b6c3 --- /dev/null +++ b/datasets/deepdrug-dpeb.yaml @@ -0,0 +1,38 @@ +Name: DeepDrug Protein Embeddings Bank (DPEB) +Description: DPEB is a multimodal database of human protein embeddings integrating four biologically complementary representations—AlphaFold2, BioEmbeddings, ESM-2, and ProtVec—designed for enhanced protein-protein interaction prediction and functional classification. +Documentation: https://github.com/deepdrugai/DPEB +Contact: https://github.com/deepdrugai/DPEB/issues +ManagedBy: "Louisiana State University" +UpdateFrequency: Initial release; maintained for at least 2 years with updates planned based on new embedding models and protein coverage. +Tags: + - bioinformatics + - protein + - structural biology + - machine learning + - life sciences + - aws-pds +License: MIT +Citation: "Sajol MSI et al. DeepDrug Protein Embeddings Bank (DPEB) was accessed on [DATE] at https://registry.opendata.aws/dpeb" +Resources: + - Description: Multimodal human protein embeddings (AlphaFold2, BioEmbeddings, ESM-2, ProtVec) with JSONL-formatted metadata containing FASTA, UniProt IDs, and embeddings. + ARN: arn:aws:s3:::deepdrug-dpeb + Region: us-west-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Aggregating and Clustering AlphaFold2 Embeddings from DPEB + URL: https://github.com/deepdrugai/DPEB/tree/main + AuthorName: Md. Saiful Islam Sajol + AuthorURL: https://github.com/deepdrugai + Tools & Applications: + - Title: DPEB Explorer Tool + URL: https://github.com/deepdrugai/DPEB + AuthorName: DeepDrug Lab + AuthorURL: https://github.com/deepdrugai + Publications: + - Title: A Multimodal Human Protein Embeddings Database - DeepDrug Protein Embeddings Bank (DPEB) + URL: https://doi.org/10.XXXX/nar.dpeb2025 + AuthorName: Sajol MSI, Rajasekaran M, Bess A, Alvin C, Mukhopadhyay S + AuthorURL: https://github.com/deepdrugai/DPEB +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/dendritic-consortium.yaml b/datasets/dendritic-consortium.yaml new file mode 100644 index 000000000..2553462e0 --- /dev/null +++ b/datasets/dendritic-consortium.yaml @@ -0,0 +1,45 @@ +Name: Dendritic Consortium Multimodal Dataset +Description: The Dendritic Consortium provides a multimodal dataset integrating calcium and voltage imaging, electrophysiology, electron microscopy, proteomics, and computational models of Baz1a pyramidal neurons in the mouse primary visual cortex (V1). +Documentation: https://github.com/jpcastanoo/aws-open-data-dendritic-consortium +Contact: dendriticconsortium@gmail.com +ManagedBy: Dendritic Consortium +UpdateFrequency: Continuously updated as new experimental and computational data are generated. +Tags: + - brain images + - brain models + - electrophysiology + - electron microscopy + - imaging + - life sciences + - Mus musculus + - neuroscience + - neurobiology + - neuroimaging + - neurophysiology + - simulation neuroscience + - single neuron models + - aws-pds +License: There are no restrictions on the use of this data. +Resources: + - Description: "Multimodal dataset from Baz1a pyramidal neurons in mouse V1, including TIFF, ABF, MAT, CSV, PNG, PY, HOC, and SWC files." + ARN: arn:aws:s3:::dendritic-consortium + Region: us-west-2 + Type: S3 Bucket + - Description: Notifications for new Dendritic Consortium data + ARN: arn:aws:sns:us-west-2:662855374544:dendritic-consortium-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Download and Visualize Data from the Dendritic Consortium Dataset + URL: https://github.com/jpcastanoo/aws-open-data-dendritic-consortium/tree/main/tutorials + AuthorName: Dendritic Consortium + AuthorURL: https://github.com/jpcastanoo/aws-open-data-dendritic-consortium + Tools & Applications: + - Title: Dendritic Consortium Database + URL: https://dendritic-consortium.vercel.app/database + AuthorName: Dendritic Consortium + AuthorURL: https://dendritic-consortium.vercel.app +ADXCategories: + - Healthcare & Life Sciences Data + diff --git a/datasets/dep-coastlines.yaml b/datasets/dep-coastlines.yaml index 33fd94d62..e292875ec 100644 --- a/datasets/dep-coastlines.yaml +++ b/datasets/dep-coastlines.yaml @@ -9,6 +9,10 @@ Documentation: https://digitalearthpacific.org/#/applications Contact: dep@spc.int ManagedBy: "[Pacific Community (SPC)](https://www.spc.int/)" UpdateFrequency: Annually +Collabs: + ASDI: + Tags: + - oceans Tags: - earth observation - environmental diff --git a/datasets/dep-ls-geomads.yaml b/datasets/dep-ls-geomads.yaml index a7e9424f0..1f04bc0e1 100644 --- a/datasets/dep-ls-geomads.yaml +++ b/datasets/dep-ls-geomads.yaml @@ -8,6 +8,10 @@ Documentation: https://digitalearthpacific.org/#/applications Contact: dep@spc.int ManagedBy: "[Pacific Community (SPC)](https://www.spc.int/)" UpdateFrequency: Annually +Collabs: + ASDI: + Tags: + - satellite imagery Tags: - earth observation - geoscience diff --git a/datasets/dep-mangroves.yaml b/datasets/dep-mangroves.yaml index fb377b25f..59874cdfe 100644 --- a/datasets/dep-mangroves.yaml +++ b/datasets/dep-mangroves.yaml @@ -12,6 +12,10 @@ Documentation: https://digitalearthpacific.org/#/applications Contact: dep@spc.int ManagedBy: "[Pacific Community (SPC)](https://www.spc.int/)" UpdateFrequency: Annually +Collabs: + ASDI: + Tags: + - biodiversity Tags: - earth observation - environmental diff --git a/datasets/dep-s1-annual-mosaics.yaml b/datasets/dep-s1-annual-mosaics.yaml index f7cef8615..8df7f0eb3 100644 --- a/datasets/dep-s1-annual-mosaics.yaml +++ b/datasets/dep-s1-annual-mosaics.yaml @@ -7,6 +7,10 @@ Documentation: https://digitalearthpacific.org/#/applications Contact: dep@spc.int ManagedBy: "[Pacific Community (SPC)](https://www.spc.int/)" UpdateFrequency: Annually +Collabs: + ASDI: + Tags: + - climate Tags: - earth observation - environmental diff --git a/datasets/dep-s2-geomads.yaml b/datasets/dep-s2-geomads.yaml index 669e567e6..13c9b1e4c 100644 --- a/datasets/dep-s2-geomads.yaml +++ b/datasets/dep-s2-geomads.yaml @@ -13,6 +13,10 @@ Documentation: https://digitalearthpacific.org/#/applications Contact: dep@spc.int ManagedBy: "[Pacific Community (SPC)](https://www.spc.int/)" UpdateFrequency: Annually +Collabs: + ASDI: + Tags: + - climate Tags: - earth observation - geoscience diff --git a/datasets/dep-wofs.yaml b/datasets/dep-wofs.yaml index 9c08e3ea1..369835ef9 100644 --- a/datasets/dep-wofs.yaml +++ b/datasets/dep-wofs.yaml @@ -10,6 +10,10 @@ Documentation: https://digitalearthpacific.org/#/applications Contact: dep@spc.int ManagedBy: "[Pacific Community (SPC)](https://www.spc.int/)" UpdateFrequency: Annually +Collabs: + ASDI: + Tags: + - oceans Tags: - earth observation - environmental diff --git a/datasets/depmap-omics-ccle.yaml b/datasets/depmap-omics-ccle.yaml new file mode 100644 index 000000000..f50630598 --- /dev/null +++ b/datasets/depmap-omics-ccle.yaml @@ -0,0 +1,71 @@ +Name: The Cancer Dependency Map (DepMap) Cancer Cell Line Encyclopedia (CCLE) Dataset +Description: This dataset consists of whole genome sequencing (WGS), whole exome sequencing (WES), and RNA sequencing files generated from ~1000 cancer cell lines described in Ghandi et al., 2019. +Documentation: https://github.com/broadinstitute/depmap-omics-ccle +Contact: https://forum.depmap.org +ManagedBy: "[Cancer Data Science](https://cancerdatascience.org/), [Broad Institute](https://www.broadinstitute.org/)" +UpdateFrequency: occasionally (as additional sequencings are generated for publicly-releasible CCLE models) +Tags: + - aws-pds + - bam + - biology + - bioinformatics + - cancer + - genetic + - genomic + - Homo sapiens + - life sciences + - short read sequencing + - transcriptomics + - whole exome sequencing + - whole genome sequencing +License: https://grants.nih.gov/policy-and-compliance/policy-topics/sharing-policies/accessing-data/using-genomic-data +Citation: Ghandi, Huang, Jané-Valbuena et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019). https://doi.org/10.1038/s41586-019-1186-3 +Resources: + - Description: CRAM/BAM files (and their corresponding CRAI/BAI indexes) for RNA, WES, and WGS samples released by The Cancer Dependency Map (DepMap) as part of the Cancer Cell Line Encyclopedia (CCLE) project + ARN: arn:aws:s3:::depmap-omics-ccle + Region: us-east-1 + Type: S3 Bucket + - Description: Notifications for new depmap-omics-ccle data + ARN: arn:aws:sns:us-east-1:019511184952:depmap-omics-ccle-object_created + Region: us-east-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: DepMap Omics CCLE data on the AWS Open Data Registry + URL: https://github.com/broadinstitute/depmap-omics-ccle + AuthorName: Devin McCabe + Tools & Applications: + - Title: The Cancer Dependency Map (DepMap) + URL: https://depmap.org + AuthorName: Arafeh, Shibue, Dempster et al. + - Title: Cancer Cell Line Encyclopedia (CCLE) + URL: https://sites.broadinstitute.org/ccle + AuthorName: Ghandi, Huang, Jané-Valbuena et al. + Publications: + - Title: Next-generation characterization of the Cancer Cell Line Encyclopedia + URL: https://www.nature.com/articles/s41586-019-1186-3 + AuthorName: Ghandi, Huang, Jané-Valbuena et al. + - Title: The present and future of the Cancer Dependency Map + URL: https://www.nature.com/articles/s41568-024-00763-x + AuthorName: Arafeh, Shibue, Dempster et al. + AuthorURL: https://depmap.org + - Title: Partial gene suppression improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal + URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-03020-w + AuthorName: Krill-Burger, Dempster, Borah et al. + - Title: Genetic dependencies associated with transcription factor activities in human cancer cell lines + URL: https://www.sciencedirect.com/science/article/pii/S2211124724005035 + AuthorName: Thatikonda, Supper, Wachter et al. + - Title: Bridging the gap between cancer cell line models and tumours using gene expression data + URL: https://www.nature.com/articles/s41416-021-01359-0 + AuthorName: Noorbakhsh, Vazquez & McFarland + - Title: Integrated cross-study datasets of genetic dependencies in cancer + URL: https://www.nature.com/articles/s41467-021-21898-7 + AuthorName: Pacini, Dempster, Boyle et al. + - Title: Machine learning multi-omics analysis reveals cancer driver dysregulation in pan-cancer cell lines compared to primary tumors + URL: https://www.nature.com/articles/s42003-022-04075-4 + AuthorName: Sanders, Chandra, Zebarjadi et al. + - Title: "The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks" + URL: https://link.springer.com/article/10.1186/s13059-023-02877-1 + AuthorName: Ben Guebila, Wang, Lopes-Ramos et al. +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/dmi-danra-05.yaml b/datasets/dmi-danra-05.yaml new file mode 100644 index 000000000..04b7c1ed4 --- /dev/null +++ b/datasets/dmi-danra-05.yaml @@ -0,0 +1,50 @@ +Name: Danish Meteorological Institute (DMI) Reanalysis dataset v0.5 +Description: DANRA is a high-resolution meteorological reanalysis dataset for Denmark and Northwestern Europe covering the period September 1990 to December 2023 +Documentation: https://dmidk.github.io/danradocs/intro.html +Contact: https://www.dmi.dk/kontakt +ManagedBy: "[Danish Meteorological Institute](https://www.dmi.dk/)" +UpdateFrequency: Not updated +Collabs: + ASDI: + Tags: + - climate + - weather +Tags: + - aws-pds + - air temperature + - atmosphere + - geospatial + - global + - land + - meteorological + - near-surface air temperature + - near-surface relative humidity + - near-surface specific humidity + - model + - water + - weather + - zarr +License: DMI Reanalysis dataset v0.5 is distributed under the [Creative Commons License CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en) +Resources: + - Description: DMI Reanalysis dataset v0.5 + ARN: arn:aws:s3:::dmi-danra-05 + Region: eu-north-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Looking at distributions + URL: https://dmidk.github.io/danradocs/notebooks/distributions.html + NotebookURL: https://dmidk.github.io/danradocs/_sources/notebooks/distributions.ipynb + AuthorName: Danish Meteorological Institute + AuthorURL: https://www.dmi.dk/ + Services: + - Amazon S3 + - Title: DANRA figures + URL: https://dmidk.github.io/danradocs/notebooks/paper-figures.html + NotebookURL: https://dmidk.github.io/danradocs/_sources/notebooks/paper-figures.ipynb + AuthorName: Danish Meteorological Institute + AuthorURL: https://www.dmi.dk/ + Services: + - Amazon S3 +ADXCategories: + - Environmental Data diff --git a/datasets/dmi-opendata.yaml b/datasets/dmi-opendata.yaml index eab6d02a1..c70d9b2ae 100644 --- a/datasets/dmi-opendata.yaml +++ b/datasets/dmi-opendata.yaml @@ -1,9 +1,13 @@ Name: Danish Meteorological Institute (DMI) Open Data Forecasts Description: DMI forecast data consist of various models where each model contains different set of parameters relating to a specific domain like ocean (WAM), storm flooding (DKSS) or weather (HARMONIE) -Documentation: https://opendatadocs.dmi.govcloud.dk/en/Data/Forecast_Data -Contact: https://opendatadocs.dmi.govcloud.dk/en/API_Status_and_Contact +Documentation: https://www.dmi.dk/friedata/dokumentation/forecast-data +Contact: https://www.dmi.dk/friedata/dokumentation/api-status-contact ManagedBy: "[Danish Meteorological Institute](https://www.dmi.dk/)" UpdateFrequency: Every hour, 3 hours or 6 hours depending on model +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - air temperature diff --git a/datasets/dynamical-ecmwf-ifs-ens.yaml b/datasets/dynamical-ecmwf-ifs-ens.yaml new file mode 100644 index 000000000..1c8dd1604 --- /dev/null +++ b/datasets/dynamical-ecmwf-ifs-ens.yaml @@ -0,0 +1,49 @@ +Name: ECMWF IFS ENS +Description: | +

+ The Integrated Forecasting System (IFS) is a global forecast model developed + by ECMWF. ENS is an ensemble configuration of IFS, containing 51 ensemble members. + IFS consists of a numerical model of the Earth system, which includes + an atmospheric model at its heart, coupled with models of other Earth system + components such as the ocean. The data assimilation system combines + the latest weather observations with a recent forecast to obtain the best + possible estimate of the current state of the Earth system. +

+

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

+

When Icechunk 2.0 is released, these datasets will be updated correspondingly, and updated client libraries will be required for access. To be notified of dataset updates, subscribe to the mailing list.

+ +Documentation: https://dynamical.org/catalog/models/ecmwf-ifs-ens/ +Contact: feedback@dynamical.org +ManagedBy: "[dynamical.org](https://dynamical.org)" +UpdateFrequency: "ECMWF IFS ENS Forecast, 15 day, 0.25 degree: Forecasts initialized every 24 hours" +Tags: + - aws-pds + - weather + - atmosphere + - meteorological + - climate + - forecast + - zarr +License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)" +Resources: + - Description: ECMWF IFS ENS Icechunk Zarr data + ARN: arn:aws:s3:::dynamical-ecmwf-ifs-ens + Region: us-west-2 + Type: S3 Bucket + Explore: + - "[Browse Bucket](https://dynamical-ecmwf-ifs-ens.s3.amazonaws.com/index.html)" + - Description: Notifications for dataset updates + ARN: arn:aws:sns:us-west-2:761136292730:dynamical-ecmwf-ifs-ens-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: ECMWF IFS ENS Forecast, 15 day, 0.25 degree python quickstart notebook + URL: https://github.com/dynamical-org/notebooks/blob/main/ecmwf-ifs-ens-forecast-15-day-0-25-degree-icechunk.ipynb + NotebookURL: https://github.com/dynamical-org/notebooks/blob/main/ecmwf-ifs-ens-forecast-15-day-0-25-degree-icechunk.ipynb + AuthorName: dynamical.org + AuthorURL: https://dynamical.org +ADXCategories: + - Environmental Data diff --git a/datasets/dynamical-noaa-gfs.yaml b/datasets/dynamical-noaa-gfs.yaml new file mode 100644 index 000000000..c5977956d --- /dev/null +++ b/datasets/dynamical-noaa-gfs.yaml @@ -0,0 +1,49 @@ +Name: NOAA GFS +Description: | +

+ The Global Forecast System (GFS) is a National Oceanic and Atmospheric + Administration (NOAA) National Centers for Environmental Prediction + (NCEP) weather forecast model that generates data for dozens of + atmospheric and land-soil variables, including temperatures, winds, + precipitation, soil moisture, and atmospheric ozone concentration. The + system couples four separate models (atmosphere, ocean model, land/soil + model, and sea ice) that work together to depict weather conditions. +

+

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

+

When Icechunk 2.0 is released, these datasets will be updated correspondingly, and updated client libraries will be required for access. To be notified of dataset updates, subscribe to the mailing list.

+ +Documentation: https://dynamical.org/catalog/models/noaa-gfs/ +Contact: feedback@dynamical.org +ManagedBy: "[dynamical.org](https://dynamical.org)" +UpdateFrequency: "NOAA GFS forecast: Forecasts initialized every 6 hours" +Tags: + - aws-pds + - weather + - atmosphere + - meteorological + - climate + - forecast + - zarr +License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)" +Resources: + - Description: NOAA GFS Icechunk Zarr data + ARN: arn:aws:s3:::dynamical-noaa-gfs + Region: us-west-2 + Type: S3 Bucket + Explore: + - "[Browse Bucket](https://dynamical-noaa-gfs.s3.amazonaws.com/index.html)" + - Description: Notifications for dataset updates + ARN: arn:aws:sns:us-west-2:761136292730:dynamical-noaa-gfs-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: NOAA GFS forecast python quickstart notebook + URL: https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-forecast-icechunk.ipynb + NotebookURL: https://github.com/dynamical-org/notebooks/blob/main/noaa-gfs-forecast-icechunk.ipynb + AuthorName: dynamical.org + AuthorURL: https://dynamical.org +ADXCategories: + - Environmental Data \ No newline at end of file diff --git a/datasets/dynamical-noaa-hrrr.yaml b/datasets/dynamical-noaa-hrrr.yaml new file mode 100644 index 000000000..683cab422 --- /dev/null +++ b/datasets/dynamical-noaa-hrrr.yaml @@ -0,0 +1,48 @@ +Name: NOAA HRRR +Description: | +

+ The High-Resolution Rapid Refresh (HRRR) is a NOAA real-time 3-km resolution, + hourly updated, cloud-resolving, convection-allowing atmospheric model, + initialized by 3km grids with 3km radar assimilation. Radar data is + assimilated in the HRRR every 15 min over a 1-h period adding further + detail to that provided by the hourly data assimilation from the 13km + radar-enhanced Rapid Refresh. +

+

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

+

When Icechunk 2.0 is released, these datasets will be updated correspondingly, and updated client libraries will be required for access. To be notified of dataset updates, subscribe to the mailing list.

+ +Documentation: https://dynamical.org/catalog/models/noaa-hrrr/ +Contact: feedback@dynamical.org +ManagedBy: "[dynamical.org](https://dynamical.org)" +UpdateFrequency: "NOAA HRRR forecast, 48 hour: Forecasts initialized every 6 hours" +Tags: + - aws-pds + - weather + - atmosphere + - meteorological + - climate + - forecast + - zarr +License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)" +Resources: + - Description: NOAA HRRR Icechunk Zarr data + ARN: arn:aws:s3:::dynamical-noaa-hrrr + Region: us-west-2 + Type: S3 Bucket + Explore: + - "[Browse Bucket](https://dynamical-noaa-hrrr.s3.amazonaws.com/index.html)" + - Description: Notifications for dataset updates + ARN: arn:aws:sns:us-west-2:761136292730:dynamical-noaa-hrrr-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: NOAA HRRR forecast, 48 hour python quickstart notebook + URL: https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-forecast-48-hour-icechunk.ipynb + NotebookURL: https://github.com/dynamical-org/notebooks/blob/main/noaa-hrrr-forecast-48-hour-icechunk.ipynb + AuthorName: dynamical.org + AuthorURL: https://dynamical.org +ADXCategories: + - Environmental Data \ No newline at end of file diff --git a/datasets/e11bio-prism.yaml b/datasets/e11bio-prism.yaml new file mode 100644 index 000000000..56a92e88d --- /dev/null +++ b/datasets/e11bio-prism.yaml @@ -0,0 +1,62 @@ +Name: E11bio PRISM +Description: | + This dataset was generated using E11.bio's PRISM technology (Protein Reconstruction and Identification through Multiplexing), + a platform that combines viral barcoding, expansion microscopy, and iterative immunolabeling for large-scale neuronal reconstruction. + + Neurons in the mouse hippocampal CA3 were transduced with a library of adeno-associated viruses (AAVs) + encoding diverse “protein bits”—small epitope tags that act as combinatorial barcodes. + Tissue was then processed with an expansion microscopy protocol, physically enlarging the sample ~5× + to achieve an effective voxel size of ~35 × 35 × 80 nm. + Across multiple cycles of staining, imaging, and antibody stripping, the same expanded tissue was repeatedly labeled, + enabling iterative immunostaining for dozens of molecular targets. + + The dataset includes: + 1) Light microscopy data of multiplexed brain tissue + 2) Segmentations of cell morphology and protein expression in the tissue + 3) Files for faster visualization of the data (e.g. precomputed format) + 4) Additional supporting files (e.g. model predictions, manual annotations etc.) +Documentation: https://github.com/e11bio/e11-open-data +Contact: hello@e11.bio +ManagedBy: "[E11.bio](https://e11.bio)" +UpdateFrequency: As required +Tags: + - bioinformatics + - biology + - brain images + - cell imaging + - computer vision + - fluorescence imaging + - high-throughput imaging + - image processing + - imaging + - ion channels + - life sciences + - machine learning + - microscopy + - morphological reconstructions + - Mus musculus + - neurobiology + - neuroimaging + - neuroscience + - protein + - segmentation + - zarr + - aws-pds +License: https://e11.bio/terms-of-use +Resources: + - Description: Data files in a public bucket + ARN: arn:aws:s3:::e11bio-prism + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: E11.Bio PRISM OpenData + URL: https://github.com/e11bio/e11-open-data + NotebookURL: + AuthorName: Arlo Sheridan & Johan Winnubst + AuthorURL: https://e11.bio/team + Tools & Applications: + - Title: Volara + URL: https://github.com/e11bio/volara + AuthorName: Arlo Sheridan & Will Patton + AuthorURL: https://e11.bio/team diff --git a/datasets/eai-essential-web-v1.yaml b/datasets/eai-essential-web-v1.yaml new file mode 100644 index 000000000..375bec992 --- /dev/null +++ b/datasets/eai-essential-web-v1.yaml @@ -0,0 +1,28 @@ +Name: 'Essential-Web v1.0: 24T tokens of organized web data' +Description: A 24-trillion-token dataset in which every document is annotated with a twelve-category taxonomy covering topic, format, content complexity, and quality. +Documentation: https://huggingface.co/datasets/EssentialAI/essential-web-v1.0 +Contact: research@essential.ai +ManagedBy: '[EssentialAI](https://www.essential.ai)' +UpdateFrequency: Not updated +Tags: + - aws-pds + - machine learning + - natural language processing + - web archive + - text analysis +License: 'Essential-Web-v1.0 contributions are made available under the [ODC attribution license](https://opendatacommons.org/licenses/by/odc_by_1.0_public_text.txt); however, users should also abide by the [Common Crawl - Terms of Use](https://commoncrawl.org/terms-of-use). We do not alter the license of any of the underlying data.' +Resources: + - Description: 'Essential-Web v1.0: 24T tokens of organized web data' + ARN: arn:aws:s3:::essential-web-v1.0 + Region: us-west-2 + Type: S3 Bucket + - Description: Notifications for new Essential-Web v1.0 data + ARN: arn:aws:sns:us-west-2:021391128517:essential-web-v10-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Publications: + - Title: 'Essential-Web v1.0: 24T tokens of organized web data' + URL: https://arxiv.org/abs/2506.14111 + AuthorName: Andrew Hojel, Michael Pust, Tim Romanski, Yash Vanjani, Ritvik Kapila, Mohit Parmar et al. + AuthorURL: https://arxiv.org/abs/2506.14111 diff --git a/datasets/ecmwf-era5.yaml b/datasets/ecmwf-era5.yaml index 2f40a542a..4793f608b 100644 --- a/datasets/ecmwf-era5.yaml +++ b/datasets/ecmwf-era5.yaml @@ -1,5 +1,6 @@ Deprecated: True -DeprecatedNotice: The provider of this dataset will no longer maintain this dataset. We are open to talking with anyone else who might be willing to provide this dataset to the community. Contact opendata@amazon.com. +DeprecatedNotice: | +

The provider of this dataset will no longer maintain it, but has instead worked with NSF NCAR to rehost the dataset here: https://registry.opendata.aws/nsf-ncar-era5/

Name: ECMWF ERA5 Reanalysis Description: | ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, and the first reanalysis produced as an operational service. It utilizes the best available observation data from satellites and in-situ stations, which are assimilated and processed using ECMWF's Integrated Forecast System (IFS) Cycle 41r2. diff --git a/datasets/ecmwf-forecasts.yaml b/datasets/ecmwf-forecasts.yaml index 638a6c084..94ace5f1e 100644 --- a/datasets/ecmwf-forecasts.yaml +++ b/datasets/ecmwf-forecasts.yaml @@ -5,6 +5,10 @@ Documentation: "[User Documentation](https://confluence.ecmwf.int/display/DAC/EC Contact: https://confluence.ecmwf.int/site/support ManagedBy: "[European Centre for Medium-Range Weather Forecasts](https://www.ecmwf.int/)" UpdateFrequency: "The data are released 1 hour after the [real-time dissemination schedule](https://confluence.ecmwf.int/display/DAC/Dissemination+schedule)." +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - air temperature diff --git a/datasets/ember.yaml b/datasets/ember.yaml new file mode 100644 index 000000000..a32ecd8c8 --- /dev/null +++ b/datasets/ember.yaml @@ -0,0 +1,47 @@ +Name: EMBER Open Datasets +Description: This is data from, Ecosystem for Multi-modal Brain-behavior Experimentation and Research (EMBER), It contains time series behavioral and neuroscience data from animal and deidentified human subjects across multiple modalities. +Documentation: https://emberarchive.org/ +Contact: brock.wester@jhuapl.edu +ManagedBy: "[Johns Hopkins University Applied Physics Laboratory](https://www.jhuapl.edu)" +UpdateFrequency: New datasets are added as soon as it is available. Minor updates on existing datasets occur sporadically. +Tags: + - neuroscience + - neurobiology + - neuroimaging + - neurophysiology + - electrophysiology + - machine learning + - magnetic resonance imaging + - json + - hdf5 + - zarr + - localization + - brain images + - life sciences + - signal processing + - speech processing + - activity recognition + - activity detection + - analytics + - bioinformatics + - brain models + - cloud computing + - computer vision + - deep learning + - GPS + - h5 + - Homo sapiens + - Mus musculus + - non-human primate + - aws-pds +License: Creative Commons 4.0 International (CC BY 4.0) +Resources: + - Description: Time series neurophysiology and behavioral data from animal and human (deidentified) + ARN: arn:aws:s3:::ember-open-data + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Publications: + - Title: Mapping the landscape of social behavior + URL: https://pubmed.ncbi.nlm.nih.gov/40043703/ + AuthorName: Ugne Klibaite, Tianqing Li, Diego Aldarondo, Jumana F Akoad, Bence P Ölveczky, Timothy W Dunn. diff --git a/datasets/emearth.yaml b/datasets/emearth.yaml index 50221efca..270a51294 100644 --- a/datasets/emearth.yaml +++ b/datasets/emearth.yaml @@ -4,6 +4,10 @@ Documentation: https://doi.org/10.20383/102.0547 Contact: shervan.gharari@usask.ca ManagedBy: "[Computational Hydrology at the University of Saskatchewan](https://uofs-comphyd.github.io/)" UpdateFrequency: N/A +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - atmosphere diff --git a/datasets/emory-breast-imaging-dataset-embed.yaml b/datasets/emory-breast-imaging-dataset-embed.yaml index 157a8656b..a65cbc78b 100644 --- a/datasets/emory-breast-imaging-dataset-embed.yaml +++ b/datasets/emory-breast-imaging-dataset-embed.yaml @@ -27,11 +27,11 @@ Resources: ARN: arn:aws:s3:::embed-dataset-open Region: us-west-2 Type: S3 Bucket - ControlledAccess: https://forms.gle/HwGMM6vdv3w32TKF9 + ControlledAccess: https://forms.gle/6YVFKTz7ucEJKEWw8 DataAtWork: Tutorials: - - Title: Sample Notebook - URL: https://github.com/Emory-HITI/EMBED_Open_Data/blob/main/Sample_Notebook.ipynb + - Title: Screening Label Assignment Example + URL: https://github.com/Emory-HITI/EMBED_Open_Data/blob/43e76483284a87b07d33982fc673082b5e2d41c9/resources/notebooks/screening_label_assignment.ipynb AuthorName: Emory-HITI Publications: - Title: "The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4M Screening and Diagnostic Mammograms" diff --git a/datasets/enhance-pet-1-6k.yaml b/datasets/enhance-pet-1-6k.yaml new file mode 100644 index 000000000..18f6d5fb6 --- /dev/null +++ b/datasets/enhance-pet-1-6k.yaml @@ -0,0 +1,69 @@ +Name: ENHANCE.PET 1.6k - Whole-/Total-Body [18F]FDG-PET/CT with CT-Derived Segmentations +Description: > + Open, multi-center dataset of 1,597 whole-/total-body FDG-PET/CT studies with + 130 CT-derived, expert-verified anatomical segmentations per scan (~250 GB). + Provided as anonymized NIfTI (PET, CT, labels) with spreadsheet metadata. + Designed for segmentation benchmarking, multi-organ analysis, radiomics, and PET/CT AI research. + +Documentation: https://github.com/ENHANCE-PET/MOOSE/blob/main/DATA_CARD.md +Contact: Lalith.shiyam@med.uni-muenchen.de +ManagedBy: ENHANCE.PET initiative (LMU Klinikum & partners) +UpdateFrequency: Ad hoc (new releases aligned with additional cohort availability) + +Tags: + - medical imaging + - segmentation + - nifti + - cancer + - radiology + - life sciences + +License: > + Dataset licensing per originating site: + - AutoPET Challenge: CC BY-NC 4.0 (non-commercial use) + - University Hospital Leipzig: CC BY 4.0 + - Azienda Ospedaliero Universitaria Careggi: CC BY 4.0 + Software (MOOSE): Apache-2.0. + +Citation: > + Ferrara D. et al. (2025). Sharing a whole-/total-body [18F]FDG-PET/CT dataset with + CT-derived segmentations - an ENHANCE.PET initiative. https://doi.org/10.21203/rs.3.rs-7169062/v2 + +Resources: + - Description: ENHANCE.PET 1.6k public S3 bucket + ARN: arn:aws:s3:::enhance-pet-1-6k + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://enhance-pet-1-6k.s3.us-west-2.amazonaws.com/)' + + - Description: SNS topic for ENHANCE.PET 1.6k S3 object creation events + ARN: arn:aws:sns:us-west-2:602670427264:enhance-pet-1-6k-object_created + Region: us-west-2 + Type: SNS Topic + +DataAtWork: + Tutorials: + - Title: Dataset Organization & AWS Access (MOOSE CLI) + URL: https://github.com/ENHANCE-PET/MOOSE/blob/main/DATA_CARD.md + AuthorName: ENHANCE.PET Team + AuthorURL: https://enhance.pet/ + + Tools & Applications: + - Title: MOOSE (Multi-organ objective segmentation tool) + URL: https://github.com/ENHANCE-PET/MOOSE + AuthorName: ENHANCE.PET (QIMP Team) + AuthorURL: https://enhance.pet/ + + Publications: + - Title: Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations - an ENHANCE.PET initiative + URL: https://doi.org/10.21203/rs.3.rs-7169062/v2 + AuthorName: Ferrara, D.; Pires, M.; Gutschmayer, S.; Yu, J.; Abdelhafez, Y. G.; Abenavoli, E.; Badawi, R. D.; + Chaudhari, A. J.; Chen, M. S.; Cherry, S. R.; Frille, A.; Geist, B. K.; Grüenert, S.; Hacker, M.; + Hesse, S.; Kerkhoff, T.; Linder, P.; Pappisch, J.; Pusitz, S.; Raslan, O. A.; Rausch, I.; + Raychaudhuri, S. P.; Sabri, O.; Schmidt, F.; Sciagrà, R.; Spencer, B.; Wang, G.; Wirtz, H.; + Beyer, T.; Sundar, L. K. S. + AuthorURL: https://orcid.org/0000-0002-8711-8081 + +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/eot-web-archive.yaml b/datasets/eot-web-archive.yaml index 278bc6d0c..8316946b7 100644 --- a/datasets/eot-web-archive.yaml +++ b/datasets/eot-web-archive.yaml @@ -2,8 +2,8 @@ Name: End of Term Web Archive Dataset Description: > The End of Term Web Archive (EOT) captures and saves U.S. Government websites at the end of presidential administrations. The EOT has - thus far preserved websites from administration changes in 2008, 2012, 2016, - and 2020. Data from these web crawls have been made openly available in + thus far preserved websites from administration changes in 2008, 2012, 2016, 2020 + and 2024. Data from these web crawls have been made openly available in several formats in this dataset. Documentation: https://eotarchive.org/data/ Contact: Mark Phillips , Sawood Alam diff --git a/datasets/epa-2022-modeling-platform.yaml b/datasets/epa-2022-modeling-platform.yaml index a876a858e..19df61a51 100644 --- a/datasets/epa-2022-modeling-platform.yaml +++ b/datasets/epa-2022-modeling-platform.yaml @@ -1,97 +1,102 @@ -Name: >- - OAQPS 2022 Modeling Platform -Description: >- - The data are part of the 2022 Modeling Platform used to support regulatory actions - and technical analyses conducted by the EPA's Office of Air Quality Planning and - Standards. Specifically, this data includes Weather Research and Forecasting Model (v4.4.2) - conducted at a 12-km resolution over the Continental United States (12US). MCIP-processed - files and wrfcamx-processed (12US1 domain) are also available as part of this dataset - to assist in the use of emissions processing and photochemical modeling. These files - may be used in downstream applications to generate emissions, photochemical - modeling, or dispersion modeling inputs. Additionally, lateral boundary condition files - generated using GEOS-CF at 36-km with results translated from GEOS-Chem species available - in GEOS-CF to CMAQ cb6/ae7. Simulations for boundary conditions covering the northern - hemisphere are also provided. 12US2 lateral boundary condition files are also generated based - on 36US3 CMAQ model run outputs. These simulations were conducted using CMAQ v5.4 and GEOS-Chem - v14.0.1. 2022v1 CMAQ-ready emissions are provided for a 36km grid over North America (36US3) - and two 12km grids (12SU1 and 12US2). In addition, 2022v1 CAMx-ready emissions are provided - for a 12km grid over North America (12US2). See the documentation for pictures of the grids. - The types of emissions data provided include point sources, nonpoint sources, mobiles sources, - fires, lightning NOx, and biogenic emissions. Input files for computing biogenic emissions, - lightning NOx emissions and bi-directional deposition inline within CMAQ are also provided. - Ozone column and photolysis rate input files for CAMx model run are also provided. The related - CMAQ and CAMx run scripts are also available now. One day sample outputs for CMAQ and CAMx - on the 12US2 domain are also now available. README text files are included at multiple - levels within the directory structure to explain files at that level. For more information - about the emissions, see the below documentation or the 2022v1 web page: - https://www.epa.gov/air-emissions-modeling/2022v1-emissions-modeling-platform -Documentation: >- - 2022 WRF Modeling TSD: - https://bit.ly/2022WRF - - 2022 Emissions Base Case: - https://bit.ly/2022Emissions - - 2022 v1 36US3 model performance: - https://bit.ly/36US3_2022 - -Contact: Misenis.Chris@epa.gov -ManagedBy: - U.S. Environmental Protection Agency (https://www.epa.gov) -UpdateFrequency: As needed -Tags: - - aws-pds - - air quality - - regulatory - - weather - - meteorological -License: >- - These datasets are products of the U.S. Government and are intended for public - access and use. Unless otherwise specified, all data produced by the U.S EPA - is, by default, in the public domain and are not subject to domestic copyright - protection under 17 U.S.C. § 105. More details on the U.S. Public Domain - license are available here: http://www.usa.gov/publicdomain/label/1.0/ -Citation: >- - WRF Modeling: - US EPA, 2024, "Meteorological Model Performance for Annual 2022 Simulation - WRF v4.4.2" - Emissions Modeling: - US EPA, 2024, "Documentation of 2022 Base Year Emissions Released August 2024" -Resources: - - Description: >- - The 2022 WRF output are stored as uncompressed netcdf/hdf5 formatted files in - the /WRF directory. The 2022 MCIP output are stored as uncompressed netcdf/hdf5 - formatted files in IOAPI format in the /MCIP directory. The wrfcamx files are stored - as uncompressed netcdf files in the /wrfcamx directory. Information on the model - projection and grid structure is contained in the header information of the - netcdf file. The netcdf files can be opened and manipulated using software programs - that can read and write netcdf formatted files (e.g. Fortran, R, Python). - The WRF files are daily files containing hourly data beginning at 00UTC through - 23UTC for each modeled day. For more information: https://www2.mmm.ucar.edu/wrf/users/ - The MCIP files are daily files with multiple files for each day. For more information - about what each MCIP file contains, please see the following GitHub entry: - https://github.com/USEPA/CMAQ/blob/main/PREP/mcip/README.md For more information about - what each wrfcamx file contains, please see the README file in the wrfcamx source - code file available from Ramboll at: - https://www.camx.com/getmedia/wrfcamx_v5.2.10Jan22.tgz - The 2022v1 emissions data are stored as uncompressed netcdf files in the /emis directory. - Year 2022 CMAQ-ready emissions are provided under the folder emis/2022hc_cb6_22m. - Year 2022 CAMx-ready emissions are provided under the folder emis/CAMx. - The 2022v1 12US2 boundary conditions are stored as uncompressed netecdf files in - bcon/12US2_CMAQ_BCON and bcon/ HEMI_CMAQ_12US2_CAMxBC. The 2022v1 12US2 and 36US3 - EPIC data are stored as uncompressed netecdf files in CMAQ_ancillary_inputs/EPIC. - The 2022v1 12US2 and 36US3 lightning data are stored as uncompressed netecdf - files in CMAQ_ancillary_inputs/Lightning_data. The 2022v1 12US2 and 36US3 ozone - column data as uncompressed txt files in CAMx_ancillary_inputs/ozone_col. The 2022v1 - 12US2 and 36US3 photolysis rate data are stored as uncompressed files in - CAMx_ancillary_inputs/photolysis_rate. The 2022v1 12US2 and 36US3 model run - scripts are stored in Model_jobs/. - ARN: 'arn:aws:s3:::epa-2022-modeling-platform' - Region: us-east-1 - Type: S3 Bucket - Explore: - - '[Browse Bucket](https://epa-2022-modeling-platform.s3.amazonaws.com/index.html)' - - Description: Notification for the 2022 Modeling Platform bucket - ARN: 'arn:aws:sns:us-east-1:127085394039:epa-2022-modeling-platform-object_created' - Region: us-east-1 - Type: SNS Topic \ No newline at end of file +Name: >- + OSAP 2022 Modeling Platform +Description: >- + The data are part of the 2022 Modeling Platform used to support regulatory actions + and technical analyses conducted by the EPA's Office of State Air Partnerships (OSAP). + Specifically, this data includes Weather Research and Forecasting Model (v4.4.2) + conducted at a 12-km resolution over the Continental United States (12US). MCIP-processed + files and wrfcamx-processed (12US1 domain) are also available as part of this dataset + to assist in the use of emissions processing and photochemical modeling. These files + may be used in downstream applications to generate emissions, photochemical + modeling, or dispersion modeling inputs. Additionally, lateral boundary condition files + generated using GEOS-CF at 36-km with results translated from GEOS-Chem species available + in GEOS-CF to CMAQ cb6/ae7. Simulations for boundary conditions covering the northern + hemisphere are also provided. 12US2 lateral boundary condition files are also generated based + on 36US3 CMAQ model run outputs. These simulations were conducted using CMAQ v5.4 and GEOS-Chem + v14.0.1. 2022v1 CMAQ-ready emissions are provided for a 36km grid over North America (36US3) + and two 12km grids (12SU1 and 12US2). In addition, 2022v1 CAMx-ready emissions are provided + for a 12km grid over North America (12US2). See the documentation for pictures of the grids. + The types of emissions data provided include point sources, nonpoint sources, mobiles sources, + fires, lightning NOx, and biogenic emissions. Input files for computing biogenic emissions, + lightning NOx emissions and bi-directional deposition inline within CMAQ are also provided. + Ozone column and photolysis rate input files for CAMx model run are also provided. The related + CMAQ and CAMx run scripts are also available now. One day sample outputs for CMAQ and CAMx + on the 12US2 domain are also now available. README text files are included at multiple + levels within the directory structure to explain files at that level. For more information + about the emissions, see the below documentation or the 2022v1 web page: + https://www.epa.gov/air-emissions-modeling/2022v1-emissions-modeling-platform +Documentation: >- + 2022 WRF Modeling TSD: + https://bit.ly/2022WRF + + 2022 Emissions Base Case: + https://bit.ly/2022Emissions + + 2022 v1 36US3 model performance: + https://bit.ly/36US3_2022 + +Contact: Misenis.Chris@epa.gov +ManagedBy: + U.S. Environmental Protection Agency (https://www.epa.gov) +UpdateFrequency: As needed +Collabs: + ASDI: + Tags: + - climate +Tags: + - aws-pds + - air quality + - regulatory + - weather + - meteorological + - environmental +License: >- + These datasets are products of the U.S. Government and are intended for public + access and use. Unless otherwise specified, all data produced by the U.S EPA + is, by default, in the public domain and are not subject to domestic copyright + protection under 17 U.S.C. § 105. More details on the U.S. Public Domain + license are available here: http://www.usa.gov/publicdomain/label/1.0/ +Citation: >- + WRF Modeling: + US EPA, 2024, "Meteorological Model Performance for Annual 2022 Simulation + WRF v4.4.2" + Emissions Modeling: + US EPA, 2024, "Documentation of 2022 Base Year Emissions Released August 2024" +Resources: + - Description: >- + The 2022 WRF output are stored as uncompressed netcdf/hdf5 formatted files in + the /WRF directory. The 2022 MCIP output are stored as uncompressed netcdf/hdf5 + formatted files in IOAPI format in the /MCIP directory. The wrfcamx files are stored + as uncompressed netcdf files in the /wrfcamx directory. Information on the model + projection and grid structure is contained in the header information of the + netcdf file. The netcdf files can be opened and manipulated using software programs + that can read and write netcdf formatted files (e.g. Fortran, R, Python). + The WRF files are daily files containing hourly data beginning at 00UTC through + 23UTC for each modeled day. For more information: https://www2.mmm.ucar.edu/wrf/users/ + The MCIP files are daily files with multiple files for each day. For more information + about what each MCIP file contains, please see the following GitHub entry: + https://github.com/USEPA/CMAQ/blob/main/PREP/mcip/README.md For more information about + what each wrfcamx file contains, please see the README file in the wrfcamx source + code file available from Ramboll at: + https://www.camx.com/getmedia/wrfcamx_v5.2.10Jan22.tgz + The 2022v1 emissions data are stored as uncompressed netcdf files in the /emis directory. + Year 2022 CMAQ-ready emissions are provided under the folder emis/2022hc_cb6_22m. + Year 2022 CAMx-ready emissions are provided under the folder emis/CAMx. + The 2022v1 12US2 boundary conditions are stored as uncompressed netecdf files in + bcon/12US2_CMAQ_BCON and bcon/ HEMI_CMAQ_12US2_CAMxBC. The 2022v1 12US2 and 36US3 + EPIC data are stored as uncompressed netecdf files in CMAQ_ancillary_inputs/EPIC. + The 2022v1 12US2 and 36US3 lightning data are stored as uncompressed netecdf + files in CMAQ_ancillary_inputs/Lightning_data. The 2022v1 12US2 and 36US3 ozone + column data as uncompressed txt files in CAMx_ancillary_inputs/ozone_col. The 2022v1 + 12US2 and 36US3 photolysis rate data are stored as uncompressed files in + CAMx_ancillary_inputs/photolysis_rate. The 2022v1 12US2 and 36US3 model run + scripts are stored in Model_jobs/. + ARN: 'arn:aws:s3:::epa-2022-modeling-platform' + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://epa-2022-modeling-platform.s3.amazonaws.com/index.html)' + - Description: Notification for the 2022 Modeling Platform bucket + ARN: 'arn:aws:sns:us-east-1:127085394039:epa-2022-modeling-platform-object_created' + Region: us-east-1 + Type: SNS Topic diff --git a/datasets/epa-edde-v1.yaml b/datasets/epa-edde-v1.yaml index 8adb56808..236475d52 100644 --- a/datasets/epa-edde-v1.yaml +++ b/datasets/epa-edde-v1.yaml @@ -37,6 +37,10 @@ Contact: >- ManagedBy: >- U.S. Environmental Protection Agency (https://www.epa.gov) UpdateFrequency: Quarterly +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - weather diff --git a/datasets/epa-edde-v2.yaml b/datasets/epa-edde-v2.yaml index 859af3df3..9d30adba3 100644 --- a/datasets/epa-edde-v2.yaml +++ b/datasets/epa-edde-v2.yaml @@ -36,6 +36,10 @@ Contact: >- ManagedBy: >- U.S. Environmental Protection Agency (https://www.epa.gov) UpdateFrequency: Quarterly +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - weather diff --git a/datasets/epa-equates-v1.yaml b/datasets/epa-equates-v1.yaml index 56e8dec34..ef4160ab2 100644 --- a/datasets/epa-equates-v1.yaml +++ b/datasets/epa-equates-v1.yaml @@ -1,63 +1,67 @@ -Name: >- - Community Multiscale Air Quality (CMAQ) 2019 3D Gridded and Column data from - the EPA's Air Quality Time Series (EQUATES) Project -Description: >- - The data are part of EPA’s Air Quality Time Series (EQUATES) Project. The - data consist of hourly gridded pollutant concentrations estimates by the - Community Multiscale Air Quality (CMAQ) model version 5.3.2 - (https://doi.org/10.15139/S3/F2KJSK) for January 1 – December 31, 2019. Model - data is provided for two spatial domains : the Northern Hemisphere (108 km x - 108km horizontal grid spacing) and the Contiguous United States including - parts of Canada and Mexico (12km x 12km horizontal grid spacing). Two types - of hourly data are provided: three-dimensional air pollutant concentrations - and vertical column pollutant totals. Previous studies have used this type of - CMAQ 3D and vertical column air quality data to evaluate the modeling system, - created model-observed ‘fused’ surfaces, and to analyze spatial and temporal - changes in air quality in the upper atmosphere, e.g., - https://doi.org/10.1016/j.envint.2019.104909; - https://doi.org/10.5194/acp-17-12449-2017; - https://doi.org/10.1029/2006JD008085; - https://doi.org/10.5194/acp-15-9997-2015. -Documentation: >- - EQUATES data DOI: https://doi.org/10.15139/S3/F2KJSK. Please see the Data Use - Statement if you plan to use this data for your own research. Additional - information may be found on the EQUATES home page (www.epa.gov/cmaq/equates). - For questions or issues please use this User Support Forum: - https://forum.cmascenter.org/t/about-the-equates-category/2723 -Contact: CMAQ_Team@epa.gov -ManagedBy: >- - U.S. Environmental Protection Agency (https://www.epa.gov) -UpdateFrequency: Annual -Tags: - - aws-pds - - air quality - - atmosphere - - model -License: >- - These datasets are products of the U.S. Government and are intended for public - access and use. Unless otherwise specified, all data produced by the U.S EPA - is, by default, in the public domain and are not subject to domestic copyright - protection under 17 U.S.C. § 105. More details on the U.S. Public Domain - license are available here: http://www.usa.gov/publicdomain/label/1.0/ -Citation: >- - US EPA, 2021, "EQUATESv1.0: Emissions, WRF/MCIP, CMAQv5.3.2 Data -- 2002-2019 - US_12km and NHEMI_108km", https://doi.org/10.15139/S3/F2KJSK, UNC Dataverse, - V5 -Resources: - - Description: >- - The 2019 CMAQ output are stored as compressed netcdf/hdf5 formatted files - using I/O API data structures (https://www.cmascenter.org/ioapi/). - Information on the model projection and grid structure is contained in the - header information of the netcdf file. The netcdf files can be opened and - manipulated using I/O API utilities (e.g. M3XTRACT, M3WNDW) or other - software programs that can read and write netcdf formatted files (e.g. - Fortran, R, Python). - ARN: 'arn:aws:s3:::epa-equates-v1' - Region: us-east-1 - Type: S3 Bucket - Explore: - - '[Browse Bucket](https://epa-equates-v1.s3.amazonaws.com/index.html)' - - Description: Notifications for EQUATES bucket - ARN: 'arn:aws:sns:us-east-1:127085394039:epa-equates-v1-object_created' - Region: us-east-1 +Name: >- + Community Multiscale Air Quality (CMAQ) 2019 3D Gridded and Column data from + the EPA's Air Quality Time Series (EQUATES) Project +Description: >- + The data are part of EPA’s Air Quality Time Series (EQUATES) Project. The + data consist of hourly gridded pollutant concentrations estimates by the + Community Multiscale Air Quality (CMAQ) model version 5.3.2 + (https://doi.org/10.15139/S3/F2KJSK) for January 1 – December 31, 2019. Model + data is provided for two spatial domains : the Northern Hemisphere (108 km x + 108km horizontal grid spacing) and the Contiguous United States including + parts of Canada and Mexico (12km x 12km horizontal grid spacing). Two types + of hourly data are provided: three-dimensional air pollutant concentrations + and vertical column pollutant totals. Previous studies have used this type of + CMAQ 3D and vertical column air quality data to evaluate the modeling system, + created model-observed ‘fused’ surfaces, and to analyze spatial and temporal + changes in air quality in the upper atmosphere, e.g., + https://doi.org/10.1016/j.envint.2019.104909; + https://doi.org/10.5194/acp-17-12449-2017; + https://doi.org/10.1029/2006JD008085; + https://doi.org/10.5194/acp-15-9997-2015. +Documentation: >- + EQUATES data DOI: https://doi.org/10.15139/S3/F2KJSK. Please see the Data Use + Statement if you plan to use this data for your own research. Additional + information may be found on the EQUATES home page (www.epa.gov/cmaq/equates). + For questions or issues please use this User Support Forum: + https://forum.cmascenter.org/t/about-the-equates-category/2723 +Contact: CMAQ_Team@epa.gov +ManagedBy: >- + U.S. Environmental Protection Agency (https://www.epa.gov) +UpdateFrequency: Annual +Collabs: + ASDI: + Tags: + - climate +Tags: + - aws-pds + - air quality + - atmosphere + - model +License: >- + These datasets are products of the U.S. Government and are intended for public + access and use. Unless otherwise specified, all data produced by the U.S EPA + is, by default, in the public domain and are not subject to domestic copyright + protection under 17 U.S.C. § 105. More details on the U.S. Public Domain + license are available here: http://www.usa.gov/publicdomain/label/1.0/ +Citation: >- + US EPA, 2021, "EQUATESv1.0: Emissions, WRF/MCIP, CMAQv5.3.2 Data -- 2002-2019 + US_12km and NHEMI_108km", https://doi.org/10.15139/S3/F2KJSK, UNC Dataverse, + V5 +Resources: + - Description: >- + The 2019 CMAQ output are stored as compressed netcdf/hdf5 formatted files + using I/O API data structures (https://www.cmascenter.org/ioapi/). + Information on the model projection and grid structure is contained in the + header information of the netcdf file. The netcdf files can be opened and + manipulated using I/O API utilities (e.g. M3XTRACT, M3WNDW) or other + software programs that can read and write netcdf formatted files (e.g. + Fortran, R, Python). + ARN: 'arn:aws:s3:::epa-equates-v1' + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://epa-equates-v1.s3.amazonaws.com/index.html)' + - Description: Notifications for EQUATES bucket + ARN: 'arn:aws:sns:us-east-1:127085394039:epa-equates-v1-object_created' + Region: us-east-1 Type: SNS Topic \ No newline at end of file diff --git a/datasets/epa-hourly-prognostic-meteorology.yaml b/datasets/epa-hourly-prognostic-meteorology.yaml new file mode 100644 index 000000000..9a5f10843 --- /dev/null +++ b/datasets/epa-hourly-prognostic-meteorology.yaml @@ -0,0 +1,55 @@ +Name: >- + EPA Hourly Prognostic Meteorological Data +Description: >- + The data are hourly outputs from the Weather Research and Forecasting (WRF) model + generated by the EPA's Office of State Air Partnerships (OSAP), Air Quality + Assessment Division, Air Quality Modeling Branch. These data were generated at a 12-km + resolution over the Continental United States (12US), beginning for the year 2021 and + continuing annually through 2023. These files are intended for use in a broad range of + air quality applications, but specifically may be used in dispersion modeling applications + that would benefit from the use of the Mesoscale Model Interface (MMIF) tool + (https://www.epa.gov/scram/air-quality-dispersion-modeling-related-model-support-programs#mmif) + which translates prognostic meteorological data into formats suitable for use with AERMOD, + CALPUFF, or SCICHEM. The individual files are less than 1GB in size, which allows for + the use of the MMIF tool in a Windows environment. These data are anticipated to be updated + annually so the 3 most-recent years are available for use. Additionally, model-observation + paired files are included to aid in the performance evaluation that is necessary for use + of these data in regulatory applications per Appendix W to 40 CFR Part 51. +Documentation: >- + 2022 WRF Modeling TSD: + https://bit.ly/2022WRF +Contact: Misenis.Chris@epa.gov +ManagedBy: U.S. Environmental Protection Agency (https://www.epa.gov) +UpdateFrequency: Annually +Tags: + - aws-pds + - air quality + - regulatory + - weather + - meteorological + - environmental +License: >- + These datasets are products of the U.S. Government and are intended for public + access and use. Unless otherwise specified, all data produced by the U.S EPA + is, by default, in the public domain and are not subject to domestic copyright + protection under 17 U.S.C. § 105. More details on the U.S. Public Domain + license are available here: http://www.usa.gov/publicdomain/label/1.0/ +Citation: >- + WRF Modeling: + US EPA, 2024, "Meteorological Model Performance for Annual 2022 Simulation + WRF v4.4.2" +Resources: + - Description: >- + The WRF output are stored as uncompressed netcdf/hdf5 formatted files in + directories corresponding to the specific years of interest. The model-obs + paired files are stored as comma-delimited files in the year-specific + directories. + ARN: 'arn:aws:s3:::epa-hourly-prognostic-meteorology' + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://epa-hourly-prognostic-meteorology.s3.amazonaws.com/index.html)' + - Description: Notification for the EPA Hourly Prognostic Meteorological Data bucket + ARN: 'arn:aws:sns:us-east-1:127085394039:epa-hourly-prognostic-meteorology-object_created' + Region: us-east-1 + Type: SNS Topic \ No newline at end of file diff --git a/datasets/era5-for-wrf.yaml b/datasets/era5-for-wrf.yaml index 6747b5bcc..11bfe3eb9 100644 --- a/datasets/era5-for-wrf.yaml +++ b/datasets/era5-for-wrf.yaml @@ -1,26 +1,30 @@ -Name: ERA5-for-WRF Open Data on AWS -Description: ERA5 reanalysis data on AWS, preprocessed for use with the Weather Research and Forecasting (WRF) model. -Documentation: https://github.com/moptis/era5-for-wrf/ -Contact: info@veer.eco -ManagedBy: "[Veer Renewables](http://www.veer.eco/)" -UpdateFrequency: Monthly. -Tags: - - aws-pds - - weather - - sustainability - - atmosphere - - electricity - - meteorological - - model -License: CC BY-SA 4.0 -Resources: - - Description: ERA5-for-WRF Data - ARN: arn:aws:s3:::era5-for-wrf - Region: us-east-1 - Type: S3 Bucket -DataAtWork: - Tutorials: - - Title: ERA5-for-WRF Tutorials - URL: https://github.com/moptis/era5-for-wrf/ - AuthorName: Veer Renewables - AuthorURL: https://veer.eco +Name: ERA5-for-WRF Open Data on AWS +Description: ERA5 reanalysis data on AWS, preprocessed for use with the Weather Research and Forecasting (WRF) model. +Documentation: https://github.com/moptis/era5-for-wrf/ +Contact: info@veer.eco +ManagedBy: "[Veer Renewables](http://www.veer.eco/)" +UpdateFrequency: Monthly. +Collabs: + ASDI: + Tags: + - climate +Tags: + - aws-pds + - weather + - sustainability + - atmosphere + - electricity + - meteorological + - model +License: CC BY-SA 4.0 +Resources: + - Description: ERA5-for-WRF Data + ARN: arn:aws:s3:::era5-for-wrf + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: ERA5-for-WRF Tutorials + URL: https://github.com/moptis/era5-for-wrf/ + AuthorName: Veer Renewables + AuthorURL: https://veer.eco diff --git a/datasets/flab.yaml b/datasets/flab.yaml new file mode 100644 index 000000000..ef6a70ac5 --- /dev/null +++ b/datasets/flab.yaml @@ -0,0 +1,32 @@ +Name: "FLAb: Fitness Landscapes for Antibodies" +Description: FLAb is the largest publicly available therapeutic antibody dataset designed to train and benchmark protein AI models. It provides open-access, high-quality developability data on diverse therapeutic properties, including expression, thermostability, immunogenicity, aggregation, polyreactivity, binding affinity, and pharmacokinetics. +Documentation: https://github.com/Graylab/FLAb/blob/main/README.md +Contact: mchungy1@jhu.edu +ManagedBy: "[Jeffrey Gray Lab, Johns Hopkins University](https://graylab.jhu.edu/)" +UpdateFrequency: Any new public release of antibody developabilty data is deposited into FLAb +Tags: + - protein + - protein template + - machine learning + - life sciences + - aws-pds +License: https://creativecommons.org/licenses/by/4.0/ +Citation: "FLAb was accessed on [DATE] at registry.opendata.aws/flab" +Resources: + - Description: Antibody developabiltiy data in CSV format + ARN: arn:aws:s3:::graylab-flab + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: "FLAb tutorial: Benchmarking a protein language model for antibody expression prediction" + URL: https://github.com/Graylab/FLAb/blob/main/examples/FLAb_ZeroShotExample_IgLM_Expression.ipynb + AuthorName: Michael Chungyoun + AuthorURL: https://www.linkedin.com/in/mfc12/ + Publications: + - Title: "FLAb: Benchmarking deep learning methods for antibody fitness prediction" + URL: https://doi.org/10.1101/2024.01.13.575504 + AuthorName: Michael Chungyoun and Jeffrey J. Gray + AuthorURL: https://www.linkedin.com/in/mfc12/ +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/ford-multi-av-seasonal.yaml b/datasets/ford-multi-av-seasonal.yaml index 8b5c95953..c067a9bc9 100644 --- a/datasets/ford-multi-av-seasonal.yaml +++ b/datasets/ford-multi-av-seasonal.yaml @@ -4,6 +4,10 @@ Contact: avdata.ford.com Documentation: avdata.ford.com ManagedBy: "[Ford Motor Company](https://avdata.ford.com)" UpdateFrequency: New data will be added until the entire dataset is released online. +Collabs: + ASDI: + Tags: + - infrastructure Tags: - autonomous vehicles - computer vision diff --git a/datasets/frag-struc.yaml b/datasets/frag-struc.yaml new file mode 100644 index 000000000..b0e84118a --- /dev/null +++ b/datasets/frag-struc.yaml @@ -0,0 +1,32 @@ +Name: RNA structure by fragmentation frequency +Description: "The fragSTRUC project devises a software to extract RNA secondary structure information from Illumina datasets, based on divalent ions in standard RNA-seq library preparation fragmenting sequences at non-base-paired regions of RNA." +Documentation: https://github.com/yuukiiwa/RNA_structure_by_fragmentation_frequency +Contact: "[fragSTRUC team](https://github.com/yuukiiwa/RNA_structure_by_fragmentation_frequency)" +ManagedBy: "The Genome Institute of Singapore (https://www.a-star.edu.sg/gis) and UMass Chan Medical School's RNA Therapeutics Institute (https://www.umassmed.edu/rti/)" +UpdateFrequency: Datasets will be updated periodically as additional data is generated. +Tags: + - genomic + - transcriptomics + - life sciences + - bioinformatics + - aws-pds +License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)" +Citation: "In addition, please cite Yuk Kei Wan and Leonard Schärfen Hidden structural information in RNA sequencing data." +Resources: + - Description: RNA structure by fragmentation frequency + ARN: arn:aws:s3:::frag-struc + Region: ap-southeast-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](http://frag-struc.s3-website-ap-southeast-1.amazonaws.com/)' +DataAtWork: + Tutorials: + - Title: Accessing the fragSTRUC dataset on AWS + URL: https://github.com/yuukiiwa/RNA_structure_by_fragmentation_frequency/blob/main/README.md + AuthorName: Yuk Kei Wan and Leonard Schärfen + Tools & Applications: + - Title: "fragSTRUC: RNA structure by fragmentation frequency" + URL: https://github.com/lschaerfen/fragstruc + AuthorName: Yuk Kei Wan and Leonard Schärfen +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/fvcom_gom3.yaml b/datasets/fvcom_gom3.yaml new file mode 100644 index 000000000..4ee360b59 --- /dev/null +++ b/datasets/fvcom_gom3.yaml @@ -0,0 +1,28 @@ +Name: UMASSD-FVCOM-GOM3-Hindcast +Description: The Finite Volume Community Ocean Model (FVCOM) was used to simulate ocean water levels, velocity, temperature and salinity over a multi-decadal period (1984-present) in the waters of the Northeast US including the Gulf of Maine. The model was configured and run by the Dr. Changshen Chen, Director of the Marine Ecosystems Dynamics Modeling Laboratory in the School for Marine Science & Technology at the University of Massachusetts Dartmouth. The triangular mesh has a varying horizontal resolution from several hundred meters inshore to several kilometers offshore, and 45 terrain-following vertical layers. The model output was saved at hourly intervals from 2009-08-21 to 2022-06-17. +Documentation: https://en.wikipedia.org/wiki/Finite_Volume_Community_Ocean_Model +Contact: rich@opensciencecomputing.com +ManagedBy: Open Science Computing, LLC +UpdateFrequency: None +Citation: https://web.archive.org/web/20161229211546id_/http://fvcom.smast.umassd.edu/wp-content/uploads/2013/11/MITSG_12-25.pdf +Tags: + - aws-pds + - oceans +License: CC0 +Resources: + - Description: A collection of NetCDF files, kerchunk-generated Parquet reference files, and an Intake catalog + ARN: arn:aws:s3:::fvcom-gom3 + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: FVCOM Explorer Notebook + URL: https://github.com/opensciencecomputing/fvcom + NotebookURL: https://github.com/opensciencecomputing/umassd-fvcom/blob/main/fvcom_gom3_explore.ipynb + AuthorName: Rich Signell + AuthorURL: https://about.me/rich.signell + Services: + Publications: + - Title: An Unstructured Grid, Finite Volume, Three Dimensional, Primitive Equations Ocean Model with Application to Coastal Ocean and Estuaries + URL: https://doi.org/10.1175/1520-0426(2003)020%3C0159:AUGFVT%3E2.0.CO;2 + AuthorName: Changsheng Chen, Hedong Liu, and Robert C. Beardsley diff --git a/datasets/gaia-dr3.yaml b/datasets/gaia-dr3.yaml new file mode 100644 index 000000000..1da9ef993 --- /dev/null +++ b/datasets/gaia-dr3.yaml @@ -0,0 +1,27 @@ + +Name: Gaia DR3 +Description: | + [Gaia DR3 data](https://www.cosmos.esa.int/web/gaia/dr3) were originally released by the European Space Agency in December 2020. This [HATS](https://hats.readthedocs.io/en/stable)-formatted catalog was produced by the LSST Interdisciplinary Network for Collaboration and Computing. The GAIA HATS Datasets are specifically designed for efficient spatial cross-matching with other HATS-format catalogs, whether within the same archive or across distributed archive data centers. This enables astronomers to perform complex analyses, such as identifying correlations or overlaps between datasets from different surveys. Users can leverage [LSDB (Large-Scale Database)](https://docs.lsdb.io/en/latest/), a scalable spatial analysis library, to execute precise, high-performance operations like cone searches or cross-matching. +Documentation: https://docs.lsdb.io/en/latest/index.html +Contact: archive@stsci.edu +ManagedBy: "[Space Telescope Science Institute](http://www.stsci.edu/)" +Citation: Please see [the LSDB citation page](https://docs.lsdb.io/en/latest/citation.html) if using LSDB for an academic publication. Please also [cite the Gaia team](https://gea.esac.esa.int/archive/documentation/GDR3/Miscellaneous/sec_credit_and_citation_instructions/). +UpdateFrequency: Never +Tags: + - astronomy +License: Attribution required. +Resources: + - Description: Gaia DR3 HATS-Formatted Files + ARN: arn:aws:s3:::stpubdata/gaia + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + - Description: Notifications for new data + ARN: arn:aws:sns:us-east-1:879230861493:stpubdata/gaia + Region: us-east-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Dark Energy Survey / Gaia DR3 Crossmatch + URL: https://docs.lsdb.io/en/stable/tutorials/pre_executed/des-gaia.html + AuthorName: LSDB Collaboration \ No newline at end of file diff --git a/datasets/gdr-data-lake.yaml b/datasets/gdr-data-lake.yaml index 47cbca42d..9c0b4c335 100644 --- a/datasets/gdr-data-lake.yaml +++ b/datasets/gdr-data-lake.yaml @@ -10,7 +10,7 @@ Description: | Documentation: https://github.com/openEDI/documentation/ Contact: https://github.com/openEDI/documentation/issues -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: As needed Collabs: ASDI: diff --git a/datasets/geo_tide_geojsons.yaml b/datasets/geo_tide_geojsons.yaml new file mode 100644 index 000000000..8ac1f3751 --- /dev/null +++ b/datasets/geo_tide_geojsons.yaml @@ -0,0 +1,64 @@ +Name: "GeoJSON Files for Geo-TIDE" +Description: "GeoJSON files for the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer" +Documentation: https://github.com/mcsc-impact-climate/Geo-TIDE-datasets +Contact: mcsc@mit.edu +ManagedBy: MIT Climate & Sustainability Consortium +UpdateFrequency: Quarterly +Collabs: + ASDI: + Tags: + - sustainability +Tags: + - aws-pds + - electricity + - energy + - environmental + - geospatial + - supply chain + - sustainability + - transportation +License: Creative Commons Attribution 4.0 International +Citation: "Eamer, D., Borrero, M., Bashir, N., & MIT Climate & Sustainability Consortium. (2025). GeoJSON files for the MCSC's Trucking Industry Decarbonization Explorer (Geo-TIDE) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15851359" +Resources: + - Description: GeoJSON Files for Geo-TIDE + ARN: arn:aws:s3:::mcsc-geotide-geojson-files/geojson_files/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://mcsc-geotide-geojson-files.s3.amazonaws.com/index.html)' +DataAtWork: + Tutorials: + - Title: Geo-TIDE access and getting-started exercises + URL: https://danikam16.wixsite.com/mysite/post/accessing-and-using-the-mcsc-s-interactive-geospatial-decision-support-tool-for-trucking-fleet-decar + AuthorName: Danika Eamer and Helena De Figueiredo Valente + AuthorURL: https://github.com/danikam + Services: + - Amazon S3 + - Title: Which logistics facilities should a return-to-base carrier target for fleet electrification and chargers? + URL: https://docs.google.com/presentation/d/e/2PACX-1vQZccVHZVT1QRNdhCRRI810UxGvCD3hJhxIE4CzBDbhNr9iecHV5lp2Rv87x6rik1wrCiXXUq0WfuBk/pub + AuthorName: Danika Eamer + AuthorURL: https://github.com/danikam + Services: + - Amazon S3 + - Title: Which routes should a dry-van carrier prioritize for investment in battery electric or hydrogen trucks? + URL: https://danikam16.wixsite.com/mysite/post/user-case-studies-for-interactive-geospatial-trucking-fleet-decision-support + AuthorName: Danika Eamer and Helena De Figueiredo Valente + AuthorURL: https://github.com/danikam + Services: + - Amazon S3 + Tools & Applications: + - Title: MCSC Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) + URL: https://climatedata.mit.edu/faf5/transportation + AuthorName: Danika Eamer, Brilant Kasami, Brooke Bao, and MIT Climate & Sustainability Consortium + AuthorURL: https://impactclimate.mit.edu + Publications: + - Title: "Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE): Technical Guide and Methodology" + URL: https://dspace.mit.edu/handle/1721.1/159069 + AuthorName: Eamer, D., Borrero, M., Bao, B., Kasami, B., and De Figueiredo Valente, H. + AuthorURL: https://impactclimate.mit.edu + - Title: Thought Experiment to Explore Potential Savings from Pooled Charging Infrastructure Investment + URL: https://dspace.mit.edu/handle/1721.1/153617 + AuthorName: Eamer, D. and Borrero, M. + AuthorURL: https://impactclimate.mit.edu +ADXCategories: + - Resources Data diff --git a/datasets/geoglows-v2.yaml b/datasets/geoglows-v2.yaml index ef7a793b9..fe536853c 100644 --- a/datasets/geoglows-v2.yaml +++ b/datasets/geoglows-v2.yaml @@ -25,6 +25,10 @@ Documentation: https://training.geoglows.org Contact: https://groups.google.com/g/geoglows ManagedBy: Riley Hales UpdateFrequency: Monthly +Collabs: + ASDI: + Tags: + - oceans Tags: - aws-pds - hydrology diff --git a/datasets/glo-30-hand.yaml b/datasets/glo-30-hand.yaml index b065417af..95458341f 100644 --- a/datasets/glo-30-hand.yaml +++ b/datasets/glo-30-hand.yaml @@ -11,6 +11,10 @@ ManagedBy: "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)" UpdateFrequency: > None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://registry.opendata.aws/copernicus-dem/) dataset is updated. +Collabs: + ASDI: + Tags: + - disaster response Tags: - aws-pds - elevation diff --git a/datasets/global-drought-flood-catalogue.yaml b/datasets/global-drought-flood-catalogue.yaml index bdcf28ece..2c7c53835 100644 --- a/datasets/global-drought-flood-catalogue.yaml +++ b/datasets/global-drought-flood-catalogue.yaml @@ -5,6 +5,10 @@ Documentation: https://prep-next.github.io/data/GDFC/index.html Contact: For any questions regrading dataset, email Professor Xiaogang He at hexg@nus.edu.sg. ManagedBy: "[PREP-NexT Lab](https://github.com/PREP-NexT)" UpdateFrequency: No future updates planned. +Collabs: + ASDI: + Tags: + - disaster response Tags: - aws-pds - floods diff --git a/datasets/gmsdata.yaml b/datasets/gmsdata.yaml index e2cd328a9..c9bfca33d 100644 --- a/datasets/gmsdata.yaml +++ b/datasets/gmsdata.yaml @@ -4,6 +4,10 @@ Documentation: https://github.com/genome/gms/wiki Contact: https://github.com/genome/gms/issues ManagedBy: Genome Institute at the Washington University School of Medicine in St. Louis UpdateFrequency: Not updated +Collabs: + ASDI: + Tags: + - biodiversity Tags: - aws-pds - genetic diff --git a/datasets/gnss-ro-opendata.yaml b/datasets/gnss-ro-opendata.yaml index dded0e1f0..7971306c5 100644 --- a/datasets/gnss-ro-opendata.yaml +++ b/datasets/gnss-ro-opendata.yaml @@ -4,6 +4,10 @@ Documentation: "http://github.com/gnss-ro/aws-opendata" Contact: "Stephen Leroy (sleroy@aer.com)" ManagedBy: "Verisk Atmospheric and Environmental Research, Inc." UpdateFrequency: "The dataset is updated monthly for UCAR and ROM SAF contributions only. The update frequency for the JPL contribution is to be determined." +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - atmosphere diff --git a/datasets/graf-reforecast.yaml b/datasets/graf-reforecast.yaml new file mode 100644 index 000000000..ca21ce183 --- /dev/null +++ b/datasets/graf-reforecast.yaml @@ -0,0 +1,39 @@ +Name: GRAF Reforecast +Description: "A zarr-formatted dataset of 1836 reforecast cases (approx. 5 years) from The Weather Company GRAF (Global high-Resolution Atmospheric Forecasting) model, a version of the National Center for Atmospheric Research (NCAR) Model for Predictions Across Scales (MPAS). GRAF is global, but the configuration for this reforecast had a mesh refinement to approx. 4 km over the US, Caribbean Basin, and Europe, and 15 km elsewhere. This model was designed to run much of its computation on graphical processing units, with this development assisted by NVIDIA. The 1836 cases (approx. 5 years) were generated from ECMWF reanalyses (ERA5) for initial condition dates spanning more than 20 years, 2004-2024. These dates of the chosen initial conditions mostly selected based on high-impact weather in the contiguous US (CONUS) and Caribbean. Sampling in this way spanned a wider range of interesting, high-impact weather scenarios than were there five contiguous years of data. GRAF reforecasts were mostly run to +27 h lead time, assuming a 3-h for spin up followed by a full diurnal cycle. Data were saved in zarr format on the native model vertical coordinate. Most fields were saved at 15-min intervals, though several precipitation variables were saved at 5-min cadence." +Documentation: "[Documentation](https://docs.google.com/forms/d/e/1FAIpQLSejRyG2CXrfcmrX7g_iFhc3RF-n3ZzmPQdVieSDwTzLNkR-_w/viewform)" +Contact: graf.reforecast@weather.com +ManagedBy: "[The Weather Company](https://www.weathercompany.com/)" +UpdateFrequency: One time push only +Collabs: + ASDI: + Tags: + - weather +Tags: + - atmosphere + - forecast + - geoscience + - geospatial + - model + - near-surface air temperature + - near-surface relative humidity + - precipitation + - wind speeds + - cloud amount + - visibility + - ERA5 + - MPAS + - zarr + - weather +License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)" +Resources: + - Description: GRAF Reforecast dataset + ARN: arn:aws:s3:::twc-graf-reforecast + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://s3-us-west-2.amazonaws.com/twc-graf-reforecast/index.html)' +DataAtWork: + Publications: + - Title: Global reforecasts from MPAS “GRAF” with mesh refinement over the US and Europe + URL: https://cesoc.net/wp-content/uploads/2024/08/GRAF-reforecast-Hamill-CESOC24.pdf + AuthorName: Thomas M. Hamill, Raghu Raj Prasanna Kumar, Karthik Kashinath2, Carl Ponder, Mike Pritchard, Tao Ge, Akshay Subramanian, Jaideep Pathak, John Wong, Brett Wilt, Peter Neilley diff --git a/datasets/green_et.yaml b/datasets/green_et.yaml index cb55ed77b..a11576f9e 100644 --- a/datasets/green_et.yaml +++ b/datasets/green_et.yaml @@ -1,32 +1,36 @@ -Name: IWMI DIWASA Green ET for Africa -Description: Green evapotranspiration (Green ET) is the portion of ET derived from green water, which includes soil moisture and rainfall used by vegetation. It represents a key component of green water fluxes in water accounting. Green ET consists of evaporation from soil moisture in non-irrigated areas, transpiration from rainfed crops and natural vegetation, and interception losses from precipitation on vegetation. It plays a crucial role in rainfed agriculture, drought monitoring, and sustainable water management by tracking how rainfall supports plant growth. -Documentation: https://iwmi.africageoportal.com/pages/continent-africa -Contact: iwmiwaplus@gmail.com -ManagedBy: "[IWMI](https://www.iwmi.org/)" -UpdateFrequency: None -Tags: - - soil moisture - - rainfed cropland - - interception loss - - evapotranspiration - - water -License: "Creative commons open license" -Resources: - - Description: Monthly Green ET for Africa - ARN: arn:aws:s3:::iwmi-datasets/Water_accounting_plus/Africa/Rainfall_ET_M/ - Region: af-south-1 - Type: S3 Bucket - Explore: - - '[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)' - -DataAtWork: - Tutorials: - - Title: Analysis of IWMI’s Water Data Products through Digital Earth Africa - URL: https://learn.digitalearthafrica.org/courses/course-v1:IWMI+DIWASA1+2024_10/about - AuthorName: A.T. Haile, E.T. Negash, K. Mubea, M. Tadesse - AuthorURL: https://github.com/iwmiwaplus - Tools & Applications: - - Title: Multi-Scale Water Accounting in the Volta Basin - URL: https://public.tableau.com/app/profile/iwmi.wa/viz/Voltabasinvertical/Merged?publish=yes - AuthorName: iwmiwaplus - AuthorURL: https://public.tableau.com/app/profile/iwmi.wa +Name: IWMI DIWASA Green ET for Africa +Description: Green evapotranspiration (Green ET) is the portion of ET derived from green water, which includes soil moisture and rainfall used by vegetation. It represents a key component of green water fluxes in water accounting. Green ET consists of evaporation from soil moisture in non-irrigated areas, transpiration from rainfed crops and natural vegetation, and interception losses from precipitation on vegetation. It plays a crucial role in rainfed agriculture, drought monitoring, and sustainable water management by tracking how rainfall supports plant growth. +Documentation: https://iwmi.africageoportal.com/pages/continent-africa +Contact: iwmiwaplus@gmail.com +ManagedBy: "[IWMI](https://www.iwmi.org/)" +UpdateFrequency: None +Collabs: + ASDI: + Tags: + - agriculture +Tags: + - soil moisture + - rainfed cropland + - interception loss + - evapotranspiration + - water +License: "Creative commons open license" +Resources: + - Description: Monthly Green ET for Africa + ARN: arn:aws:s3:::iwmi-datasets/Water_accounting_plus/Africa/Rainfall_ET_M/ + Region: af-south-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://iwmi-datasets.s3.af-south-1.amazonaws.com/Cropland_partition/index.html)' + +DataAtWork: + Tutorials: + - Title: Analysis of IWMI’s Water Data Products through Digital Earth Africa + URL: https://learn.digitalearthafrica.org/courses/course-v1:IWMI+DIWASA1+2024_10/about + AuthorName: A.T. Haile, E.T. Negash, K. Mubea, M. Tadesse + AuthorURL: https://github.com/iwmiwaplus + Tools & Applications: + - Title: Multi-Scale Water Accounting in the Volta Basin + URL: https://public.tableau.com/app/profile/iwmi.wa/viz/Voltabasinvertical/Merged?publish=yes + AuthorName: iwmiwaplus + AuthorURL: https://public.tableau.com/app/profile/iwmi.wa diff --git a/datasets/gulfwide-avian-monitoring.yaml b/datasets/gulfwide-avian-monitoring.yaml index bd9a0fdf3..162ae942a 100755 --- a/datasets/gulfwide-avian-monitoring.yaml +++ b/datasets/gulfwide-avian-monitoring.yaml @@ -1,43 +1,47 @@ ---- -Name: Gulfwide Avian Colony Monitoring Survey Photos -Description: > - For this project, The Water Institute (the Institute) and - subcontractor Colibri Ecological Consulting, LLC (Colibri) utilized - established methods and protocols capable of assessing changes of colonial - waterbird populations and their important habitats within individual states - and the broader northern Gulf of Mexico region. - Data collection activities included: - Aerial Photographic Nest Surveys: Implementation of fixed-wing aircraft surveys intended to assess waterbird colonies and document associated nesting within select portions of the northern Gulf of Mexico. Additional detail is provided on the Survey Protocols page of this portal. - Nest Dotting Analyses: Review and analysis of aerial photographic nest surveys (2010-2013, 2015, 2018, and 2021) with the intention of documenting the breeding population size and associated nesting for each species at each colony. Additional detail is provided on the Dotting Protocols page of this portal. -Documentation: https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647 -Contact: avaiandataaws@thewaterinstitute.org -ManagedBy: "[CPRA](https://coastal.la.gov/) and [The Water - Institute](https://thewaterinstitute.org/)" -UpdateFrequency: ~2 years -Tags: - - biology - - conservation - - ecosystems - - object detection - - labeled - - environmental - - aws-pds -License: Creative Commons BY-SA -Resources: - - Description: > - High resolution(5184 x 3456) images are provided in jpg format - (compression quality level 98%). - - The avian-monitoring folder includes the high resolution photos, the dotting process screenshots, the dotting information (birds and nest counts by species), and thumbnails of the subset of the images referenced on those dataset. Files in this subfolder have been renamed and organized to have a common naming convension across the years. - - The top level `High Resolution Images` includes all the high resolution images (even the ones not referenced in the dotting process) with their original filenames. - ARN: arn:aws:s3:::twi-aviandata - Region: us-east-2 - Type: S3 Bucket - Explore: - - "[Explore - dataset](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f35\ - 70220a53647/page/Data-Explorer/)" - - "[README](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3\ - 570220a53647/page/Project-Information/)" - - "[Data processing notebook](https://github.com/waterinstitute/avian_data_ingestor/blob/master/doc/Metadata%20for%20DottedImages.ipynb)" +--- +Name: Gulfwide Avian Colony Monitoring Survey Photos +Description: > + For this project, The Water Institute (the Institute) and + subcontractor Colibri Ecological Consulting, LLC (Colibri) utilized + established methods and protocols capable of assessing changes of colonial + waterbird populations and their important habitats within individual states + and the broader northern Gulf of Mexico region. + Data collection activities included: + Aerial Photographic Nest Surveys: Implementation of fixed-wing aircraft surveys intended to assess waterbird colonies and document associated nesting within select portions of the northern Gulf of Mexico. Additional detail is provided on the Survey Protocols page of this portal. + Nest Dotting Analyses: Review and analysis of aerial photographic nest surveys (2010-2013, 2015, 2018, and 2021) with the intention of documenting the breeding population size and associated nesting for each species at each colony. Additional detail is provided on the Dotting Protocols page of this portal. +Documentation: https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647 +Contact: avaiandataaws@thewaterinstitute.org +ManagedBy: "[CPRA](https://coastal.la.gov/) and [The Water + Institute](https://thewaterinstitute.org/)" +UpdateFrequency: ~2 years +Collabs: + ASDI: + Tags: + - biodiversity +Tags: + - biology + - conservation + - ecosystems + - object detection + - labeled + - environmental + - aws-pds +License: Creative Commons BY-SA +Resources: + - Description: > + High resolution(5184 x 3456) images are provided in jpg format + (compression quality level 98%). + + The avian-monitoring folder includes the high resolution photos, the dotting process screenshots, the dotting information (birds and nest counts by species), and thumbnails of the subset of the images referenced on those dataset. Files in this subfolder have been renamed and organized to have a common naming convension across the years. + + The top level `High Resolution Images` includes all the high resolution images (even the ones not referenced in the dotting process) with their original filenames. + ARN: arn:aws:s3:::twi-aviandata + Region: us-east-2 + Type: S3 Bucket + Explore: + - "[Explore + dataset](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f35\ + 70220a53647/page/Data-Explorer/)" + - "[README](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3\ + 570220a53647/page/Project-Information/)" + - "[Data processing notebook](https://github.com/waterinstitute/avian_data_ingestor/blob/master/doc/Metadata%20for%20DottedImages.ipynb)" diff --git a/datasets/hprc-epigenome.yaml b/datasets/hprc-epigenome.yaml new file mode 100644 index 000000000..40d4be708 --- /dev/null +++ b/datasets/hprc-epigenome.yaml @@ -0,0 +1,40 @@ +Name: Epigenomes of the Human Pangenome Reference Consortium (HPRC) Release 2 +Description: | + The Human Pangenome Reference Consortium (HPRC) Release 2 represents a landmark achievement in genomics, providing high-quality phased genome assemblies from over 200 individuals with comprehensive functional genomics data. The HPRC Epigenome Browser provides researchers a way to explore all epigenomics data generated by release 2. The HPRC Epigenome Browser (HPRCEB) is a modern, interactive web portal that democratizes access to HPRC Release 2 epigenomics data through an intuitive interface supporting genome selection, data visualization, and bulk download capabilities. The portal integrates genome assemblies, DNA methylation profiles, gene expression data, and chromatin accessibility measurements across diverse populations, enabling researchers to efficiently identify and retrieve datasets matching their specific research needs. +Contact: dli23@wustl.edu +ManagedBy: Ting Wang Lab (https://wang.wustl.edu/) +Documentation: https://epigenome.humanpangenome.org/?tab=tutorials +UpdateFrequency: Annual. The repository will be updated with each new batch of data as it is generated and released under the next HPRC yearly cycle. +Tags: + - biology + - bioinformatics + - genetic + - genomic + - epigenomics + - life sciences + - aws-pds +License: External data users may freely download, analyze, and publish results based on any HPRC data provided here without restrictions. +Resources: + - Description: HPRC Epigenome Browser + ARN: arn:aws:s3:::hprc-epigenome + Region: us-west-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: | + "Get To Know A Dataset: HPRC Epigenome" + URL: https://github.com/twlab/open-data-examples/blob/main/get-to-know-hprc-epigenome.ipynb + AuthorName: HPRC Epigenome Browser + AuthorURL: https://epigenome.humanpangenome.org/ + Publications: + - Title: A draft human pangenome reference + URL: https://doi.org/10.1038/s41586-023-05896-x + AuthorName: Liao, WW., Asri, M., Ebler, J. et al. + - Title: | + "Modbed track: Visualization of modified bases in single-molecule sequencing" + URL: https://www.sciencedirect.com/science/article/pii/S2666979X23002999?via%3Dihub + AuthorName: Daofeng Li, Xiaoyu Zhuo, Jessica K. Harrison, Shane Liu, Ting Wang + - Title: WashU Epigenome Browser update 2025 + URL: https://doi.org/10.1093/nar/gkaf387 + AuthorName: Chanrung Seng, Shane Liu, Wenjin Zhang, Xiaoyu Zhuo, Daofeng Li, Ting Wang + diff --git a/datasets/huj-herbarium.yaml b/datasets/huj-herbarium.yaml new file mode 100644 index 000000000..59b891572 --- /dev/null +++ b/datasets/huj-herbarium.yaml @@ -0,0 +1,43 @@ +Name: National Herbarium of Israel +Description: + Our collection encompasses approximately one million vascular plant specimens from the Mediterranean and Middle East biodiversity hotspot, representing flora from Israel, Jordan, Hermon, Sinai, Egypt, the Caucasus, Arabia, North Africa, and throughout the Mediterranean basin. This scientifically significant repository includes published voucher specimens, original specimens used for "Flora Palaestina" illustrations, and critical references for the Israeli gene bank collections. + The ongoing digitization process captures high-resolution images of each specimen while systematically incorporating label information into our computerized catalog. This virtual herbarium will democratize access to these valuable botanical resources, enabling global researchers to examine specimens in exceptional detail from anywhere in the world. + Beyond preservation, this digital transformation unlocks new research possibilities through computational analysis of both visual specimen characteristics and associated metadata. The dataset will serve as a foundational resource for advancing botanical research, ecological modeling, taxonomic investigation, historical analysis, and numerous other scientific disciplines concerned with plant biodiversity in this ecologically and historically significant region. +Documentation: https://bit.ly/HUJVirtualHerbarium +Contact: Eyal.Ben-Hur@mail.huji.ac.il +ManagedBy: National Natural History Collections, The Hebrew University of Jerusalem +UpdateFrequency: Monthly +Tags: + - biology + - life sciences + - biodiversity + - environmental + - climate + - digital preservation + - imaging + - image processing + - aws-pds +License: CC-BY-SA 4.0 +Citation: Vascular plants - Herbarium of The National Natural History Collections was accessed on DATE from https://registry.opendata.aws/huj-herbarium. +Resources: + - Description: HUJ Herbarium Collection Images + ARN: arn:aws:s3:::hujinnhc/specify_assets/ + Region: il-central-1 + Type: S3 Bucket + Explore: +DataAtWork: + Tutorials: + - Title: How to use AWS S3 bucket to explore our public images dataset + URL: https://bit.ly/HUJimages + NotebookURL: + AuthorName: Eyal Ben-Hur + AuthorURL: +DeprecatedNotice: +ADXCategories: + - Healthcare & Life Sciences Data + + + + + + diff --git a/datasets/humancellatlas.yaml b/datasets/humancellatlas.yaml index d1d9ec5d7..30bd81938 100644 --- a/datasets/humancellatlas.yaml +++ b/datasets/humancellatlas.yaml @@ -12,7 +12,7 @@ Documentation: https://data.humancellatlas.org/ Contact: https://data.humancellatlas.org/contact -ManagedBy: UC Santa Cruz Genomics Institute, University of California, Santa Cruz (UCSC) +ManagedBy: UC Santa Cruz Genomics Institute, University of California, Santa Cruz, UCSC UpdateFrequency: Monthly @@ -95,4 +95,4 @@ DataAtWork: AuthorName: "Various authors" ADXCategories: - - Healthcare & Life Sciences Data \ No newline at end of file + - Healthcare & Life Sciences Data diff --git a/datasets/hycom-global-drifters.yaml b/datasets/hycom-global-drifters.yaml index 729bcd4b8..725e38e4b 100644 --- a/datasets/hycom-global-drifters.yaml +++ b/datasets/hycom-global-drifters.yaml @@ -4,6 +4,10 @@ Documentation: https://github.com/selipot/hycom-oceantrack Contact: https://github.com/selipot/hycom-oceantrack/issues ManagedBy: Shane Elipot UpdateFrequency: Not updated +Collabs: + ASDI: + Tags: + - oceans Tags: - aws-pds - drifters diff --git a/datasets/hycom-gofs-3pt1-reanalysis.yaml b/datasets/hycom-gofs-3pt1-reanalysis.yaml index cf1d6a0a9..b56a0522c 100644 --- a/datasets/hycom-gofs-3pt1-reanalysis.yaml +++ b/datasets/hycom-gofs-3pt1-reanalysis.yaml @@ -4,6 +4,10 @@ Documentation: https://www.hycom.org/dataserver/gofs-3pt1/reanalysis Contact: help@hycom.org ManagedBy: "[COAPS](https://www.coaps.fsu.edu/)" UpdateFrequency: "Static Dataset Covering 1994-01-01 to 2015-12-31" +Collabs: + ASDI: + Tags: + - oceans Tags: - aws-pds - global diff --git a/datasets/ibl-autism.yaml b/datasets/ibl-autism.yaml new file mode 100644 index 000000000..84435dbfe --- /dev/null +++ b/datasets/ibl-autism.yaml @@ -0,0 +1,41 @@ +Name: IBL Neuropixels Brainwide Map on AWS +Description: Electrophysiological recordings of mouse brain activity acquired during a decision making task in multiple autism mice models. +Documentation: https://docs.internationalbrainlab.org/notebooks_external/2025_data_release_autism_noel.html +Contact: info@internationalbrainlab.org +ManagedBy: "[International Brain Laboratory](https://www.internationalbrainlab.com)" +UpdateFrequency: TBD +Tags: + - aws-pds + - life sciences + - neuroscience + - neurophysiology + - open source software + - Mus musculus + - autism spectrum disorder +License: CC-BY 4.0 +Resources: + - Description: Project data in public bucket + ARN: arn:aws:s3:::ibl-brain-wide-map-public + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Intermediate Datasets and Analysis Code + URL: https://osf.io/fap2s/ and https://osf.io/fap2s/wiki/home/ + AuthorName: Noel et al. + - Title: Download the public data via ONE + URL: https://docs.internationalbrainlab.org/notebooks_external/data_download.html + AuthorName: IBL Data Architecture Working Group + AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members + - Title: Find data associated with a release or publication + URL: https://docs.internationalbrainlab.org/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication + AuthorName: IBL Data Architecture Working Group + AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members + - Title: Loading Data + URL: https://docs.internationalbrainlab.org/loading_examples.html + AuthorName: IBL Data Architecture Working Group + AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members + Publications: + - Title: A common computational and neural anomaly across mouse models of autism + URL: https://doi.org/10.1038/s41593-025-01965-8 + AuthorName: Noel et al. diff --git a/datasets/ibl-behaviour.yaml b/datasets/ibl-behaviour.yaml index c79e76aee..cb54d3bbc 100644 --- a/datasets/ibl-behaviour.yaml +++ b/datasets/ibl-behaviour.yaml @@ -1,6 +1,6 @@ Name: IBL Behavioral Data on AWS Description: Behavioral data of mice performing a decision-making task, associated with 2020 publication of the IBL. -Documentation: https://int-brain-lab.github.io/iblenv/notebooks_external/data_release_behavior.html +Documentation: https://docs.internationalbrainlab.org/notebooks_external/2021_data_release_behavior.html Contact: info@internationalbrainlab.org ManagedBy: "[International Brain Laboratory](https://www.internationalbrainlab.com)" UpdateFrequency: TBD @@ -29,19 +29,19 @@ DataAtWork: AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members - Title: Download the public data via ONE - URL: https://int-brain-lab.github.io/iblenv/notebooks_external/data_download.html + URL: https://docs.internationalbrainlab.org/notebooks_external/data_download.html AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members - Title: Find data associated with a release or publication - URL: https://int-brain-lab.github.io/iblenv/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication + URL: https://docs.internationalbrainlab.org/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members - Title: Loading Data - URL: https://int-brain-lab.github.io/iblenv/loading_examples.html# + URL: https://docs.internationalbrainlab.org/loading_examples.html AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members Publications: - Title: Standardized and reproducible measurement of decision-making in mice URL: https://doi.org/10.7554/eLife.63711 AuthorName: International Brain Laboratory et al. - AuthorURL: www.internationalbrainlab.com \ No newline at end of file + AuthorURL: www.internationalbrainlab.com diff --git a/datasets/ibl-brain-wide-map.yaml b/datasets/ibl-brain-wide-map.yaml index 1ecc083d6..998456dcd 100644 --- a/datasets/ibl-brain-wide-map.yaml +++ b/datasets/ibl-brain-wide-map.yaml @@ -1,6 +1,6 @@ Name: IBL Neuropixels Brainwide Map on AWS -Description: Electrophysiological recordings of mouse brain activity acquired using Neuropixels probes and accompanying behavioral data. -Documentation: https://int-brain-lab.github.io/iblenv/notebooks_external/data_release_brainwidemap.html +Description: Electrophysiological recordings of mouse brain activity acquired during a decision making task. +Documentation: https://docs.internationalbrainlab.org/notebooks_external/2025_data_release_brainwidemap.html Contact: info@internationalbrainlab.org ManagedBy: "[International Brain Laboratory](https://www.internationalbrainlab.com)" UpdateFrequency: TBD @@ -40,15 +40,24 @@ DataAtWork: URL: https://colab.research.google.com/drive/1th3MRZGHMSaeAvGmKGJQ84rBk8eEI4Fu AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members - - Title: Download the public datasets - URL: https://int-brain-lab.github.io/iblenv/notebooks_external/data_download.html + - Title: Download the public data via ONE + URL: https://docs.internationalbrainlab.org/notebooks_external/data_download.html AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members - Title: Find data associated with a release or publication - URL: https://int-brain-lab.github.io/iblenv/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication + URL: https://docs.internationalbrainlab.org/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members - Title: Loading Data - URL: https://int-brain-lab.github.io/iblenv/loading_examples.html# + URL: https://docs.internationalbrainlab.org/loading_examples.html AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members + Publications: + - Title: A brain-wide map of neural activity during complex behaviour + URL: https://doi.org/10.1038/s41586-025-09235-0 + AuthorName: International Brain Laboratory et al. + AuthorURL: www.internationalbrainlab.com + - Title: Brain-wide representations of prior information in mouse decision-making + URL: https://doi.org/10.1038/s41586-025-09226-1 + AuthorName: International Brain Laboratory et al. + AuthorURL: www.internationalbrainlab.com diff --git a/datasets/ibl-reproducible-ephys.yaml b/datasets/ibl-reproducible-ephys.yaml index 22150a6f0..9a16e1119 100644 --- a/datasets/ibl-reproducible-ephys.yaml +++ b/datasets/ibl-reproducible-ephys.yaml @@ -1,6 +1,6 @@ Name: IBL Neuropixels Reproducible Ephys Data on AWS Description: Electrophysiological recordings acquired using Neuropixels probes in different mice and labs, targeting the same brain locations (including posterior parietal cortex, hippocampus, and thalamus). -Documentation: https://int-brain-lab.github.io/iblenv/notebooks_external/data_release_repro_ephys.html +Documentation: https://docs.internationalbrainlab.org/notebooks_external/2024_data_release_repro_ephys.html Contact: info@internationalbrainlab.org ManagedBy: "[International Brain Laboratory](https://www.internationalbrainlab.com)" UpdateFrequency: TBD @@ -28,20 +28,24 @@ DataAtWork: AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members Tutorials: - - Title: Download the public datasets - URL: https://int-brain-lab.github.io/iblenv/notebooks_external/data_download.html + - Title: Compute the RIGOR metrics + URL: https://github.com/int-brain-lab/paper-reproducible-ephys/blob/2397f2cf5b92689f39e94ef7d8f76f0a7e2bd2a7/RIGOR_script.ipynb + AuthorName: IBL Data Architecture Working Group + AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members + - Title: Download the public data via ONE + URL: https://docs.internationalbrainlab.org/notebooks_external/data_download.html AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members - Title: Find data associated with a release or publication - URL: https://int-brain-lab.github.io/iblenv/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication + URL: https://docs.internationalbrainlab.org/notebooks_external/data_download.html#Find-data-associated-with-a-release-or-publication AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members - Title: Loading Data - URL: https://int-brain-lab.github.io/iblenv/loading_examples.html# + URL: https://docs.internationalbrainlab.org/loading_examples.html AuthorName: IBL Data Architecture Working Group AuthorURL: https://github.com/orgs/int-brain-lab/teams/data-architecture-wg/members Publications: - - Title: Reproducibility of in-vivo electrophysiological measurements in mice - URL: https://doi.org/10.1101/2022.05.09.491042 + - Title: Reproducibility of in vivo electrophysiological measurements in mice + URL: https://doi.org/10.7554/eLife.100840.1 AuthorName: International Brain Laboratory et al. - AuthorURL: www.internationalbrainlab.com \ No newline at end of file + AuthorURL: www.internationalbrainlab.com diff --git a/datasets/iceye-opendata.yaml b/datasets/iceye-opendata.yaml new file mode 100644 index 000000000..aee9631b9 --- /dev/null +++ b/datasets/iceye-opendata.yaml @@ -0,0 +1,45 @@ +Name: ICEYE Synthetic Aperture Radar (SAR) Open Dataset +Description: | + ICEYE operates the world’s largest constellation of synthetic aperture radar (SAR) satellites, delivering unmatched access to persistent, high-resolution Earth observation data regardless of time of day or weather conditions. The ICEYE Open Dataset makes a curated selection of SAR imagery publicly available to promote research, innovation, and education in the geospatial community. ICEYE’s constellation enables rapid revisit rates and flexible imaging modes, unlocking insights into natural disasters, climate monitoring, infrastructure, and more. + + Learn more at [www.iceye.com](https://www.iceye.com). +Documentation: Documentation is available at the [ICEYE website](https://www.iceye.com) and the [ICEYE Product Documentation](sar.iceye.com). +Contact: customer@iceye.com +ManagedBy: "[ICEYE](https://www.iceye.com)" +UpdateFrequency: New data is added frequently. +Collabs: + ASDI: + Tags: + - satellite imagery +Tags: + - aws-pds + - synthetic aperture radar + - stac + - earth observation + - satellite imagery + - image processing + - geospatial + - computer vision + - disaster response +License: | + The data is provided under the Creative Commons License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), which gives the user the right to share, copy, and redistribute the material in any medium or format, as well as adapt, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given to the original creator. +Resources: + - Description: ICEYE Open SAR Data + ARN: arn:aws:s3:::iceye-open-data-catalog + Region: us-west-2 + Type: S3 Bucket + RequesterPays: False + Explore: + - '[Browse bucket](http://iceye-open-data-catalog.s3-website-us-west-2.amazonaws.com)' + - '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/iceye-open-data-catalog.s3-us-west-2.amazonaws.com/catalog.json)' + - Description: Notification for new ICEYE Open SAR Data + ARN: arn:aws:sns:us-west-2:058264311954:iceye-open-data-catalog-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: ICEYE Product Documentation + URL: sar.iceye.com + AuthorName: ICEYE + AuthorURL: https://www.iceye.com + diff --git a/datasets/ideam-radares.yaml b/datasets/ideam-radares.yaml index f66486136..98913330e 100644 --- a/datasets/ideam-radares.yaml +++ b/datasets/ideam-radares.yaml @@ -29,9 +29,9 @@ DataAtWork: - Title: Read and plot Sigmet files available on AWS using Xradar URL: https://docs.openradarscience.org/projects/xradar/en/stable/notebooks/Read-plot-Sigmet-data-from-AWS.html AuthorName: Alfonso Ladino - - Title: Taller de datos científicos con Python y R - AtmosCol 2023 - URL: https://projectpythia.org/AtmosCol-2023/notebooks/2.acceso-datos/2.2.Radares.html + - Title: Ciencia de Datos Hidrometeorológicos con Python + URL: https://projectpythia.org/AtmosCol-2023/radares AuthorName: Alfonso Ladino, Nicole Rivera, Max Grover - Title: Specific Differential Phase (KDP) retrieval methods comparison - URL: https://projectpythia.org/radar-cookbook/notebooks/example-workflows/kdp-comparison.html + URL: https://projectpythia.org/radar-cookbook/notebooks/example-workflows/kdp-comparison/ AuthorName: Alfonso Ladino, Max Grover diff --git a/datasets/igvf-consortium.yaml b/datasets/igvf-consortium.yaml new file mode 100644 index 000000000..2b5e40cbc --- /dev/null +++ b/datasets/igvf-consortium.yaml @@ -0,0 +1,52 @@ +Name: The Impact of Variation on Function Consortium (IGVF) +Description: | + The IGVF (Impact of Genomic Variation on Function) Consortium aims to understand how genomic variation affects genome function, + which in turn impacts phenotype. The NHGRI is funding this collaborative program that brings together teams of investigators who + will use state-of-the-art experimental and computational approaches to model, predict, characterize and map genome function, how + genome function shapes phenotype, and how these processes are affected by genomic variation. These joint efforts will produce a + catalog of the impact of genomic variants on genome function and phenotypes. + The Data Corpus consists of single-cell Genomics experiments (both single modal, and multimodal, typically snRNA-seq and snATAC-seq), + Characterization experiments using Massively Parallel Reporter Assays (MPRAs) and CRISPR-screens along with a variety of protein mutatation + assays, and Predictive Models. + There are a huge variety of files in IGVF that are stored in the AWS OpenData Set so we recommend using the [metadata file]() or browsing the [IGVF Data Portal](https://data.igvf.org) +Contact: igvf-portal-help@lists.stanford.edu +ManagedBy: IGVF Data Administration and Coordination Center at Stanford University +Documentation: https://data.igvf.org/general-help +UpdateFrequency: Daily +Tags: + - aws-pds + - biology + - bioinformatics + - genetic + - genomic + - life sciences +License: CC BY 4.0 - https://creativecommons.org/licenses/by/4.0/ - You are free to share and adapt this data with proper attribution +Resources: + - Description: Released and Archived IGVF Data Files + ARN: arn:aws:s3:::igvf-public + Region: us-west-2 + Type: S3 Bucket + - Description: Notifications for new IGVF data + ARN: arn:aws:sns:us-west-2:407227577691:igvf-public-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Load AnnData files from IGVF into scanpy and view the UMAPs + URL: https://github.com/IGVF-DACC/igvf-data-usage-examples/blob/master/igvf-scanpy.ipynb + AuthorName: Ben Hitz + AuthorURL: https://github.com/hitz + - Title: Ingesting IGVF Data into TileDB with S3 backend + URL: https://github.com/IGVF-DACC/igvf-data-usage-examples/blob/master/ingest_igvf_h5ad_data_to_anndata_and_tiledb.ipynb + AuthorName: Otto Jolanki + AuthorURL: https://github.com/ottojolanki + Tools & Applications: + - Title: The IGVF Catalog + URL: https://catalog.igvf.org + AuthorName: The IGVF Consortium + AuthorURL: www.igvf.org + Publications: + - Title: Deciphering the impact of genomic variation on function + URL: https://www-nature-com.stanford.idm.oclc.org/articles/s41586-024-07510-0 + AuthorName: Jesse M. Engreitz and The IGVF Consortium + AuthorURL: https://orcid.org/0000-0002-5754-1719 diff --git a/datasets/in-elevation.yaml b/datasets/in-elevation.yaml index 2b07759c8..f2a5169cb 100644 --- a/datasets/in-elevation.yaml +++ b/datasets/in-elevation.yaml @@ -1,38 +1,42 @@ -Name: Indiana Statewide Elevation Catalog -Description: | - The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital LiDAR LAS files stored in AWS, dating back to the 2011-2013 collection and including the NRCS-funded 2016-2020 collection. These LiDAR datasets are available as uncompressed LAS files, for cloud storage and access. Each year's data is organized into a tile grid scheme covering the entire geography of Indiana, ensuring easy access and efficient processing. The tiles' naming reflects each tile's lower left coordinate, facilitating accurate data management and retrieval. The AWS storage solution ensures that these extensive datasets are readily accessible for analysis and application across various projects. -Documentation: https://elevation.gio.in.gov/ -Contact: sscholer@iot.in.gov -ManagedBy: Indiana Geographic Information Office -UpdateFrequency: The State of Indiana has another four-year cycle of collecting orthoimagery and Lidar starting in 2025 and continuing through 2028. The collections are designated by counties in three groups that cover Indiana, South to North. These areas are frequently referred to as Tiers in the other documentation. For example, tier 1 (Central 3rd) extends from Harrison County in the South to Elkhart and St. Joseph County in the North, while Tier 2 consists of the counties to the eastern side of the State, and Tier 3 is those counties to the western side of the State. -Tags: -- lidar -- aws-pds -- earth observation -- geospatial -- imaging -- mapping -- natural resource -- sustainability -- agriculture -License: "Access to Indiana Geographic Information Office Lidar is governed by Creative Commons 0 (CC0): https://creativecommons.org/publicdomain/zero/1.0/legalcode" -Resources: -- Description: State of Indiana Elevation archive. - ARN: arn:aws:s3:::giselevationingov - Region: us-east-2 - Type: S3 Bucket -DataAtWork: - Tutorials: - Tools & Applications: - - Title: ArcGIS Online Indiana Lidar Viewer - URL: https://indianamap-inmap.hub.arcgis.com/maps/ff98e3834d464619bd5c8974b0038a13/about - AuthorName: Indiana Geographic Information Office (IGIO) - - Title: IGIO Elevation Opendata S3 Browser - URL: https://giselevationingov.s3.amazonaws.com/index.html - AuthorName: Indiana Geographic Information Office (IGIO) - - Publications: - - Title: "Recording of 2025 - 2028 Indiana Imagery and Elevation Program Presentation" - URL: https://elevation.gio.in.gov/pages/resources - AuthorName: Indiana Geographic Information Office (IGIO) - +Name: Indiana Statewide Elevation Catalog +Description: | + The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital LiDAR LAS files stored in AWS, dating back to the 2011-2013 collection and including the NRCS-funded 2016-2020 collection. These LiDAR datasets are available as uncompressed LAS files, for cloud storage and access. Each year's data is organized into a tile grid scheme covering the entire geography of Indiana, ensuring easy access and efficient processing. The tiles' naming reflects each tile's lower left coordinate, facilitating accurate data management and retrieval. The AWS storage solution ensures that these extensive datasets are readily accessible for analysis and application across various projects. +Documentation: https://elevation.gio.in.gov/ +Contact: sscholer@iot.in.gov +ManagedBy: Indiana Geographic Information Office +UpdateFrequency: The State of Indiana has another four-year cycle of collecting orthoimagery and Lidar starting in 2025 and continuing through 2028. The collections are designated by counties in three groups that cover Indiana, South to North. These areas are frequently referred to as Tiers in the other documentation. For example, tier 1 (Central 3rd) extends from Harrison County in the South to Elkhart and St. Joseph County in the North, while Tier 2 consists of the counties to the eastern side of the State, and Tier 3 is those counties to the western side of the State. +Collabs: + ASDI: + Tags: + - elevation +Tags: +- lidar +- aws-pds +- earth observation +- geospatial +- imaging +- mapping +- natural resource +- sustainability +- agriculture +License: "Access to Indiana Geographic Information Office Lidar is governed by Creative Commons 0 (CC0): https://creativecommons.org/publicdomain/zero/1.0/legalcode" +Resources: +- Description: State of Indiana Elevation archive. + ARN: arn:aws:s3:::giselevationingov + Region: us-east-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + Tools & Applications: + - Title: ArcGIS Online Indiana Lidar Viewer + URL: https://indianamap-inmap.hub.arcgis.com/maps/ff98e3834d464619bd5c8974b0038a13/about + AuthorName: Indiana Geographic Information Office (IGIO) + - Title: IGIO Elevation Opendata S3 Browser + URL: https://giselevationingov.s3.amazonaws.com/index.html + AuthorName: Indiana Geographic Information Office (IGIO) + + Publications: + - Title: "Recording of 2025 - 2028 Indiana Imagery and Elevation Program Presentation" + URL: https://elevation.gio.in.gov/pages/resources + AuthorName: Indiana Geographic Information Office (IGIO) + diff --git a/datasets/in-imagery.yaml b/datasets/in-imagery.yaml index 0c1ed1225..ce52165b2 100644 --- a/datasets/in-imagery.yaml +++ b/datasets/in-imagery.yaml @@ -1,40 +1,44 @@ -Name: Indiana Statewide Digital Aerial Imagery Catalog -Description: | - The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital orthophotography dating back to 2005. Every year's worth of imagery is available as Cloud Optimized GeoTIFF (COG) files, original GeoTIFF, and other compressed deliverables such as ECW and MrSID. Additionally, each imagery year is organized into a tile grid scheme covering the entire geography of Indiana. All years of imagery are tiled from a 5,000 ft grid or sub tiles depending upon the resolution of the imagery. The naming of the tiles reflects the lower left coordinate from the image. -Documentation: https://imagery.gio.in.gov/ -Contact: sscholer@iot.in.gov -ManagedBy: Indiana Geographic Information Office -UpdateFrequency: The State of Indiana has had a 4-year cycle collecting imagery. The collections are designated by counties in three groups that cover Indiana, South to North. These areas are frequently referred to as Tiers in the other documentation. For example, tier 1 (Central 3rd) extends from Harrison County in the South to Elkhart and St. Joseph County in the North, while Tier 2 consists of the counties to the eastern side of the State, and Tier 3 is those counties to the western side of the State. -Tags: -- aerial imagery -- aws-pds -- earth observation -- geospatial -- imaging -- mapping -- cog -- natural resource -- sustainability -- agriculture -License: "Access to Indiana Geographic Information Office Orthoimagery is governed by Creative Commons 0 (CC0): https://creativecommons.org/publicdomain/zero/1.0/legalcode" -Resources: -- Description: State of Indiana digital orthophotography archive. - ARN: arn:aws:s3:::gisimageryingov - Region: us-east-2 - Type: S3 Bucket -DataAtWork: - Tutorials: - Tools & Applications: - - Title: ArcGIS Online Indiana Orthoimagery Viewer - URL: https://indianamap-inmap.hub.arcgis.com/datasets/61d4dc991c154af49ad7c1d675182a4f/explore - AuthorName: Indiana Geographic Information Office (IGIO) - - Title: IGIO Imagery Opendata S3 Browser - URL: https://gisimageryingov.s3.amazonaws.com/index.html - AuthorName: Indiana Geographic Information Office (IGIO) - - Publications: - - Title: "Recording of 2025 - 2028 Indiana Orthoimagery Program Presentation" - URL: https://imagery.gio.in.gov/pages/resources - AuthorName: Indiana Geographic Information Office (IGIO) - - +Name: Indiana Statewide Digital Aerial Imagery Catalog +Description: | + The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital orthophotography dating back to 2005. Every year's worth of imagery is available as Cloud Optimized GeoTIFF (COG) files, original GeoTIFF, and other compressed deliverables such as ECW and MrSID. Additionally, each imagery year is organized into a tile grid scheme covering the entire geography of Indiana. All years of imagery are tiled from a 5,000 ft grid or sub tiles depending upon the resolution of the imagery. The naming of the tiles reflects the lower left coordinate from the image. +Documentation: https://imagery.gio.in.gov/ +Contact: sscholer@iot.in.gov +ManagedBy: Indiana Geographic Information Office +UpdateFrequency: The State of Indiana has had a 4-year cycle collecting imagery. The collections are designated by counties in three groups that cover Indiana, South to North. These areas are frequently referred to as Tiers in the other documentation. For example, tier 1 (Central 3rd) extends from Harrison County in the South to Elkhart and St. Joseph County in the North, while Tier 2 consists of the counties to the eastern side of the State, and Tier 3 is those counties to the western side of the State. +Collabs: + ASDI: + Tags: + - climate +Tags: +- aerial imagery +- aws-pds +- earth observation +- geospatial +- imaging +- mapping +- cog +- natural resource +- sustainability +- agriculture +License: "Access to Indiana Geographic Information Office Orthoimagery is governed by Creative Commons 0 (CC0): https://creativecommons.org/publicdomain/zero/1.0/legalcode" +Resources: +- Description: State of Indiana digital orthophotography archive. + ARN: arn:aws:s3:::gisimageryingov + Region: us-east-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + Tools & Applications: + - Title: ArcGIS Online Indiana Orthoimagery Viewer + URL: https://indianamap-inmap.hub.arcgis.com/datasets/61d4dc991c154af49ad7c1d675182a4f/explore + AuthorName: Indiana Geographic Information Office (IGIO) + - Title: IGIO Imagery Opendata S3 Browser + URL: https://gisimageryingov.s3.amazonaws.com/index.html + AuthorName: Indiana Geographic Information Office (IGIO) + + Publications: + - Title: "Recording of 2025 - 2028 Indiana Orthoimagery Program Presentation" + URL: https://imagery.gio.in.gov/pages/resources + AuthorName: Indiana Geographic Information Office (IGIO) + + diff --git a/datasets/indian-supreme-court-judgments.yaml b/datasets/indian-supreme-court-judgments.yaml new file mode 100644 index 000000000..d9f2d14d7 --- /dev/null +++ b/datasets/indian-supreme-court-judgments.yaml @@ -0,0 +1,29 @@ +Name: Indian Supreme Court Judgments +Description: This dataset contains judgements from the Indian Supreme Court, downloaded from ecourts website. It contains judgments from 1950 to 2025, along with raw metadata (in json format) and structured metadata in parquet format. Judgments are available in both English and regional Indian languages in zip format for easier download. +Documentation: https://github.com/vanga/indian-supreme-court-judgments/blob/main/opendata/docs/dataset.md +Contact: contact@dattam.in +ManagedBy: "[Dattam Labs](https://dattam.in)" +UpdateFrequency: Bi-monthly +Tags: + - legal data + - aws-pds +License: CC-BY-4.0 +Resources: + - Description: S3 bucket containing the judgments + ARN: arn:aws:s3:::indian-supreme-court-judgments + Region: ap-south-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Using AWS Athena to query the metadata + URL: https://github.com/vanga/indian-supreme-court-judgments/blob/main/opendata/tutorials/ATHENA.md + AuthorName: Nihesh Rachakonda + AuthorURL: https://github.com/rnihesh + Services: + - Amazon Athena + - Title: Dataset Overview and Usage Examples + URL: https://github.com/vanga/indian-supreme-court-judgments/blob/main/opendata/tutorials/README.md + AuthorName: Nihesh Rachakonda + AuthorURL: https://github.com/rnihesh + Services: + - Amazon S3 diff --git a/datasets/intelinair_corn_kernel_counting.yaml b/datasets/intelinair_corn_kernel_counting.yaml index 697a72bec..320274cdb 100644 --- a/datasets/intelinair_corn_kernel_counting.yaml +++ b/datasets/intelinair_corn_kernel_counting.yaml @@ -4,6 +4,10 @@ Documentation: https://www.frontiersin.org/articles/10.3389/frobt.2021.627009/ab Contact: support@intelinair.com ManagedBy: Intelinair, Inc. UpdateFrequency: Periodically +Collabs: + ASDI: + Tags: + - agriculture Tags: - aws-pds - agriculture diff --git a/datasets/intelinair_longitudinal_nutrient_deficiency.yaml b/datasets/intelinair_longitudinal_nutrient_deficiency.yaml index 442a04c15..cde7b326a 100644 --- a/datasets/intelinair_longitudinal_nutrient_deficiency.yaml +++ b/datasets/intelinair_longitudinal_nutrient_deficiency.yaml @@ -4,6 +4,10 @@ Documentation: https://arxiv.org/abs/2012.09654 Contact: support@intelinair.com ManagedBy: Intelinair, Inc. UpdateFrequency: Periodically +Collabs: + ASDI: + Tags: + - agriculture Tags: - aws-pds - aerial imagery diff --git a/datasets/isic-archive.yaml b/datasets/isic-archive.yaml new file mode 100644 index 000000000..b4eacb9e0 --- /dev/null +++ b/datasets/isic-archive.yaml @@ -0,0 +1,61 @@ +Name: International Skin Imaging Collaboration (ISIC) Archive +Description: A public-access archive of skin lesion images, supporting teaching, research, and the development and evaluation of diagnostic algorithms. +Documentation: https://www.isic-archive.com/ +Contact: support@isic-archive.com +ManagedBy: International Skin Imaging Collaboration (ISIC) +UpdateFrequency: Upon new data ingest from contributors. +Tags: + - biology + - cancer + - classification + - computational pathology + - dicom + - grand-challenge.org + - health + - Homo sapiens + - imaging + - life sciences + - machine learning + - medical image computing + - medical imaging + - medicine + - microscopy + - segmentation +License: Creative Commons licenses (CC-0, CC-BY, or CC-BY-NC) are defined per-image. +Resources: + - Description: Images of skin lesions and associated metadata + ARN: arn:aws:s3:::isic-archive + Region: us-east-1 + Type: S3 Bucket + - Description: Notifications of new data + ARN: arn:aws:sns:us-east-1:024848456264:isic-archive-object_created + Region: us-east-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: ISIC Archive Data Dictionary + URL: https://www.isic-archive.com/data-dictionary + NotebookURL: + AuthorName: International Skin Imaging Collaboration (ISIC) + Tools & Applications: + - Title: ISIC Archive Gallery + URL: https://gallery.isic-archive.com + AuthorName: International Skin Imaging Collaboration (ISIC) + - Title: isic-cli - The official command line tool for interacting with the ISIC Archive + URL: https://pypi.org/project/isic-cli + AuthorName: International Skin Imaging Collaboration (ISIC) + Publications: + - Title: "A patient-centric dataset of images and metadata for identifying melanomas using clinical context" + URL: https://doi.org/10.1038/s41597-021-00815-z + AuthorName: Rotemberg V, Kurtansky N, Betz-Stablein B, Caffery L, Chousakos E, Codella N, et al + - Title: "The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection" + URL: https://doi.org/10.1038/s41597-024-03743-w + AuthorName: Kurtansky N, D'Alessandro B, Gillis M, Betz-Stablein B, Cerminara S, Garcia R, et al + - Title: "International Skin Imaging Collaboration - Designated Diagnoses (ISIC-DX): Consensus terminology for lesion diagnostic labeling" + URL: https://doi.org/10.1111/jdv.20055 + AuthorName: Scope A, Liopyris K, Weber J, Barnhill R, Braun R, Curiel-Lewandrowski C, et al + - Title: "Human surface anatomy terminology for dermatology: a Delphi consensus from the International Skin Imaging Collaboration" + URL: https://doi.org/10.1111/jdv.16855 + AuthorName: Navarrete-Dechent C, Liopyris K, Molenda M, Braun R, Curiel-Lewandrowski C, Dusza S, et al +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/its-live-data.yaml b/datasets/its-live-data.yaml index 4a734198f..1e767143b 100644 --- a/datasets/its-live-data.yaml +++ b/datasets/its-live-data.yaml @@ -31,6 +31,10 @@ Contact: > ManagedBy: "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)" UpdateFrequency: Up to daily, as new satellite imagery is made available. +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - ice diff --git a/datasets/japan_pointcloud.yaml b/datasets/japan_pointcloud.yaml new file mode 100644 index 000000000..5cc02b8ff --- /dev/null +++ b/datasets/japan_pointcloud.yaml @@ -0,0 +1,35 @@ +Name: Japan Prefectures, 3D Point Cloud Data +Description: | + This dataset comprises high-precision 3D point cloud data that covers all prefectures throughout Japan. + The data is produced through aerial laser surveys, airborne laser bathymetry, and mobile mapping systems, representing the culmination of many years of dedicated effort. + This data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures, and autonomous vehicle driving. +Documentation: https://github.com/aigidjp/opendata_japan_pointcloud/blob/main/README.md +Contact: japan-pointcloud@aigid.jp +ManagedBy: "[AIGID](https://aigid.jp/)" +UpdateFrequency: Currently not scheduled +Tags: + - aws-pds + - disaster response + - elevation + - geospatial + - japanese + - land + - lidar + - mapping +License: "Creative Commons Attribution 4.0 International (CC-BY 4.0)" +Resources: + - Description: Point Cloud Data for Prefectures Across Japan + ARN: arn:aws:s3:::japan-pointcloud + Region: ap-northeast-1 + Type: S3 Bucket + - Description: Notifications for new japan-pointcloud data + ARN: arn:aws:sns:ap-northeast-1:250546175908:japan-pointcloud-object_created + Region: ap-northeast-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Tutorial of handling LAS format point cloud data + URL: https://github.com/aigidjp/opendata_japan_pointcloud/blob/main/tutorials/README.md + AuthorName: AIGID + Tools & Applications: + Publications: diff --git a/datasets/kanagawa_pointcloud.yaml b/datasets/kanagawa_pointcloud.yaml new file mode 100644 index 000000000..0ef88f532 --- /dev/null +++ b/datasets/kanagawa_pointcloud.yaml @@ -0,0 +1,35 @@ +Name: Kanagawa, 3D Point Cloud Data +Description: | + This dataset comprises high-precision 3D point cloud data that encompasses the entire Kanagawa prefecture in Japan. + The data is produced through aerial laser survey, airborne laser bathymetry and mobile mapping systems, the culmination of many years of dedicated effort. + This data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures and autonomous vehicle driving. +Documentation: https://github.com/aigidjp/opendata_kanagawa_pointcloud/blob/main/README.md +Contact: kanagawa-pointcloud@aigid.jp +ManagedBy: "[AIGID](https://aigid.jp/)" +UpdateFrequency: Currently not scheduled +Tags: + - aws-pds + - disaster response + - elevation + - geospatial + - japanese + - land + - lidar + - mapping +License: "Creative Commons Attribution 4.0 International (CC-BY 4.0)" +Resources: + - Description: Point Cloud Data of Kanagawa Prefecture, Japan + ARN: arn:aws:s3:::kanagawa-pointcloud + Region: ap-northeast-1 + Type: S3 Bucket + - Description: Notifications for new kanagawa-pointcloud data + ARN: arn:aws:sns:ap-northeast-1:895319340027:kanagawa-pointcloud_created + Region: ap-northeast-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Tutorial of handling LAS format point cloud data + URL: https://github.com/aigidjp/opendata_kanagawa_pointcloud/blob/main/tutorials/README.md + AuthorName: AIGID + Tools & Applications: + Publications: diff --git a/datasets/kreppref.yaml b/datasets/kreppref.yaml new file mode 100644 index 000000000..d0d60298d --- /dev/null +++ b/datasets/kreppref.yaml @@ -0,0 +1,29 @@ +Name: Reference Indexes for krepp +Description: krepp is an alignment-free method for estimating distances and phylogenetic placement of individual reads to many thousands of reference genomes in a scalable manner using k-mers. This dataset includes k-mer-based indexes consisting of ultra-large reference genome sets that can be efficiently analyzed using krepp. +Documentation: https://github.com/bo1929/krepp/wiki/Available-reference-indexes +Contact: https://github.com/bo1929/krepp/issues +ManagedBy: Mirarab Lab at UC San Diego +UpdateFrequency: Quarterly or as new data becomes available +Tags: + - bioinformatics + - metagenomics + - microbiome + - reference index + - aws-pds + - life sciences +License: GPL-3.0 license. Use of the data should be cited in the usual way, following https://github.com/bo1929/krepp/tree/master?tab=readme-ov-file#citation. +Resources: + - Description: This dataset contains genomic indexes for various reference datasets in binary format. Using krepp, you can perform distance estimation and phylogenetic placement with respect to these indexes. + ARN: arn:aws:s3:::kreppref + Region: us-west-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Tutorial for using krepp indexes for metagenomic sequence analysis. + URL: https://github.com/bo1929/krepp/wiki/Tutorial + AuthorName: Ali Osman Berk Sapci + AuthorURL: https://bo1929.github.io/ + Publications: + - Title: A k-mer-based maximum likelihood method for estimating distances of reads to genomes enables genome-wide phylogenetic placement. + URL: https://www.biorxiv.org/content/10.1101/2025.01.20.633730v2 + AuthorName: Sapci et al. (2024) diff --git a/datasets/kyfromabove.yaml b/datasets/kyfromabove.yaml index 51644285a..ab64b673b 100644 --- a/datasets/kyfromabove.yaml +++ b/datasets/kyfromabove.yaml @@ -3,23 +3,37 @@ Description: The KyFromAbove initiative is focused on building and maintaining a Documentation: https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove Contact: More information regarding the KyFromAbove program can be found at https://kyfromabove.ky.gov. If you have specific questions please contact - kyfromabove@ky.gov. ManagedBy: "[Kentucky Division of Geographic Information](https://kygeonet.ky.gov)" -UpdateFrequency: KyFromAbove data is typically updated on an annual basis. Each year, a portion of the state is acquired with an overall update cycle of every three to four years. This update cadance is determined by both funding and the length of leaf-off conditions in a given year. This catalog currently includes imagery and LiDAR data from 2010 through 2024 for most products. +UpdateFrequency: KyFromAbove data are typically updated on an annual basis. Each year, a portion of the state is acquired with an overall update cycle of every three to four years. This update cadance is determined by both funding and the length of leaf-off conditions in a given year. This catalog currently includes imagery and LiDAR data from 2010 through 2024 for most products. +Collabs: + ASDI: + Tags: + - elevation Tags: + - aerial imagery - aws-pds + - cog + - dtm + - disaster response - earth observation - - aerial imagery + - elevation + - geopackage - geospatial - lidar - - elevation + - mapping + - stac + - tiff + - tiles License: | Public Domain with Attribution Resources: - - Description: Elevation and imagery data resources for the Commonwealth of Kentucky are organized in this bucket. Elevation data is available in Cloud Optimized GeoTIFF (COG) and Geopackage formats depending on the data type. Imagery data is also available in Cloud Optimized GeoTIFF (COG)format. A Cloud Optimized GeoTIFF (COG) is a GeoTIFF file optimized for hosting on a HTTP file server. COG has an internal organization that enables more efficient workflows on the cloud by supporting HTTP GET range requests, where just parts of a file are requested and returned. + - Description: Elevation and imagery data resources for the Commonwealth of Kentucky are organized in this bucket. Elevation data are available in Cloud Optimized GeoTIFF (COG) and Geopackage formats depending on the data type. Imagery data is also available in COG format. A Cloud Optimized GeoTIFF (COG) is a GeoTIFF file optimized for hosting on a HTTP file server. COG has an internal organization that enables more efficient workflows on the cloud by supporting HTTP GET range requests, where just parts of a file are requested and returned. ARN: arn:aws:s3:::kyfromabove Region: us-west-2 Type: S3 Bucket RequesterPays: False Explore: + - '[KyFromAbove Stac-Browser](https://kygeonet.ky.gov/stac)' + - '[STAC V1.0.0 endpoint](https://spved5ihrl.execute-api.us-west-2.amazonaws.com/)' - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html)' - Description: KyFromAbove Topographic Contours, digital elevation models, point cloud, spot elevations and the KyTopo Map Series quadrangles can be found in this bucket. ARN: arn:aws:s3:::kyfromabove/elevation/ @@ -28,33 +42,41 @@ Resources: RequesterPays: False Explore: - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/)' - - Description: Topographic contours created from the KyFromAbove Phase 1 LiDAR-derived digital elevation model (DEM) in a geopackage and Esri file geodatabase format. There are four data resources in this folder - 1) KyTopo contours at a 10-foot interval primarily for Western and Central Kentucky, 2) KyTopo contours at a 20-foot interval primarily for Central and Eastern Kentucky, 3) KyTopo contours at a 40-foot interval for Eastern Kentucky, and 4) KyTopo contours at a 5-foot interval for the entire Commonwealth. + - Description: Topographic contours created from the KyFromAbove Phase 1 LiDAR-derived digital elevation model (DEM) in geopackage and Esri file geodatabase format. There are four data resources in this folder - 1) KyTopo contours at a 10-foot interval primarily for Western and Central Kentucky, 2) KyTopo contours at a 20-foot interval primarily for Central and Eastern Kentucky, 3) KyTopo contours at a 40-foot interval for Eastern Kentucky, and 4) KyTopo contours at a 5-foot interval for the entire Commonwealth. ARN: arn:aws:s3:::kyfromabove/elevation/Contours/ Region: us-west-2 Type: S3 Bucket RequesterPays: False Explore: - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/Contours/)' - - Description: LiDAR-derived digital elevation models (DEM) for the Commonwealth of Kentucky organized in a 5000x5000 foot grid. There are currently three data resources in this folder - 1) Phase 1 LiDAR-derived DEMs at a 5 foot resolution, 2) Phase 2 LiDAR-derived DEMs at a 2 foot resolution, and 3) Phase 3 LiDAR-derived DEMs at a 2 foot resolution. All data has been converted to a Cloud Optimized GeoTIFF (COG) format. Phase 2 is now complete however Phase 3 efforts are still underway. + - Description: LiDAR-derived digital elevation models (DEM) for the Commonwealth of Kentucky organized in a 5000x5000 foot grid. There are currently three data resources in this folder - 1) Phase 1 LiDAR-derived DEMs at a 5 foot resolution, 2) Phase 2 LiDAR-derived DEMs at a 2 foot resolution, and 3) Phase 3 LiDAR-derived DEMs at a 2 foot resolution. All data has been converted to a Cloud Optimized GeoTIFF (COG) format. Phase 3 efforts are still underway. ARN: arn:aws:s3:::kyfromabove/elevation/DEM/ Region: us-west-2 Type: S3 Bucket RequesterPays: False Explore: + - '[KyFromAbove Stac-Browser - Phase 1](https://kygeonet.ky.gov/collections/dem-phase1)' + - '[KyFromAbove Stac-Browser - Phase 2](https://kygeonet.ky.gov/collections/dem-phase2)' + - '[KyFromAbove Stac-Browser - Phase 3](https://kygeonet.ky.gov/collections/dem-phase3)' + - '[STAC V1.0.0 endpoint](https://spved5ihrl.execute-api.us-west-2.amazonaws.com/)' - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/DEM/)' - - Description: There are three data resources in this folder - 1) KyTopo Map Series quadrangles in a Cloud Optimized GeoTIFF (COG) format, 2) KyTopo Map Series quadrangles with all collar information in a non-georeferenced PNG format for printing on a standard ARCH-D sized sheet, and 3) the KyTopo Map Series quadrangles tile grid in a geopackage format. The COGs were created using GDAL with JPEG compression at a 90% quality setting and the default 512x512 tile setting. + - Description: There are three data resources in this folder - 1) KyTopo Map Series quadrangles in a COG format, 2) KyTopo Map Series quadrangles with all collar information in a non-georeferenced PNG format for printing on a standard ARCH-D sized sheet, and 3) the KyTopo Map Series quadrangles tile grid in a geopackage format. The COGs were created using GDAL with JPEG compression at a 90% quality setting and the default 512x512 tile setting. ARN: arn:aws:s3:::kyfromabove/elevation/KyTopoMapSeries/ Region: us-west-2 Type: S3 Bucket RequesterPays: False Explore: - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/KyTopoMapSeries/)' - - Description: LiDAR-derived Point Cloud tiles for the Commonwealth of Kentucky organized in a 5000x5000 foot grid. There is currently one data resource in this folder - 1) Phase 1 LiDAR-derived point clouds in LAZ format. Phase 2 is now complete however Phase 3 efforts are still underway It is our aim to provide Phase 2 and Phase 3 data in a COPC (LAZ format). + - Description: LiDAR-derived Point Cloud tiles for the Commonwealth of Kentucky organized in a 5000x5000 foot grid. There currently two complete resources in this folder - 1) Phase 1 LiDAR-derived point clouds in LAZ format and 2) Phase 2 LiDAR-derived Point Clouds in COPC format. Phase 3 is partially availabe in COPC format while efforts are still ongoing. ARN: arn:aws:s3:::kyfromabove/elevation/PointCloud/ Region: us-west-2 Type: S3 Bucket RequesterPays: False Explore: + - '[KyFromAbove Stac-Browser - Phase 1](https://kygeonet.ky.gov/collections/laz-phase1)' + - '[KyFromAbove Stac-Browser - Phase 2](https://kygeonet.ky.gov/collections/laz-phase2)' + - '[KyFromAbove Stac-Browser - Phase 3](https://kygeonet.ky.gov/collections/laz-phase3)' + - '[STAC V1.0.0 endpoint](https://spved5ihrl.execute-api.us-west-2.amazonaws.com/)' - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/PointCloud/)' - Description: The data in this bucket includes spot elevations for the entire Commonwealth of Kentucky generated from the KyFromAbove Phase 1 LiDAR-derived digital elevation model (DEM) in a geopackage format. ArcGIS was used to create this dataset. Spot elevations for Phase 2 and Phase 3 will be generated upon completion of each Phase. ARN: arn:aws:s3:::kyfromabove/elevation/SpotElevations/ @@ -63,26 +85,34 @@ Resources: RequesterPays: False Explore: - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/SpotElevations/)' - - Description: KyFromAbove aerial imagery, both nadir and oblique views, can be found in this bucket. Phase 1 and 2 data is currently available. Phase 3 oblique imagery is available and ortho imagery will be available in early 2025. + - Description: KyFromAbove aerial imagery, both nadir and oblique views, can be found in this bucket. Ortho imagery is available for Phases 1, 3, and 3. Oblique imagery is available for Phase 3 only. ARN: arn:aws:s3:::kyfromabove/imagery/ Region: us-west-2 Type: S3 Bucket RequesterPays: False Explore: + - '[KyFromAbove Stac-Browser - Phase 1 Orthos](https://kygeonet.ky.gov/collections/orthos-phase1)' + - '[KyFromAbove Stac-Browser - Phase 2 Orthos](https://kygeonet.ky.gov/collections/orthos-phase2)' + - '[KyFromAbove Stac-Browser - Phase 3 Orthos](https://kygeonet.ky.gov/collections/orthos-phase3)' + - '[KyFromAbove Explorer oblique-viewer](https://explore.kyfromabove.ky.gov/)' + - '[STAC V1.0.0 endpoint](https://spved5ihrl.execute-api.us-west-2.amazonaws.com/)' - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/)' - - Description: KyFromAbove ortho imagery for the Commonwealth of Kentucky organized in a 5000x5000 foot grid. Each image tile has been converted to a Cloud Optimized GeoTiff format. Phase 1 and 2 data is organized by acquisition year and is currently available for use. Phase 3 is organized by year and season, as imagery is being acquired during the fall and spring leaf-off seasons as sun angle permits. Phase 3 ortho imagery will be available in early 2025. + - Description: KyFromAbove ortho imagery for the Commonwealth of Kentucky organized in a 5000x5000 foot grid. Each image tile has been converted to a COG format. Phase 1 and 2 data are organized by acquisition year and are currently available for use. Phase 3 is organized by year and season, as imagery was acquired during the fall and spring leaf-off seasons as sun angle permitted. ARN: arn:aws:s3:::kyfromabove/imagery/orthos/ Region: us-west-2 Type: S3 Bucket RequesterPays: False - Explore: + Explore: + - '[KyFromAbove Stac-Browser](https://kygeonet.ky.gov/stac)' + - '[STAC V1.0.0 endpoint](https://spved5ihrl.execute-api.us-west-2.amazonaws.com/)' - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/orthos/)' - - Description: KyFromAbove oblique imagery can be found in this folder. The four oblique views associated with each ortho image are provided in a 3-band (RGB) Cloud Optimized GeoTiff format using the default 512x512 tile setting. There are no oblique images available for Phase 1 and 2. Phase 3 data is available for the entire state. It is organized by year and season (where Season1 = Spring and Season2 = Fall) as imagery is being acquired during the fall and spring leaf-off seasons as sun angle and weather conditions permit. + - Description: KyFromAbove oblique imagery can be found in this folder. The four oblique views associated with each ortho image are provided in a 3-band (RGB) COG format using the default 512x512 tile setting. There are no oblique images available for Phases 1 and 2. Phase 3 data is available for the entire state. It is organized by year and season (where Season1 = Spring and Season2 = Fall) as imagery was acquired during the fall and spring leaf-off seasons as sun angle and weather conditions permitted. ARN: arn:aws:s3:::kyfromabove/imagery/obliques/ Region: us-west-2 Type: S3 Bucket RequesterPays: False Explore: + - '[KyFromAbove Explorer](https://explore.kyfromabove.ky.gov/)' - '[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/obliques/)' DataAtWork: Tutorials: @@ -96,8 +126,17 @@ DataAtWork: URL: https://github.com/ianhorn/kyfromabove-on-aws-examples/blob/main/examples/clip_tiles_to_boundary.ipynb NotebookURL: https://github.com/ianhorn/kyfromabove-on-aws-examples/blob/main/examples/clip_tiles_to_boundary.ipynb AuthorName: Ian Horn + AuthorURL: https://www.linkedin.com/in/ian-horn-503b1022/ Services: - Amazon S3 + Tools & Applications: + - Title: Kentucky From Above SpatioTemporal Asset Catalog + URL: https://kygeonet.ky.gov/stac + AuthorName: Ian Horn, Ky Div. of Geographic Information + - Title: KyFromAbove Explorer + URL: https://explore.kyfromabove.ky.gov/ + AuthorName: NV5 + AuthorURL: https://www.nv5.com/geospatial/ Publications: - Title: A New View of Kentucky's Cities URL: https://www.mydigitalpublication.com/publication/?m=16892&i=816848&view=articleBrowser&article_id=4737566&ver=html5 diff --git a/datasets/ladi.yaml b/datasets/ladi.yaml index 3f3492235..06771af0f 100644 --- a/datasets/ladi.yaml +++ b/datasets/ladi.yaml @@ -5,6 +5,10 @@ Contact: ladi-dataset-admin@mit.edu ManagedBy: "[MIT Lincoln Laboratory Humanitarian Assistance and Disaster Relief group](https://www.ll.mit.edu/r-d/biotechnology-and-human-systems/humanitarian-assistance-and-disaster-relief-systems)" UpdateFrequency: Periodically License: Creative Commons Attribution 4.0 International (CC BY 4.0) +Collabs: + ASDI: + Tags: + - disaster response Tags: - aws-pds - aerial imagery diff --git a/datasets/lemat-rho.yaml b/datasets/lemat-rho.yaml new file mode 100644 index 000000000..022bd4375 --- /dev/null +++ b/datasets/lemat-rho.yaml @@ -0,0 +1,54 @@ +Name: LeMat-Rho +Description: Charge densities and other raw VASP files from density functional theory calculations of equilibrium materials in LeMat-Bulk and non-equilibrium materials from MAD dataset. +Documentation: https://github.com/LeMaterial/LeMat-Rho/tree/main/docs +Contact: info@entalpic.ai +ManagedBy: "[LeMaterial](http://lematerial.org)" +UpdateFrequency: Continuously, as calculated +Tags: + - chemistry + - materials science + - machine learning + - physics + - crystallography + - density functional theory +License: BY CC 4.0 +Citation: +Resources: Raw Data + - Description: Raw, gzipped VASP calculations for all materials calculated + ARN: + Region: + Type: S3 Bucket + Explore: +DataAtWork: + Tutorials: + - Title: Accessing Data in LeMat-Rho AWS OpenData Repository + URL: https://github.com/LeMaterial/LeMat-Rho/blob/feat/aws-upload/scripts/aws-open-data.ipynb + NotebookURL: https://github.com/LeMaterial/LeMat-Rho/blob/feat/aws-upload/scripts/aws-open-data.ipynb + AuthorName: Martin Siron, Mathilde Franckel, Jonathan Schmidt, Richard Tran, Daniel Speckhard, Georgia Channing, Guilherme Penedo + Tools & Applications: + - Title: Pymatgen + URL: https://pymatgen.org + AuthorName: Materials Project + AuthorURL: https://materialsproject.org + - Title: Atomate2 + URL: https://materialsproject.github.io/atomate2 + AuthorName: Materials Project + AuthorURL: https://materialsproject.org + - Title: FireWorks + URL: https://materialsproject.github.io/fireworks + AuthorName: Materials Project + AuthorURL: https://materialsproject.org + - Title: MP-PyRho + URL: https://github.com/materialsproject/pyrho + AuthorName: MaterialsProject + AuthorURL: https://materialsproject.org + Publications: + - Title: + URL: + AuthorName: + AuthorURL: +ADXCategories: + - Education + - Public Sector & Government + - Technology + - Manufacturing diff --git a/datasets/loc-sanborn-maps.yaml b/datasets/loc-sanborn-maps.yaml new file mode 100644 index 000000000..a7fce8f39 --- /dev/null +++ b/datasets/loc-sanborn-maps.yaml @@ -0,0 +1,69 @@ +--- +Name: Sanborn Maps Data Package +Description: The dataset contains metadata records for 50,600 maps from the + [Sanborn Fire Insurance Maps + collection](https://www.loc.gov/collections/sanborn-maps/) and their + corresponding 440,048 JPEG images. The Sanborn collection at Library of + Congress includes over fifty thousand editions of fire insurance maps + comprising almost seven hundred thousand individual sheets. The Library of + Congress holdings represent the largest extant collection of maps produced by + the Sanborn Map Company. +Documentation: https://data.labs.loc.gov/sanborn/ +Contact: For curatorial questions about the content of the collection and + formats, contact the Library of Congress Geography and Map Division at + https://ask.loc.gov/map-geography. For technical questions about access, + contact LC-Labs@loc.gov +ManagedBy: "[Library of Congress](https://www.loc.gov/)" +UpdateFrequency: As new and significant changes to the underlying digital collection occurs +Tags: + - aws-pds + - archives + - cities + - computer vision + - conservation + - culture + - cultural preservation + - demographics + - digital assets + - geospatial + - history + - housing + - land use + - mapping + - urban +License: The content of the Library of Congress online Sanborn Maps Collection + is in the public domain and is free to use and reuse. For more information, + see + https://www.loc.gov/collections/sanborn-maps/about-this-collection/rights-and-access/. +Resources: + - Description: Sanborn Maps data + ARN: arn:aws:s3:::loc-sanborn-maps + Region: us-west-2 + Type: S3 Bucket + Explore: + - "[Browse Bucket by + State](https://loc-sanborn-maps.s3.amazonaws.com/maps-by-state/index.html)" + - "[README](https://loc-sanborn-maps.s3.amazonaws.com/README.html)" +DataAtWork: + Tutorials: + - Title: README data cover sheet + URL: https://loc-sanborn-maps.s3.amazonaws.com/README.html + AuthorName: Library of Congress + - Title: Sanborn Map Data Python Tutorial (Jupyter notebook) + URL: https://libraryofcongress.github.io/data-exploration/Data%20Packages/sanborn.html + AuthorName: Library of Congress + AuthorURL: https://github.com/LibraryOfCongress + - Title: "Fire Insurance Maps at the Library of Congress: A Resource Guide" + URL: https://guides.loc.gov/fire-insurance-maps/introduction + AuthorName: Julie Stoner, Reference Librarian, Geography and Map Division, + Library of Congress + Tools & Applications: + - Title: Sanborn Atlas Volume Finder + URL: https://loc.maps.arcgis.com/apps/instant/media/index.html?appid=0cb2c04324a0413081e1b793ea18f854 + AuthorName: Julie Stoner and Meagan Snow, Geography and Map Division, Library of + Congress + AuthorURL: https://github.com/aarande + Publications: + - Title: Introduction to the Collection + URL: https://www.loc.gov/collections/sanborn-maps/articles-and-essays/introduction-to-the-collection/ + AuthorName: Walter W. Ristow diff --git a/datasets/longbench.yaml b/datasets/longbench.yaml new file mode 100644 index 000000000..7f653856c --- /dev/null +++ b/datasets/longbench.yaml @@ -0,0 +1,34 @@ +Name: LongBench - cross-platform reference dataset profiling cancer cell lines with bulk and single-cell approaches +Description: > + LongBench is a comprehensive benchmark dataset of the latest long-read transcriptomics technologies from Oxford Nanopore (ON) and Pacific Biosciences, alongside a comparison with next-generation sequencing from Illumina. We generated bulk and single-cell libraries from lung cancer cell lines which include different cancer subtypes to capture real biological variation. To further compare and assess sequencing platform performance, Sequins and SIRVs (Set 4) synthetic spike-ins have been included. +Documentation: https://github.com/mritchielab/LongBench.io +Contact: mritchie@wehi.edu.au +ManagedBy: Richie Lab, Walter and Eliza Hall Institute of Medical Research +UpdateFrequency: New data will be added as soon as they are available. +Tags: + - benchmark + - long read sequencing + - single-cell transcriptomics + - short read sequencing + - bioinformatics + - fastq + - bam + - vcf + - cancer + - life sciences + - aws-pds +License: CC BY-4.0 +Resources: + - Description: Bulk, single-cell, and single-nucleus RNA-seq data from the LongBench project, covering eight human lung cancer cell lines. Bulk sequencing (FASTQ) was performed on ONT PCR-cDNA, ONT direct RNA (including pod5 files for RNA modification analysis), PacBio Kinnex, and Illumina platforms. Single-cell and single-nucleus sequencing (FASTQ) was performed on ONT PCR-cDNA, PacBio Kinnex, and Illumina platforms. Aligned reads (BAM), variant calls (VCF), and processed gene expression data are also provided, along with reference genome annotations (GTF and FASTA). + ARN: arn:aws:s3:::longbench-data + Region: ap-southeast-2 + Type: S3 Bucket + +DataAtWork: + Tutorials: + - Title: Benchmarking long-read DE gene and transcript analysis with edgeR + URL: https://mritchielab.github.io/LongBench.io/bulk-de-benchmarking/ + AuthorName: Yupei You + +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/lwi-model-data.yaml b/datasets/lwi-model-data.yaml new file mode 100644 index 000000000..0be8b8d23 --- /dev/null +++ b/datasets/lwi-model-data.yaml @@ -0,0 +1,97 @@ +Name: Louisiana Watershed Initiative (LWI) Model Data +Description: > + Geographic (land cover, land elevation, etc.), meteorologic (pluvial, wind, etc.), + hydrologic (fluvial, tidal, etc.), hydrodynamic (water surface elevations, flow velocities), + and built environment (structures, levees, floodgates, culverts) data used as inputs to and + outputs from numerical modeling software for the prediction of flood risk in stochastic and + probabilistic frameworks. This data was collected from open sources, such as from the + National Oceanographic and Atmospheric Administration (NOAA) or the + United States Geological Survey (USGS). The format of these data is modified to suit the + needs of the modeling program and software, and then used to predict flooding + in Louisiana across a range of scenarios. The modeling software used to predict + flooding which utilizes and creates this data is freely available from the + United States Army Corps of Engineers Hydrologic Engineering Center’s + Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS). + All data is made public by the State of Louisiana for the benefit of its citizens. + This flood prediction data can be used by federal, state, and local + decision makers as well as private citizens to assess the flood risk they face and + make sound science-based decisions for response and adaptation. +Contact: endmc@thewaterinstitute.org +ManagedBy: The Water Institute +UpdateFrequency: yearly +Documentation: https://watershed.la.gov/modeling-program +Tags: + - forecast + - bathymetry + - climate + - coastal + - disaster response + - elevation + - floods + - geospatial + - hydrologic model + - hydrology + - infrastructure + - land cover + - land use + - mapping + - meteorological + - model + - open source software + - precipitation + - simulations + - sustainability + - water + - weather + - aws-pds +License: https://creativecommons.org/licenses/by/4.0/ with attribution to Louisiana Watershed Council +Citation: "Louisiana Watershed Initiative Model Data. Louisiana Watershed Council. 2025. https://lwi.endmc.org/" +Resources: + - Description: Model Applications and Simulations + ARN: arn:aws:s3:::lwi-model-data #placeholder + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse the bucket](https://lwi-model-data.s3.amazonaws.com/index.html)' + - '[Disover the data and models. The ID of the resources can be used to explore the data on the AWS bucket.](https://lwi.endmc.org/)' +DataAtWork: + Tutorials: + - Title: ENDMC Documentation + URL: https://lwi.endmc.org/help/help_index + AuthorName: The Water Institute + AuthorURL: https://thewaterinstitute.org/ + Tools & Applications: + - Title: LWI ENDMC Datan and Model Catalog. + URL: https://lwi.endmc.org/ + AuthorName: The Water Institute + AuthorURL: https://thewaterinstitute.org/ + - Title: HEC-HMS + URL: https://www.hec.usace.army.mil/software/hec-hms/ + AuthorName: United States Army Corps of Engineers + AuthorURL: https://www.usace.army.mil/ + - Title: HEC-HMS documentation + URL: https://www.hec.usace.army.mil/software/hec-hms/documentation.aspx + AuthorName: United States Army Corps of Engineers + AuthorURL: https://www.usace.army.mil/ + - Title: HEC-RAS + URL: https://www.hec.usace.army.mil/software/hec-ras/ + AuthorName: United States Army Corps of Engineers + AuthorURL: https://www.usace.army.mil/ + - Title: HEC-RAS documentation + URL: https://www.hec.usace.army.mil/software/hec-ras/documentation.aspx + AuthorName: United States Army Corps of Engineers + AuthorURL: https://www.usace.army.mil/ + - Title: HEC-FIA + URL: https://www.hec.usace.army.mil/software/hec-fia/ + AuthorName: United States Army Corps of Engineers + AuthorURL: https://www.usace.army.mil/ + - Title: HEC-FIA documentation + URL: https://www.hec.usace.army.mil/software/hec-fia/documentation.aspx + AuthorName: United States Army Corps of Engineers + AuthorURL: https://www.usace.army.mil/ + - Title: go-consequences + URL: https://github.com/USACE/go-consequences + AuthorName: United States Army Corps of Engineers + AuthorURL: https://www.usace.army.mil/ +ADXCategories: + - Environmental Data \ No newline at end of file diff --git a/datasets/mapping-africa.yaml b/datasets/mapping-africa.yaml index 0e0ff37cb..9dbfda5f5 100644 --- a/datasets/mapping-africa.yaml +++ b/datasets/mapping-africa.yaml @@ -9,6 +9,10 @@ Documentation: https://github.com/agroimpacts/mapping-africa Contact: mappingafrica@clarku.edu ManagedBy: "[The Agricultural Impacts Research Group](https://agroimpacts.info/)" UpdateFrequency: "New maps are added as developed" +Collabs: + ASDI: + Tags: + - agriculture Tags: - aws-pds - agriculture diff --git a/datasets/marine-energy-data.yaml b/datasets/marine-energy-data.yaml index 94fb299f0..9e43b77c1 100644 --- a/datasets/marine-energy-data.yaml +++ b/datasets/marine-energy-data.yaml @@ -7,7 +7,7 @@ Description: | This data lake is a sister-data lake to the Department of Energy’s Open Energy Data Initiative (OEDI) data lake. Documentation: https://github.com/openEDI/documentation/ Contact: https://github.com/openEDI/documentation/issues -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: As needed Collabs: ASDI: @@ -38,6 +38,18 @@ Resources: Type: S3 Bucket Explore: - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=pacwave%2F)' + - Description: "[Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) Dataset](https://mhkdr.openei.org/submissions/507)" + ARN: arn:aws:s3:::marine-energy-data/umsli/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=umsli%2F)' + - Description: "[High Resolution Tidal Hindcast (US Tidal) Dataset](https://mhkdr.openei.org/submissions/632)" + ARN: arn:aws:s3:::marine-energy-data/us-tidal/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=marine-energy-data&prefix=us-tidal%2F)' DataAtWork: Tools & Applications: Publications: diff --git a/datasets/mbers-open-data.yaml b/datasets/mbers-open-data.yaml new file mode 100644 index 000000000..14ae6586a --- /dev/null +++ b/datasets/mbers-open-data.yaml @@ -0,0 +1,29 @@ +Name: Marginal Build Emissions Rates (MBERs) for Electricity +Description: The Climate TRACE coalition has developed and maintains free global hourly Build Margin data, also known as MBERs, that are compliant with the Greenhouse Gas Protocol's Project Protocol electricity sector guidance, Guidelines for Grid-Connected Electricity Projects ("GHGP Guidelines"). +Documentation: https://github.com/WattTime/mbers-open-data/blob/main/MBER_Data_Summary_and_Methodology.pdf +Contact: The annual and hourly MBERs data are created and maintained by the Climate TRACE coalition of nonprofits, universities, and tech companies. The largest contributors to the coalition's electricity sector work are WattTime, Transition Zero, Global Energy Monitor, Pixel Scientia Labs, Planet Labs, and Georgetown University. For questions or more information about MBER data, contact coalition@ClimateTRACE.org or visit https://climatetrace.org/contact. +ManagedBy: Climate TRACE +UpdateFrequency: Annually +Tags: + - aws-pds + - carbon + - climate + - csv + - electricity + - energy + - energy modeling + - environmental +License: All data are free and provided without license restrictions. +Citation: "Marginal Build Emissions Rates (MBERs) for Electricity. Climate TRACE. [DATE]. URL: https://www.gem.wiki/MBERs" +Resources: + - Description: Marginal Build Emissions Rates (MBERs) for Electricity CSV Data + ARN: arn:aws:s3:::mbers-open-data + Region: us-west-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: MBER Orientation and Tutorial + URL: https://github.com/WattTime/mbers-open-data/blob/main/MBER_Orientation_and_Tutorial.pdf + AuthorName: Climate TRACE +ADXCategories: + - Environmental Data diff --git a/datasets/mosaic.yaml b/datasets/mosaic.yaml new file mode 100644 index 000000000..8cfdfb6f0 --- /dev/null +++ b/datasets/mosaic.yaml @@ -0,0 +1,45 @@ +Name: Meta-Organized Stimuli And fMRI Imaging data for Computational modeling (MOSAIC) +Description: This extensible dataset, MOSAIC, aggregates individual functional magnetic resonance imaging (fMRI) datasets by leveraging a shared preprocessing pipeline and stimulus curation procedure. This dataset aggregation procedure achieves the scale necessary for neural network training and the diversity needed for generalizable results. +Documentation: https://blahner.github.io/MOSAICfmri/ +Contact: blahner@mit.edu +ManagedBy: Massachusetts Institute of Technology, Georgia Tech +UpdateFrequency: New data is uploaded as researchers preprocess their fMRI data according to MOSAIC format and submit. +Tags: + - aws-pds + - brain images + - brain models + - hdf5 + - neuroimaging + - neuroscience + - machine learning +License: CC BY 4.0 +Citation: +Resources: + - Description: HDF5 files containing preprocessed fMRI data + ARN: arn:aws:s3:::mosaicfmri + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://mosaicfmri.s3.amazonaws.com/index.html)' +DataAtWork: + Tutorials: + - Title: Preprocess fMRI datasets with MOSAIC shared pipeline + URL: https://github.com/blahner/mosaic-preprocessing + AuthorName: Benjamin Lahner + - Title: MOSAIC Python package (mosaic-dataset) + URL: https://pypi.org/project/mosaic-dataset/ + AuthorName: Mayukh Deb + - Title: Download MOSAIC data, visualize fMRI responses, load and run brain-optimized models (Jupyter notebook) + URL: https://github.com/murtylab/mosaic-dataset/blob/master/examples/mosaic-starter.ipynb + NotebookURL: https://github.com/murtylab/mosaic-dataset/blob/master/examples/mosaic-starter.ipynb + AuthorName: Mayukh Deb + - Title: Run a synthetic localizer experiment using MOSAIC's brain-optimized models (Jupyter notebook) + URL: https://github.com/murtylab/mosaic-dataset/blob/master/examples/mosaic_synthetic_localizer.ipynb + NotebookURL: https://github.com/murtylab/mosaic-dataset/blob/master/examples/mosaic_synthetic_localizer.ipynb + AuthorName: Benjamin Lahner + - Title: Load HDF5 file (Jupyter notebook) + URL: https://github.com/blahner/mosaic-preprocessing/blob/main/src/fmriDatasetPreparation/create_hdf5/load_hdf5.ipynb + NotebookURL: https://github.com/blahner/mosaic-preprocessing/blob/main/src/fmriDatasetPreparation/create_hdf5/load_hdf5.ipynb + AuthorName: Benjamin Lahner +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/nasa-airibrad.yaml b/datasets/nasa-airibrad.yaml new file mode 100644 index 000000000..74c3eae00 --- /dev/null +++ b/datasets/nasa-airibrad.yaml @@ -0,0 +1,41 @@ +Name: AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V005 (AIRIBRAD) at GES DISC +Description: |- + WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space. + + The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K. + + Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING. + + The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_005 products are stored in files (often referred to as "granules") that contain 6 minutes of data, 90 footprints across track by 135 lines along track. + Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/YZEXEVN4JGGJ +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov' +ManagedBy: NASA +UpdateFrequency: From 2002-08-30 to Ongoing +Tags: + - aws-pds + - atmosphere + - datacenter + - earth observation + - global + - hdf + - ice + - land + - metadata + - opendap + - orbit +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances + V005 (AIRIBRAD) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level1/AIRIBRAD.005/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-airicrad.yaml b/datasets/nasa-airicrad.yaml new file mode 100644 index 000000000..0a10a5e09 --- /dev/null +++ b/datasets/nasa-airicrad.yaml @@ -0,0 +1,56 @@ +Name: AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC +Description: |- + The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research. + + This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct + to this page. For this collection the switchover occurred on June 1, 2020. + Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/VWD3DRC07UEN +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 2002-08-30 to Ongoing +Tags: + - aws-pds + - atmosphere + - climate + - datacenter + - earth observation + - global + - metadata + - opendap + - orbit + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 + (AIRICRAD) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/Aqua_AIRS_Level1/AIRICRAD.6.7/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: AIRS version 6.6 and version 7 level-1C products + URL: https://doi.org/10.1117/12.2529400 + AuthorName: Evan M. Manning, L. Larrabee Strow, and Hartmut H. Aumann + - Title: AIRS Level-1C and applications to cross-calibration with MODIS and CrIS + URL: https://doi.org/10.1117/12.2061967 + AuthorName: Evan M. Manning, Hartmut H. Aumann, and Ali Behrangi + - Title: Validation of the Atmospheric Infrared Sounder radiative transfer algorithm + URL: https://doi.org/10.1029/2005JD006146 + AuthorName: Strow, L.L, Hannon, S.E, De-Souza Machado, S., Motteler, H.E., and + Tobin, D.C. + - Title: Radiometric Stability Validation of 17 Years of AIRS Data Using Sea Surface + Temperatures. + URL: https://doi.org/10.1029/2019GL085098 + AuthorName: Aumann, H.H. Brogerg, S.,Manning, E., and Pagaino, T. + - Title: Updates to the absolute radiometric accuracy of the AIRS on Aqua + URL: https://doi.org/10.1117/12.2324605 + AuthorName: Pagano, T.S., Aumann, H.H., Broberg, S., Manning, E., Overoye, K., + and Weiler, M. + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-astl1t.yaml b/datasets/nasa-astl1t.yaml new file mode 100644 index 000000000..482808fe0 --- /dev/null +++ b/datasets/nasa-astl1t.yaml @@ -0,0 +1,47 @@ +Name: ASTER Level 1T Precision Terrain Corrected Registered At-Sensor Radiance V004 +Description: |- + The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B ([AST_L1B](https://doi.org/10.5067/ASTER/AST_L1B.004)) that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A ([AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.004)) product. The bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3. The AST_L1T product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands. + + The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). + + For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution as both a text file and single band browse images with the valid GCPs overlaid. + + This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding [AST_L1A](https://doi.org/10.5067/ASTER/AST_L1A.004) dataset. + + Known Issues + + * Since October 1, 2017, a correction addresses zero-filled scans in low-latitude, ascending orbit (nighttime) TIR data. Additional details are available in the ASTER L1T User Advisory. + * Data from the SWIR bands collected after April 2008 may show anomalous saturation and striping. See the ASTER SWIR User Advisory for further information. + + Improvements/Changes from Previous Versions + + * Enhanced Geolocation Accuracy: Version 4 uses Collection 2 Ground Control Points (GCPs) compared against Global Land Survey (GLS) 2000 standards to improve positional accuracy. + * Radiometric Calibration Update: Version 4 applies Radiometric Calibration Coefficient Version 5 (RCC V5) to improve the radiometric accuracy of the raw DNs, based on research by [Tsuchida and others (2020)](https://doi.org/10.3390/rs12030427), published in Remote Sensing. + Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/ASTER/AST_L1T.004 +Contact: 'User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact' +ManagedBy: NASA +UpdateFrequency: From 2000-03-04 to Ongoing (Varies) +Tags: + - aws-pds + - cog + - earth observation + - global + - land + - orbit + - cog +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'ASTER Level 1T Precision Terrain Corrected Registered At-Sensor + Radiance V004.' + ARN: arn:aws:s3:::lp-prod-protected/AST_L1T.004 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC diff --git a/datasets/nasa-atl03.yaml b/datasets/nasa-atl03.yaml new file mode 100644 index 000000000..95cc3aa71 --- /dev/null +++ b/datasets/nasa-atl03.yaml @@ -0,0 +1,31 @@ +Name: ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 +Description: |- + This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. + Read our doc on how to get AWS Credentials to retrieve this data: https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/ATLAS/ATL03.006 +Contact: 'Email: nsidc@nsidc.org. Home Page: https://nsidc.org/daac' +ManagedBy: NASA +UpdateFrequency: From 2018-10-13 to Ongoing +Tags: + - aws-pds + - atmosphere + - datacenter + - earth observation + - global + - hdf + - ice + - land + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006.' + ARN: arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL03/006 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.nsidc.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Accessing and working with ICESat-2 data in the cloud + URL: https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/notebooks/ICESat-2_Cloud_Access/ATL06-direct-access.ipynb + AuthorName: Andy Barrett, Jennifer Roebuck, Amy Steiker diff --git a/datasets/nasa-atl08.yaml b/datasets/nasa-atl08.yaml new file mode 100644 index 000000000..8cdcbb0a5 --- /dev/null +++ b/datasets/nasa-atl08.yaml @@ -0,0 +1,31 @@ +Name: ATLAS/ICESat-2 L3A Land and Vegetation Height V006 +Description: |- + This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. + Read our doc on how to get AWS Credentials to retrieve this data: https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/ATLAS/ATL08.006 +Contact: 'NASA NSIDC DAAC: nsidc@nsidc.org. Home Page: https://nsidc.org/daac' +ManagedBy: NASA +UpdateFrequency: From 2018-10-14 to Ongoing +Tags: + - aws-pds + - atmosphere + - datacenter + - earth observation + - global + - ice + - land + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'ATLAS/ICESat-2 L3A Land and Vegetation Height V006.' + ARN: arn:aws:s3:::nsidc-cumulus-prod-protected/ATLAS/ATL08/006 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.nsidc.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: Accessing and working with ICESat-2 data in the cloud + URL: https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/notebooks/ICESat-2_Cloud_Access/ATL06-direct-access_rendered.ipynb + AuthorName: Andy Barrett, Jennifer Roebuck, and Amy Steiker. \ No newline at end of file diff --git a/datasets/nasa-gedi02a.yaml b/datasets/nasa-gedi02a.yaml new file mode 100644 index 000000000..3be6d5e48 --- /dev/null +++ b/datasets/nasa-gedi02a.yaml @@ -0,0 +1,69 @@ +Name: GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002 +Description: |- + The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting. + + The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024. + + The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters. + + The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary. + + Known Issues + + * Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8). + * Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this [document](https://lpdaac.usgs.gov/documents/2236/GEDI_CORRECTED_RGT_FILENAMES.pptx) for the correct RGT numbers. + * Known Issues: Section 8 of the User Guide provides additional information on known issues. + + Improvements/Changes from Previous Versions + + * Metadata has been updated to include spatial coordinates. + * Granule size has been reduced from one full ISS orbit (~5.83 GB) to four segments per orbit (~1.48 GB). + * Filename has been updated to include segment number and version number. + * Improved geolocation for an orbital segment. + * Added elevation from the SRTM digital elevation model for comparison. + * Modified the method to predict an optimum algorithm setting group per laser shot. + * Added additional land cover datasets related to phenology, urban infrastructure, and water persistence. + * Added selected_mode_flag dataset to root beam group using selected algorithm. + * Removed shots when the laser is not firing. + * Modified file name to include segment number and dataset version. + Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/GEDI/GEDI02_A.002 +Contact: 'User Services: lpdaac@usgs.gov' +ManagedBy: NASA +UpdateFrequency: From 2019-04-04 to 2023-03-16 (Varies) +Tags: + - aws-pds + - biodiversity + - carbon + - datacenter + - earth observation + - energy + - global + - hdf + - ice + - land + - land cover + - lidar + - metadata + - orbit + - urban + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GEDI L2A Elevation and Height Metrics Data Global Footprint Level + V002.' + ARN: arn:aws:s3:::lp-prod-protected/GEDI02_A.002 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: 'How to: Find and Access GEDI Data' + URL: https://github.com/nasa/GEDI-Data-Resources/blob/main/python/tutorials/how-to-find-and-access-GEDI-data_earthaccess.ipynb + AuthorName: Land Processes Distributed Active Archive Center (LP DAAC) + AuthorURL: https://lpdaac.usgs.gov/ + - Title: Getting Started with GEDI L2A Version 2 Data in Python + URL: https://github.com/nasa/GEDI-Data-Resources/blob/main/python/tutorials/GEDI_L2A_V2_Tutorial.ipynb + AuthorName: Land Processes Distributed Active Archive Center (LP DAAC) + AuthorURL: https://lpdaac.usgs.gov/ diff --git a/datasets/nasa-gedil4aagbdensityv212056.yaml b/datasets/nasa-gedil4aagbdensityv212056.yaml new file mode 100644 index 000000000..e63541c8c --- /dev/null +++ b/datasets/nasa-gedil4aagbdensityv212056.yaml @@ -0,0 +1,32 @@ +Name: GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1 +Description: |- + This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2024-11-27. No acquisitions occurred while the GEDI instrument was in storage on the International Space Station (ISS) from March 2023 to April 2024. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group. Model inputs include the scaled and transformed GEDI L2A RH metrics, footprint geolocation variables and land cover input data including PFTs and the world region identifiers. Additional model outputs include the AGBD predictions for each of the six GEDI L2A algorithm setting groups with AGBD in natural and transformed units and associated prediction uncertainty for each GEDI L2A algorithm setting group. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. Also provided are outputs of parameters and variables from the L4A models used to generate AGBD predictions that are required as input to the GEDI04_B algorithm to generate 1-km gridded products. + Read our doc on how to get AWS Credentials to retrieve this data: https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.3334/ORNLDAAC/2056 +Contact: 'ORNL DAAC User Services Office: uso@daac.ornl.gov.' +ManagedBy: NASA +UpdateFrequency: From 2019-04-17 to 2024-11-27 +Tags: + - aws-pds + - earth observation + - ecosystems + - global + - land + - land cover + - lidar + - opendap + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1.' + ARN: arn:aws:s3:::ornl-cumulus-prod-protected/gedi/GEDI_L4A_AGB_Density_V2_1/data + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.ornldaac.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: Searching and Downloading GEDI L4A Dataset + URL: https://github.com/ornldaac/gedi_tutorials/blob/main/notebooks/gedi_l4a_search_download.ipynb + AuthorName: Rupesh Shrestha diff --git a/datasets/nasa-gpm2adpr.yaml b/datasets/nasa-gpm2adpr.yaml new file mode 100644 index 000000000..6bd1cf114 --- /dev/null +++ b/datasets/nasa-gpm2adpr.yaml @@ -0,0 +1,44 @@ +Name: GPM DPR Precipitation Profile L2A 1.5 hours 5 km V07 (GPM_2ADPR) at GES DISC +Description: |- + Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. + . + + 2ADPR provides single- and dual-frequency-derived precipitation estimates from the Ku and Ka radars of the Dual-Frequency Precipitation Radar (DPR) on the core GPM spacecraft. The output consists of three main classes of precipitation products: those derived from the Ku-band frequency over a wide swath (245 km), those derived from the Ka-band frequency over a narrow swath (125 km), and those derived from the dual-frequency data over the narrow swath. The Ka-band results are further divided into the standard and high-sensitivity estimates. In the standard sensitivity mode, the fields of view within the inner swath are matched to those of the Ku-band. Data from these matched-beam Ku- and Ka-band fields of view are used to derive the dual-frequency precipitation products. The retrievals are performed at each radar range bin along the slant path of the radar instrument field of view (IFOV). + Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/GPM/DPR/GPM/2A/07 +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 2014-03-08 to Ongoing +Tags: + - aws-pds + - atmosphere + - contamination + - datacenter + - earth observation + - global + - metadata + - opendap + - radar + - water + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GPM DPR Precipitation Profile L2A 1.5 hours 5 km V07 (GPM_2ADPR) + at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L2/GPM_2ADPR.07/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: How to Read IMERG Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. + \ No newline at end of file diff --git a/datasets/nasa-gpm3imergde.yaml b/datasets/nasa-gpm3imergde.yaml new file mode 100644 index 000000000..7ee5ece1f --- /dev/null +++ b/datasets/nasa-gpm3imergde.yaml @@ -0,0 +1,108 @@ +Name: GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDE) at GES DISC +Description: "Version 07 is the current version of the data set. Older versions will + no longer be available and have been superseded by Version 07.\n\nThe Integrated + Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global + surface precipitation rates at a high resolution of 0.1° every half-hour beginning + 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) + mission, using the GPM Core Observatory satellite as the standard to combine precipitation + observations from an international constellation of satellites using advanced techniques. + \ IMERG can be used for global-scale applications as well as over regions with sparse + or no reliable surface observations. The fine spatial and temporal resolution of + IMERG data allows them to be accumulated to the scale of the application for increased + skill. IMERG has three Runs with varying latencies in response to a range of application + needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications + (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). + \ While IMERG strives for consistency and accuracy, satellite estimates of precipitation + are expected to have lower skill over frozen surfaces, complex terrain, and coastal + zones. As well, the changing GPM satellite constellation over time may introduce + artifacts that affect studies focusing on multi-year changes.\n\nThis dataset is + the GPM Level 3 IMERG *Early* Daily 10 x 10 km (GPM_3IMERGDE) derived from the + half-hourly GPM_3IMERGHHE. The derived result represents an early (expedited) estimate + of the daily mean precipitation rate in mm/day. The dataset is produced by first + computing the mean precipitation rate in (mm/hour) in every grid cell, and then + multiplying the result by 24. This minimizes the possible dry bias in versions + before \"07\", in the simple daily totals for cells where less than 48 half-hourly + observations are valid for the day. The latter under-sampling is very rare in the + combined microwave-infrared (and rain gauge in the final) dataset, variable \"precipitation\", + \ and appears in higher latitudes. Thus, in most cases users of global \"precipitation\" + data will not notice any difference. This correction, however, is noticeable in + the high-quality microwave retrieval, variable \"MWprecipitation\", where the occurrence + of less than 48 valid half-hourly samples per day is very common. The counts of + the valid half-hourly samples per day have always been provided as a separate variable, + and users of daily data were advised to pay close attention to that variable and + use it to calculate the correct precipitation daily rates. Starting with version + \"07\", this is done in production to minimize possible misinterpretations of the + data. The counts are still provided in the data, but they are only given to gauge + the significance of the daily rates, and reconstruct the simple totals if someone + wishes to do so. \n\nThe latency of the derived Early daily product is a couple + of minutes after the last granule of GPM_3IMERGHHE for the UTC data day is received + at GES DISC. Since the target latency of GPM_3IMERGHHE is 4 hours, the daily should + appear about 4 hours after the closure of the UTC day. For information on the original + data (GPM_3IMERGHHE), please see the Documentation (Related URL). \n\nThe daily + mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) + in a grid cell for the data day, and then multiplying the result by 24. Thus, for + every grid cell we have \n\nPdaily_mean = SUM{Pi * 1[Pi valid]} + / Pdaily_cnt * 24, i=[1,Nf]\n\nWhere:\nPdaily_cnt = SUM{1[Pi valid]}\n\nPi - + half-hourly input, in (mm/hr)\nNf - Number of half-hourly files per + day, Nf=48\n1[.] - Indicator function; 1 when Pi is valid, 0 otherwise\nPdaily_cnt + \ - Number of valid retrievals in a grid cell per day.\n\nGrid cells for which + Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that Pi=0 is a valid + value.\n\nPdaily_cnt are provided in the data files as variables \"precipitation_cnt\" + and \"MWprecipitation_cnt\", for correspondingly the microwave-IR-gauge and microwave-only + retrievals. They are only given to gauge the significance of the daily rates, and + reconstruct the simple totals if someone wishes to do so. \n\nThere are various + ways the daily error could be estimated from the source half-hourly random error + (variable \"randomError\"). The daily error provided in the data files is calculated + in a fashion similar to the daily mean precipitation rate. First, the mean of the + squared half-hourly \"randomError\" for the day is computed, and the resulting + (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result + in (mm/day):\n\nPerr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err + \ * 24}^0.5, i=[1,Nf]\nNcnt_err = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_i\t\t- + half-hourly input, \"randomError\", (mm/hr)\nPerr_daily\t- Magnitude of the daily + error, (mm/day)\nNcnt_err\t\t- Number of valid half-hour error estimates\n\nAgain, + the sum of squared \"randomError\" can be reconstructed, and other estimates can + be derived using the available counts in the Daily files.\n\n\nRead our doc on how + to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/GPM/IMERGDE/DAY/07 +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 1998-01-01 to Ongoing +Tags: + - aws-pds + - atmosphere + - climate + - coastal + - datacenter + - global + - hydrology + - land + - metadata + - opendap + - netcdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V07 + (GPM_3IMERGDE) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDE.07/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: Precipitation Estimation from Remotely Sensed Imagery Using an Artificial + Neural Network Cloud Classification System + URL: https://doi.org/10.1175/JAM2173.1 + AuthorName: Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao + - Title: Kalman Filter Based CMORPH + URL: https://doi.org/10.1175/JHM-D-11-022.1 + AuthorName: Joyce, R. J., P. Xie, and J. E. Janowiak + - Title: Calculation of Gridded Precipitation Data for the Global Land-Surface + Using In-Situ Gauge Observations + URL: https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf + AuthorName: Rudolf, B., and U. Schneider + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-gpm3imergdf.yaml b/datasets/nasa-gpm3imergdf.yaml new file mode 100644 index 000000000..415e36b1c --- /dev/null +++ b/datasets/nasa-gpm3imergdf.yaml @@ -0,0 +1,107 @@ +Name: GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF) at GES DISC +Description: "Version 07 is the current version of the data set. Older versions will + no longer be available and have been superseded by Version 07.\n\nThe Integrated + Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global + surface precipitation rates at a high resolution of 0.1° every half-hour beginning + 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) + mission, using the GPM Core Observatory satellite as the standard to combine precipitation + observations from an international constellation of satellites using advanced techniques. + \ IMERG can be used for global-scale applications as well as over regions with sparse + or no reliable surface observations. The fine spatial and temporal resolution of + IMERG data allows them to be accumulated to the scale of the application for increased + skill. IMERG has three Runs with varying latencies in response to a range of application + needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications + (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). + \ While IMERG strives for consistency and accuracy, satellite estimates of precipitation + are expected to have lower skill over frozen surfaces, complex terrain, and coastal + zones. As well, the changing GPM satellite constellation over time may introduce + artifacts that affect studies focusing on multi-year changes.\n\nThis dataset is + the GPM Level 3 IMERG *Final* Daily 10 x 10 km (GPM_3IMERGDF) derived from the + half-hourly GPM_3IMERGHH. The derived result represents the Final estimate of the + daily mean precipitation rate in mm/day. The dataset is produced by first computing + the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying + the result by 24. This minimizes the possible dry bias in versions before \"07\", + in the simple daily totals for cells where less than 48 half-hourly observations + are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared + and rain gauge dataset, variable \"precipitation\", and appears in higher latitudes. + Thus, in most cases users of global \"precipitation\" data will not notice any difference. + This correction, however, is noticeable in the high-quality microwave retrieval, + variable \"MWprecipitation\", where the occurrence of less than 48 valid half-hourly + samples per day is very common. The counts of the valid half-hourly samples per + day have always been provided as a separate variable, and users of daily data were + advised to pay close attention to that variable and use it to calculate the correct + precipitation daily rates. Starting with version \"07\", this is done in production + to minimize possible misinterpretations of the data. The counts are still provided + in the data, but they are only given to gauge the significance of the daily rates, + and reconstruct the simple totals if someone wishes to do so. \n\nThe latency of + the derived *Final* Daily product depends on the delivery of the IMERG *Final* Half-Hourly + product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for + the entire month, with up to 4 months latency, so will be the latency for the Final + Daily, plus about 24 hours. Thus, e.g. the Dailies for January can be expected + to appear no earlier than April 2. \n\nThe daily mean rate (mm/day) is derived by + first computing the mean precipitation rate (mm/hour) in a grid cell for the data + day, and then multiplying the result by 24. Thus, for every grid cell we have \n\nPdaily_mean + \ = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]\n\nWhere:\nPdaily_cnt + = SUM{1[Pi valid]}\n\nPi - half-hourly input, in (mm/hr)\nNf - + Number of half-hourly files per day, Nf=48\n1[.] - Indicator function; + 1 when Pi is valid, 0 otherwise\nPdaily_cnt - Number of valid retrievals in + a grid cell per day.\n\nGrid cells for which Pdaily_cnt=0, are set to fill value + in the Daily files.\nNote that Pi=0 is a valid value.\n\nPdaily_cnt are provided + in the data files as variables \"precipitation_cnt\" and \"MWprecipitation_cnt\", + for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are + only given to gauge the significance of the daily rates, and reconstruct the simple + totals if someone wishes to do so. \n\nThere are various ways the daily error could + be estimated from the source half-hourly random error (variable \"randomError\"). + The daily error provided in the data files is calculated in a fashion similar to + the daily mean precipitation rate. First, the mean of the squared half-hourly \"randomError\" + \ for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). + Finally, square root is taken to get the result in (mm/day):\n\nPerr_daily = { SUM{ + (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf]\nNcnt_err = SUM( + 1[Perr_i valid] )\n\nwhere:\nPerr_i\t\t- half-hourly input, \"randomError\", (mm/hr)\nPerr_daily\t- + Magnitude of the daily error, (mm/day)\nNcnt_err\t\t- Number of valid half-hour + error estimates\n\nAgain, the sum of squared \"randomError\" can be reconstructed, + and other estimates can be derived using the available counts in the Daily files.\n\nRead + our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/GPM/IMERGDF/DAY/07 +Contact: 'Email: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 1998-01-01 to Ongoing +Tags: + - aws-pds + - climate + - coastal + - datacenter + - global + - hydrology + - ice + - land + - metadata + - netcdf + - opendap +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 + (GPM_3IMERGDF) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDF.07/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: Precipitation Estimation from Remotely Sensed Imagery Using an Artificial + Neural Network Cloud Classification System + URL: https://doi.org/10.1175/JAM2173.1 + AuthorName: Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao + - Title: Kalman Filter Based CMORPH + URL: https://doi.org/10.1175/JHM-D-11-022.1 + AuthorName: Joyce, R. J., P. Xie, and J. E. Janowiak + - Title: Calculation of Gridded Precipitation Data for the Global Land-Surface + Using In-Situ Gauge Observations + URL: https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf + AuthorName: Rudolf, B., and U. Schneider + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-gpm3imergdl.yaml b/datasets/nasa-gpm3imergdl.yaml new file mode 100644 index 000000000..82c8d32ce --- /dev/null +++ b/datasets/nasa-gpm3imergdl.yaml @@ -0,0 +1,108 @@ +Name: GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDL) at GES DISC +Description: "Version 07 is the current version of the data set. Older versions will + no longer be available and have been superseded by Version 07.\n\nThe Integrated + Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global + surface precipitation rates at a high resolution of 0.1° every half-hour beginning + 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) + mission, using the GPM Core Observatory satellite as the standard to combine precipitation + observations from an international constellation of satellites using advanced techniques. + \ IMERG can be used for global-scale applications as well as over regions with sparse + or no reliable surface observations. The fine spatial and temporal resolution of + IMERG data allows them to be accumulated to the scale of the application for increased + skill. IMERG has three Runs with varying latencies in response to a range of application + needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications + (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). + \ While IMERG strives for consistency and accuracy, satellite estimates of precipitation + are expected to have lower skill over frozen surfaces, complex terrain, and coastal + zones. As well, the changing GPM satellite constellation over time may introduce + artifacts that affect studies focusing on multi-year changes.\n\nThis dataset is + the GPM Level 3 IMERG Late Daily 10 x 10 km (GPM_3IMERGDL) derived from the half-hourly + GPM_3IMERGHHL. The derived result represents a Late expedited estimate of the daily + mean precipitation rate in mm/day. The dataset is produced by first computing the + mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the + result by 24. This minimizes the possible dry bias in versions before \"07\", in + the simple daily totals for cells where less than 48 half-hourly observations are + valid for the day. The latter under-sampling is very rare in the combined microwave-infrared + (and rain gauge in the final) dataset, variable \"precipitation\", and appears + in higher latitudes. Thus, in most cases users of global \"precipitation\" data + will not notice any difference. This correction, however, is noticeable in the high-quality + microwave retrieval, variable \"MWprecipitation\", where the occurrence of less + than 48 valid half-hourly samples per day is very common. The counts of the valid + half-hourly samples per day have always been provided as a separate variable, and + users of daily data were advised to pay close attention to that variable and use + it to calculate the correct precipitation daily rates. Starting with version \"07\", + this is done in production to minimize possible misinterpretations of the data. + The counts are still provided in the data, but they are only given to gauge the + significance of the daily rates, and reconstruct the simple totals if someone wishes + to do so. \n\nThe latency of the derived Late daily product is a couple of minutes + after the last granule of GPM_3IMERGHHL for the UTC data day is received at GES + DISC. Since the target latency of GPM_3IMERGHHL is 14 hours, the daily should appear + no earlier than 14 hours after the closure of the UTC day. For information on the + original data (GPM_3IMERGHHL), please see the Documentation (Related URL). \n\nThe + daily mean rate (mm/day) is derived by first computing the mean precipitation rate + (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. + \ Thus, for every grid cell we have \n\nPdaily_mean = SUM{Pi + * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]\n\nWhere:\nPdaily_cnt = SUM{1[Pi valid]}\n\nPi + \ - half-hourly input, in (mm/hr)\nNf - Number of half-hourly + files per day, Nf=48\n1[.] - Indicator function; 1 when Pi is valid, + 0 otherwise\nPdaily_cnt - Number of valid retrievals in a grid cell per day.\n\nGrid + cells for which Pdaily_cnt=0, are set to fill value in the Daily files.\nNote that + Pi=0 is a valid value.\n\nPdaily_cnt are provided in the data files as variables + \"precipitation_cnt\" and \"MWprecipitation_cnt\", for correspondingly the microwave-IR-gauge + and microwave-only retrievals. They are only given to gauge the significance of + the daily rates, and reconstruct the simple totals if someone wishes to do so. \n\nThere + are various ways the daily error could be estimated from the source half-hourly + random error (variable \"randomError\"). The daily error provided in the data files + is calculated in a fashion similar to the daily mean precipitation rate. First, + the mean of the squared half-hourly \"randomError\" for the day is computed, and + the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken + to get the result in (mm/day):\n\nPerr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] + ) } / Ncnt_err * 24}^0.5, i=[1,Nf]\nNcnt_err = SUM( 1[Perr_i valid] )\n\nwhere:\nPerr_i\t\t- + half-hourly input, \"randomError\", (mm/hr)\nPerr_daily\t- Magnitude of the daily + error, (mm/day)\nNcnt_err\t\t- Number of valid half-hour error estimates\n\nAgain, + the sum of squared \"randomError\" can be reconstructed, and other estimates can + be derived using the available counts in the Daily files.\n\n\nRead our doc on how + to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/GPM/IMERGDL/DAY/07 +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 1998-01-01 to Ongoing +Tags: + - aws-pds + - atmosphere + - climate + - coastal + - datacenter + - global + - hydrology + - land + - metadata + - opendap + - netcdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V07 + (GPM_3IMERGDL) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGDL.07/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: Precipitation Estimation from Remotely Sensed Imagery Using an Artificial + Neural Network Cloud Classification System + URL: https://doi.org/10.1175/JAM2173.1 + AuthorName: Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao + - Title: Kalman Filter Based CMORPH + URL: https://doi.org/10.1175/JHM-D-11-022.1 + AuthorName: Joyce, R. J., P. Xie, and J. E. Janowiak + - Title: Calculation of Gridded Precipitation Data for the Global Land-Surface + Using In-Situ Gauge Observations + URL: https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf + AuthorName: Rudolf, B., and U. Schneider + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-gpm3imerghh.yaml b/datasets/nasa-gpm3imerghh.yaml new file mode 100644 index 000000000..8bdf2c380 --- /dev/null +++ b/datasets/nasa-gpm3imerghh.yaml @@ -0,0 +1,73 @@ +Name: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHH) at GES DISC +Description: |- + Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07. + + The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team. + + The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases. + + The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme. + + The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR. + + The IMERG system is run twice in near-real time: + + "Early" multi-satellite product ~4 hr after observation time using only forward morphing and + "Late" multi-satellite product ~14 hr after observation time, using both forward and backward morphing + and once after the monthly gauge analysis is received: + + "Final", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses. + + In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users. + + Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then "forward/backward morphed" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months). + Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/GPM/IMERG/3B-HH/07 +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov' +ManagedBy: NASA +UpdateFrequency: From 1998-01-01 to Ongoing +Tags: + - aws-pds + - atmosphere + - climate + - datacenter + - forecast + - global + - hdf + - hydrology + - land + - metadata + - opendap + - radar + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree + V07 (GPM_3IMERGHH) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGHH.07/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: Precipitation Estimation from Remotely Sensed Imagery Using an Artificial + Neural Network Cloud Classification System + URL: https://doi.org/10.1175/JAM2173.1 + AuthorName: Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao + - Title: Kalman Filter Based CMORPH + URL: https://doi.org/10.1175/JHM-D-11-022.1 + AuthorName: Joyce, R. J., P. Xie, and J. E. Janowiak + - Title: Calculation of Gridded Precipitation Data for the Global Land-Surface + Using In-Situ Gauge Observations + URL: https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf + AuthorName: Rudolf, B., and U. Schneider + Tutorials: + - Title: How to Read IMERG Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-gpm3imerghhe.yaml b/datasets/nasa-gpm3imerghhe.yaml new file mode 100644 index 000000000..2635fb6fe --- /dev/null +++ b/datasets/nasa-gpm3imerghhe.yaml @@ -0,0 +1,100 @@ +Name: GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHE) at GES DISC +Description: "Version 07B is the current version of the IMERG data sets. Older versions + will no longer be available and have been superseded by Version 07.\n\nThe Integrated + Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides + the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation + estimates from the various precipitation-relevant satellite passive microwave (PMW) + sensors comprising the GPM constellation are computed using the 2021 version of + the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the + GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly + 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly + Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over + high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated + merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian + time interpolation scheme based on work by the Climate Prediction Center (CPC) and + the Precipitation Estimation from Remotely Sensed Information using Artificial Neural + Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In + parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR + fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported + by an asynchronous re-calibration cycle) which are then input to the KF morphing + (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an + asynchronous KF weights updating cycle) uses the PMW and IR estimates to create + half-hourly estimates. Motion vectors for the morphing are computed by maximizing + the pattern correlation of successive hours within each of the precipitation (PRECTOT), + total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data + fields provided by the Modern-Era Retrospective Analysis for Research and Applications, + Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) + Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time + (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, + else from TQL, if available, else from TQV. The KF uses the morphed data as the + “forecast” and the IR estimates as the “observations”, with weighting that depends + on the time interval(s) away from the microwave overpass time. The IR becomes important + after about ±90 minutes away from the overpass time. Variable averaging in the KF + is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation + Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of + KF morphed precipitation to the local histogram of forward- and backward-morphed + microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" + multi-satellite product ~4 hr after observation time using only forward morphing + and\n\"Late\" multi-satellite product ~14 hr after observation time, using both + forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", + satellite-gauge product ~4 months after the observation month, using both forward + and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time + Early and Late half-hourly estimates have a monthly climatological concluding calibration + based on averaging the concluding calibrations computed in the Final, while in the + post-real-time Final Run the multi-satellite half-hourly estimates are adjusted + so that they sum to the Final Run monthly satellite-gauge combination. In all cases + the output contains multiple fields that provide information on the input data, + selected intermediate fields, and estimation quality. In general, the complete calibrated + precipitation, precipitation, is the data field of choice for most users.\n\nPrecipitation + phase is a diagnostic variable computed using analyses of surface temperature, humidity, + and pressure. \nRead our doc on how to get AWS Credentials to retrieve this data: + https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/GPM/IMERG/3B-HH-E/07 +Contact: 'Email: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 1998-01-01 to Ongoing +Tags: + - aws-pds + - atmosphere + - climate + - datacenter + - forecast + - global + - hdf + - hydrology + - land + - metadata + - opendap + - radar + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree + V07 (GPM_3IMERGHHE) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGHHE.07/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: Precipitation Estimation from Remotely Sensed Imagery Using an Artificial + Neural Network Cloud Classification System + URL: https://doi.org/10.1175/JAM2173.1 + AuthorName: Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao + - Title: Kalman Filter Based CMORPH + URL: https://doi.org/10.1175/JHM-D-11-022.1 + AuthorName: Joyce, R. J., P. Xie, and J. E. Janowiak + - Title: Calculation of Gridded Precipitation Data for the Global Land-Surface + Using In-Situ Gauge Observations + URL: https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf + AuthorName: Rudolf, B., and U. Schneider + Tutorials: + - Title: How to Read IMERG Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-gpm3imerghhl.yaml b/datasets/nasa-gpm3imerghhl.yaml new file mode 100644 index 000000000..cbdefbdd6 --- /dev/null +++ b/datasets/nasa-gpm3imerghhl.yaml @@ -0,0 +1,101 @@ +Name: GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHL) at GES DISC +Description: |- + Version 07B is the current version of the IMERG data sets. Older versions + will no longer be available and have been superseded by Version 07.\n\nThe Integrated + Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides + the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation + estimates from the various precipitation-relevant satellite passive microwave (PMW) + sensors comprising the GPM constellation are computed using the 2021 version of + the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the + GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly + 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly + Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over + high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated + merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian + time interpolation scheme based on work by the Climate Prediction Center (CPC) and + the Precipitation Estimation from Remotely Sensed Information using Artificial Neural + Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In + parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR + fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported + by an asynchronous re-calibration cycle) which are then input to the KF morphing + (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an + asynchronous KF weights updating cycle) uses the PMW and IR estimates to create + half-hourly estimates. Motion vectors for the morphing are computed by maximizing + the pattern correlation of successive hours within each of the precipitation (PRECTOT), + total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data + fields provided by the Modern-Era Retrospective Analysis for Research and Applications, + Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) + Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time + (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, + else from TQL, if available, else from TQV. The KF uses the morphed data as the + “forecast” and the IR estimates as the “observations”, with weighting that depends + on the time interval(s) away from the microwave overpass time. The IR becomes important + after about ±90 minutes away from the overpass time. Variable averaging in the KF + is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation + Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of + KF morphed precipitation to the local histogram of forward- and backward-morphed + microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n\"Early\" + multi-satellite product ~4 hr after observation time using only forward morphing + and\n\"Late\" multi-satellite product ~14 hr after observation time, using both + forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n\"Final\", + satellite-gauge product ~4 months after the observation month, using both forward + and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time + Early and Late half-hourly estimates have a monthly climatological concluding calibration + based on averaging the concluding calibrations computed in the Final, while in the + post-real-time Final Run the multi-satellite half-hourly estimates are adjusted + so that they sum to the Final Run monthly satellite-gauge combination. In all cases + the output contains multiple fields that provide information on the input data, + selected intermediate fields, and estimation quality. In general, the complete calibrated + precipitation, precipitation, is the data field of choice for most users.\n\nPrecipitation + phase is a diagnostic variable computed using analyses of surface temperature, humidity, + and pressure. \n\n\n\nRead our doc on how to get AWS Credentials to retrieve this + data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/GPM/IMERG/3B-HH-L/07 +Contact: 'GES DISC HELP DESK SUPPORT GROUP": gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 1998-01-01 to Ongoing +Tags: + - aws-pds + - atmosphere + - climate + - datacenter + - forecast + - global + - hydrology + - land + - metadata + - opendap + - radar + - water + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree + V07 (GPM_3IMERGHHL) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGHHL.07/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: Precipitation Estimation from Remotely Sensed Imagery Using an Artificial + Neural Network Cloud Classification System + URL: https://doi.org/10.1175/JAM2173.1 + AuthorName: Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao + - Title: Kalman Filter Based CMORPH + URL: https://doi.org/10.1175/JHM-D-11-022.1 + AuthorName: Joyce, R. J., P. Xie, and J. E. Janowiak + - Title: Calculation of Gridded Precipitation Data for the Global Land-Surface + Using In-Situ Gauge Observations + URL: https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf + AuthorName: Rudolf, B., and U. Schneider + Tutorials: + - Title: How to Read IMERG Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-gpm3imergm.yaml b/datasets/nasa-gpm3imergm.yaml new file mode 100644 index 000000000..5a03706c8 --- /dev/null +++ b/datasets/nasa-gpm3imergm.yaml @@ -0,0 +1,73 @@ +Name: GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V07 (GPM_3IMERGM) at GES DISC +Description: |- + Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07. + + The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team. + + The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases. + + The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme. + + The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR. + + The IMERG system is run twice in near-real time: + + "Early" multi-satellite product ~4 hr after observation time using only forward morphing and + "Late" multi-satellite product ~14 hr after observation time, using both forward and backward morphing + and once after the monthly gauge analysis is received: + + "Final", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses. + + In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users. + + Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then "forward/backward morphed" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months). + Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/GPM/IMERG/3B-MONTH/07 +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 1998-01-01 to Ongoing +Tags: + - aws-pds + - atmosphere + - climate + - datacenter + - forecast + - global + - hydrology + - land + - metadata + - opendap + - radar + - water + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree + V07 (GPM_3IMERGM) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/GPM_L3/GPM_3IMERGM.07/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: Precipitation Estimation from Remotely Sensed Imagery Using an Artificial + Neural Network Cloud Classification System + URL: https://doi.org/10.1175/JAM2173.1 + AuthorName: Hong, Y., K. L. Hsu, S. Sorooshian, and X. Gao + - Title: Kalman Filter Based CMORPH + URL: https://doi.org/10.1175/JHM-D-11-022.1 + AuthorName: Joyce, R. J., P. Xie, and J. E. Janowiak + - Title: Calculation of Gridded Precipitation Data for the Global Land-Surface + Using In-Situ Gauge Observations + URL: https://www.researchgate.net/profile/Udo-Schneider-4/publication/253114707_Calculation_of_Gridded_Precipitation_Data_for_the_Global_Land-Surface_using_in-situ_Gauge_Observations/links/0deec53bbcb3a0e220000000/Calculation-of-Gridded-Precipitation-Data-for-the-Global-Land-Surface-using-in-situ-Gauge-Observations.pdf + AuthorName: Rudolf, B., and U. Schneider + Tutorials: + - Title: How to Read IMERG Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_IMERG_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-gpmimerglandseamask.yaml b/datasets/nasa-gpmimerglandseamask.yaml new file mode 100644 index 000000000..d7690a77e --- /dev/null +++ b/datasets/nasa-gpmimerglandseamask.yaml @@ -0,0 +1,38 @@ +Name: Land/Sea static mask relevant to IMERG precipitation 0.1x0.1 degree V2 (GPM_IMERG_LandSeaMask) at GES DISC +Description: |- + Version 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2. + + This land sea mask originated from the NOAA group at SSEC in the 1980s. It was originally produced at 1/6 deg resolution, and then regridded for the purposes of GPCP, TMPA, and IMERG precipitation products. NASA code 610.2, Terrestrial Information Systems Laboratory, restructured this land sea mask to match the IMERG grid, and converted the file to CF-compliant netCDF4. Version 2 was created in May, 2019 to resolve detected inaccuracies in coastal regions. + + Users should be aware that this is a static mask, i.e. there is no seasonal or annual variability, and it is due for update. It is not recommended to be used outside of the aforementioned precipitation data. + Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/6P5EM1HPR3VD +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 1998-01-01 to Ongoing +Tags: + - aws-pds + - atmosphere + - coastal + - datacenter + - global + - land + - metadata + - opendap + - netcdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'Land/Sea static mask relevant to IMERG precipitation 0.1x0.1 degree + V2 (GPM_IMERG_LandSeaMask) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/AUXILIARY/GPM_IMERG_LandSeaMask.2/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-gpmmergir.yaml b/datasets/nasa-gpmmergir.yaml new file mode 100644 index 000000000..098a21d8b --- /dev/null +++ b/datasets/nasa-gpmmergir.yaml @@ -0,0 +1,80 @@ +Name: NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1 (GPM_MERGIR) at GES DISC +Description: "These data originate from NOAA/NCEP.\n\nThe NOAA Climate Prediction + Center/NCEP/NWS is making the data available originally in binary format, in a weekly + rotating archive. The NASA GES DISC is acquiring the binary files as they become + available, converts them into CF (Climate and Forecast) -convention compliant netCDF-4 + format, and stores the product in a permanent archive. The original record started + from February, 2000, but in June, 2025 it was extended back to January, 1998.\n\nThe + leading edge of data availability is delayed by about 24 hours from real-time to + abide by international data exchange agreements between NOAA and EUMETSAT (the METEOSAT + data providers).\n\nThe data contain globally-merged (60°S-60°N) 4-km pixel-resolution + IR brightness temperature data (equivalent blackbody temps), merged from the European, + Japanese, and U.S. geostationary satellites over the period of record (GOES-8/9/10/11/12/13/14/15/16/17/18/19, + METEOSAT-5/7/8/9/10/11, and GMS-5/MTSat-1R/2/Himawari-8/9).\n\nThe global geo-IR + are dynamically calibrated to GOES East, using a 35 day trailing inter-calibration + using time/space-matched IR Tb’s at the mid-point between sub-satellite positions. + \ In the event of duplicate data in a grid box, the value with the smaller zenith + angle is taken. The data have been corrected for \"zenith angle dependence\", in + which IR temperatures for locations far from satellite nadir are erroneously cold + due to a combination of geometric effects and radiometric path extinction effects + (Joyce et al. 2001). Finally, the data are re-navigated for parallax, which shifts + the geo-location of the GEO-IR footprints to approximately account for the cloud + tops that the IR “sees” being displaced away from their actual geographic location + when viewed along a slanted path. These corrections allow for the merging of the + IR data from the various GEO-satellites with greatly reduced discontinuities at + GEO-satellite data boundaries. In the event of duplicate data in a grid box, the + value with the smaller zenith angle is taken.\n\nThe NASA GES DISC is curating these + data in a self-documenting, CF-compliant, netCDF-4 format, which allows a broad + range of applications to access the data directly, without the need to cope with + the original binary data format. In addition to the direct download of netCDF-4 + data, the GES DISC provides data download in binary, ASCII, and netCDF-3 formats + using the OPeNDAP interface.\n\nSimilarities with the original\n-----------------------------\nAs + in the original binaries, every netCDF-4 file covers one hour, and contains two + half-hourly grids, at 4-km grid cell resolution. \n\nDifferences from the original\n-----------------------------\n1. + The data in the netCDF-4 files are already converted to real (float) values of Brightness + Temperatures in Kelvin. There is no need to further scale these data. The netCDF-4 + format is machine-independent and users need not worry about the endian-ness of + their machines. \n\n2. To meet the requirements of collection spatial metadata, + the grid is re-ordered from the original and now goes from -180 (West) to 180 (East). + It is also starting from -60 (South).\n\nThe data and time units are reflected in + the corresponding \"units\" attributes, and grid dimensions are described by longitude + (\"lon\"), latitude (\"lat\") and \"time\" vectors. Thus, any CF-compliant tool + should automatically understand the setup in the data files and the starting time + for each half-hourly grid. Even without such tools, simple \"ncdump\" or \"h5dump\" + command line tools will easily disclose the netCDF-4 files configuration.\n\nAcknowledgements\n------------------\nThe + creation of the original data at NOAA/NCEP is supported by funding from the NOAA + Office of Global Programs for the Global Precipitation Climatology Project (GPCP) + and by NASA via the Tropical Rainfall Measuring Mission (TRMM). \n\nThe permanent + archive at GES DISC is supported by NASA's HQ Earth Science Data Systems (ESDS) + Program. \n\n\n\nRead our doc on how to get AWS Credentials to retrieve this data: + https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/P4HZB9N27EKU +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 1998-01-01 to Ongoing +Tags: + - aws-pds + - atmosphere + - climate + - datacenter + - forecast + - global + - metadata + - opendap + - netcdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1 (GPM_MERGIR) + at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/MERGED_IR/GPM_MERGIR.1/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-heasarc.yaml b/datasets/nasa-heasarc.yaml index f1518f231..83adbded9 100644 --- a/datasets/nasa-heasarc.yaml +++ b/datasets/nasa-heasarc.yaml @@ -142,6 +142,11 @@ Resources: ARN: arn:aws:s3:::nasa-heasarc/sax/data/ Region: us-east-1 Type: S3 Bucket + + - Description: "The [SRG Mission](https://heasarc.gsfc.nasa.gov/docs/heasarc/missions/srg.html) eROSITA Instrument Data Archive. More information available at [the eROSITA support site](https://heasarc.gsfc.nasa.gov/docs/srg/erosita/). Total size > 3 TB." + ARN: arn:aws:s3:::nasa-heasarc/srg/data/erosita/ + Region: us-east-1 + Type: S3 Bucket - Description: The [Suzaku Mission](https://heasarc.gsfc.nasa.gov/docs/astroe/astroe2.html) Data Archive. For more information, see the website of the [Suzaku/Astro-E2 Guest Observer Facility](https://heasarc.gsfc.nasa.gov/docs/suzaku/astroegof.html). Total size 5.4 TB. ARN: arn:aws:s3:::nasa-heasarc/suzaku/data/ @@ -173,6 +178,11 @@ Resources: Region: us-east-1 Type: S3 Bucket + - Description: "The [XRISM Mission](https://heasarc.gsfc.nasa.gov/docs/heasarc/missions/xrism.html) Data Archive. Total size > 1 TB." + ARN: arn:aws:s3:::nasa-heasarc/xrism/data/obs/ + Region: us-east-1 + Type: S3 Bucket + DataAtWork: Tutorials: - Title: HEASARC Cloud access page diff --git a/datasets/nasa-hlsl30.yaml b/datasets/nasa-hlsl30.yaml new file mode 100644 index 000000000..098773dd0 --- /dev/null +++ b/datasets/nasa-hlsl30.yaml @@ -0,0 +1,189 @@ +Name: HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 +Description: "The Harmonized Landsat Sentinel-2 (HLS) project provides consistent + surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual + constellation of satellite sensors. The Operational Land Imager (OLI) is housed + aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral + Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, + and Sentinel-2C satellites. The combined measurement enables global observations + of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses + a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric + correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, + illumination and view angle normalization, and spectral bandpass adjustment.\n\nThe + HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function + (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. + The [HLSS30](https://doi.org/10.5067/HLS/HLSS30.002) and HLSL30 products are gridded + to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) + tiling system and thus are “stackable” for time series analysis.\n\nThe HLSL30 product + is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed + as a separate file. There are 11 bands included in the HLSL30 product along with + one quality assessment (QA) band and four angle bands. See the User Guide for a + more detailed description of the individual bands provided in the HLSL30 product.\n\nKnown + Issues\n\n* Unrealistically high aerosol and low surface reflectance over bright + areas: The atmospheric correction over bright targets occasionally retrieves unrealistically + high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, + both false high aerosol and realistically high aerosol, are masked when quality + bits 6 and 7 are both set to 1 (see Table 9 in the [User Guide](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)); + the corresponding spectral data should be discarded from analysis.\n\n* Issues over + high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses + can be gridded into a single MGRS tile resulting in an L30 granule with data sensed + at two different times. In this same area, it is also possible that Landsat overpasses + that should be gridded into a single MGRS tile are actually written as separate + data files. Finally, for scenes with a latitude greater than or equal to 65 degrees + north, ascending Landsat scenes may have a slightly higher error in the BRDF correction + because the algorithm is calibrated using descending scenes.\n\n* Fmask omission + errors: There are known issues regarding the Fmask band of this data product that + impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission + errors in water detection for cases where water detection using spectral data alone + is difficult, and omission and commission errors in cloud shadow detection for areas + with great topographic relief. This issue does not impact other bands in the dataset.\n\n* + Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow + surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When + assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance + is generally higher than Sentinel-2 reflectance in the visible bands.\n\n* Unrealistically + high snow surface reflectance in the visible bands: By design, the Land Surface + Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval + over snow; instead, a default aerosol optical thickness (AOT) is used to drive the + snow surface reflectance. If the snow detection fails, the full LaSRC is used in + both AOT retrieval and surface reflectance derivation over snow, which produces + surface reflectance values as high as 1.6 in the visible bands. This is a common + problem for spring images at high latitudes.\n\n* Unrealistically low surface reflectance + surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally + too high. When this artificially high AOT is used to derive the surface reflectance + of the neighboring non-snow pixels, very low surface reflectance will result. These + pixels will appear very dark in the visible bands. If the surface reflectance value + of a pixel is below -0.2, a NO_DATA value of -9999 is used.\n\n* Unrealistically + low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction + does not attempt aerosol retrieval over clouds and a default AOT is used instead. + But if the cloud detection fails, an artificially high AOT will be retrieved over + clouds. If the high AOT is used to derive the surface reflectance of the neighboring + cloud-free pixels, very low surface reflectance values will result. If the surface + reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. \n\n* + Unusually low reflectance around other bright land targets: While the HLS atmospheric + correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT + over bright targets can be unrealistically high in some cases, similar to cloud + or snow. If this unrealistically high AOT is used to derive the surface reflectance + of the neighboring pixels, very low surface reflectance values can result as shown + in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA + value of -9999 is used. These types of bright targets are mostly man-made, such + as buildings, parking lots, and roads. \n\n* Dark plumes over water: The HLS atmospheric + correction does not attempt aerosol retrieval over water. For water pixels, the + AOT retrieved from the nearest land pixels is used to derive the surface reflectance, + but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create + dark stripes over water, as shown in Figure 3. This happens more often over large + water bodies, such as lakes and bays, than over narrow rivers. \n\n* Landsat WRS-2 + Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat + Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before + the derived surface reflectance is reprojected into Military Grid Reference System + (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining + clear pixels might be used for the atmospheric correction of the entire image. The + AOT that is used can be quite different from the value for the adjacent row in the + same path, which results in an artificial abrupt change from one row to the next, + as shown in Figure 4. This occurrence is very rare. \n \n* Landsat WRS2 path/row + boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying + thresholds to the histograms of some metrics for each path/row independently. If + two adjacent rows in the same path have distinct distributions within the metrics, + abrupt changes in masking patterns can appear across the row boundary, as shown + in Figure 5. This occurrence is very rare. \n\n* Fmask configuration was deficient + for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary + digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water + Occurrence data for a 2-3 month run in 2021. This impacted the masking results over + water and in mountainous regions. \n\n* The reflectance “scale_factor” and “offset” + for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 + and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF + (COG) files of some bands for a small number of granules. The lack of this information + creates a problem for automatic conversion of the reflectance data, requiring explicit + scaling in applications. The problem has been corrected, but the affected granules + have not been reprocessed. \n\n* Incomplete map projection information: For a time, + HLS imagery was produced with an incomplete coordinate reference system (CRS). The + metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary + to geolocate pixels within the image but might not be in a standard form, especially + for granules produced early in the HLS mission. As a result, an error will occur + in certain image processing packages due to the incomplete CRS. The simplest solution + is to update to the latest version of Geospatial Data Abstraction Library (GDAL) + and/or rasterio, which use the available information without error. \n\n* False + northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false + northing for the UTM projection, and the angle data are supposed to follow the same + convention. However, the L30 angle data incorrectly uses a false northing of 10^7. + There is no problem with the angle data itself, but the false northing needs to + be set to 0 for it to be aligned with the reflectance.\n\n* L30 from Landsat L1GT + scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. + However, some scenes made it through screening for a short period of HLS production. + L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated + using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 + granule by examining the HLS cmr.xml metadata file.\n\n* The UTC dates in the L30/S30 + filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological + Survey (USGS) in naming their Level 1 images, and HLS processing retains this information + to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia + and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 + or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end + of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired + in the 22nd hour of day 117 of year 2016 in UTC, but the time was 10:15:52 of day + 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 + was acquired in the next orbit in eastern Australia. \n\n This issue also occurs + for Landsat. For example, HLS.L30.T59HQS.2016117T221209.v2.0 was acquired on the + same day as the first S30 example given above, but both on day 118 of 2016 locally. + Adding to the confusion for L30, in the same region, Landsat 8 and 9 can each overpass + once in one of the two adjacent WRS-2 Paths (91/92/93) over a two-day period on + a local calendar, but based on UTC time, the two overpasses can appear to be on + the same day. For example, in the following seemingly same-day pair, the second + L30 is actually for day 168 locally: \n HLS.L30.T55GCN.2023167T000407.v2.0 + \ \n HLS.L30.T55GCN.2023167T235747.v2.0 \n Bear in mind, the date peculiarity + for the data occurs when the overpass time is during the late hours of a UTC day. + \n\n* The atmospheric ancillary data from the wrong date was used for LaSRC: Related + to the above, for eastern Australia and New Zealand, L30 and S30 surface reflectance + on certain days was created using the atmospheric ancillary data from a date that + was one day too early. The exact geographic extent of the affected HLS products + and the impact on the surface reflectance quality are under investigation. Practice + caution when using data with overpass times during the late hours of a UTC day.\n\n* + Duplicates in L30: The Landsat 9 acquisitions from October 2021 to March 2023 in + Landsat Collection 2 were reprocessed by USGS in March 2023. This reprocessing updated + the overpass time by a fraction of a second for some scenes. Since HLS uses overpass + time as part of the L30 filename, the older L30 granules were not automatically + overwritten due to the different filenames. For example, the first L30 granule in + the following pair originated from an older version of L1TP of Landsat 9 with the + second granule originating from the reprocessed version. \nHLS.L30.T11SLC.2022166T182646.v2.0 + \ \nHLS.L30.T11SLC.2022166T182645.v2.0 \nThere are other causes of duplicate L30 + granules, but the overall number of duplicates is very small.\n\n* Poor Geolocation: + A large amount of granules that were processed for May through July 2023 were created + with L1GT input scenes which were deemed undesirable due to a poor geolocation issue. + These granules were removed from the archive. (see the full list of removed [granules](https://lpdaac.usgs.gov/documents/2161/L30_L1GT_granules_May_July_2023.csv))\n\nImprovements/Changes + from Previous Versions\n\n* Aerosol QA bits from the USGS Land Surface Reflectance + Code (LaSRC) model output have been added into the Function of Mask (Fmask) data + layer. The added two bits indicate the aerosol levels: high, medium, low, and climatology + aerosol.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/HLS/HLSL30.002 +Contact: 'User Services: lpdaac@usgs.gov' +ManagedBy: NASA +UpdateFrequency: From 2013-04-11 to Ongoing +Tags: + - aws-pds + - atmosphere + - cog + - datacenter + - earth observation + - geospatial + - global + - ice + - land + - metadata + - orbit + - satellite imagery + - stac + - surface water + - tiles + - water + - xml +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'HLS Landsat Operational Land Imager Surface Reflectance and TOA + Brightness Daily Global 30m v2.0.' + ARN: arn:aws:s3:::lp-prod-protected/HLSL30.020 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Getting Started with Cloud-Native HLS Data in Python + URL: https://github.com/nasa/HLS-Data-Resources/blob/main/python/tutorials/HLS_Tutorial.ipynb + AuthorName: Mahsa Jami, Erik A. Bolch, Cole K. Krehbiel, Aaron M. Friesz, Brianna M. Lind diff --git a/datasets/nasa-hlss30.yaml b/datasets/nasa-hlss30.yaml new file mode 100644 index 000000000..5332e1d63 --- /dev/null +++ b/datasets/nasa-hlss30.yaml @@ -0,0 +1,189 @@ +Name: HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0 +Description: "The Harmonized Landsat Sentinel-2 (HLS) project provides consistent + surface reflectance data from the Operational Land Imager (OLI) aboard the joint + NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s + Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement + enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. + The HLS project uses a set of algorithms to obtain seamless products from OLI and + MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial + co-registration and common gridding, illumination and view angle normalization, + and spectral bandpass adjustment. \n\nThe HLSS30 product provides 30-m Nadir Bidirectional + Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived + from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and + [HLSL30](https://doi.org/10.5067/HLS/HLSL30.002) products are gridded to the same + resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) + tiling system and thus are “stackable” for time series analysis.\n\nThe HLSS30 product + is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed + as a separate COG. There are 13 bands included in the HLSS30 product along with + four angle bands and a quality assessment (QA) band. See the User Guide for a more + detailed description of the individual bands provided in the HLSS30 product.\n\nKnown + Issues\n\n* Unrealistically high aerosol and low surface reflectance over bright + areas: The atmospheric correction over bright targets occasionally retrieves unrealistically + high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, + both false high aerosol and realistically high aerosol, are masked when quality + bits 6 and 7 are both set to 1 (see Table 9 in the [User Guide](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)); + the corresponding spectral data should be discarded from analysis.\n\n* Issues over + high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses + can be gridded into a single MGRS tile resulting in an L30 granule with data sensed + at two different times. In this same area, it is also possible that Landsat overpasses + that should be gridded into a single MGRS tile are actually written as separate + data files. Finally, for scenes with a latitude greater than or equal to 65 degrees + north, ascending Landsat scenes may have a slightly higher error in the BRDF correction + because the algorithm is calibrated using descending scenes.\n\n* Fmask omission + errors: There are known issues regarding the Fmask band of this data product that + impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission + errors in water detection for cases where water detection using spectral data alone + is difficult, and omission and commission errors in cloud shadow detection for areas + with great topographic relief. This issue does not impact other bands in the dataset.\n\n* + Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow + surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When + assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance + is generally higher than Sentinel-2 reflectance in the visible bands.\n\n* Unrealistically + high snow surface reflectance in the visible bands: By design, the Land Surface + Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval + over snow; instead, a default aerosol optical thickness (AOT) is used to drive the + snow surface reflectance. If the snow detection fails, the full LaSRC is used in + both AOT retrieval and surface reflectance derivation over snow, which produces + surface reflectance values as high as 1.6 in the visible bands. This is a common + problem for spring images at high latitudes.\n\n* Unrealistically low surface reflectance + surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally + too high. When this artificially high AOT is used to derive the surface reflectance + of the neighboring non-snow pixels, very low surface reflectance will result. These + pixels will appear very dark in the visible bands. If the surface reflectance value + of a pixel is below -0.2, a NO_DATA value of -9999 is used. In Figure 1, the pixels + in front of the glaciers have surface reflectance values that are too low. \n\n* + Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric + correction does not attempt aerosol retrieval over clouds and a default AOT is used + instead. But if the cloud detection fails, an artificially high AOT will be retrieved + over clouds. If the high AOT is used to derive the surface reflectance of the neighboring + cloud-free pixels, very low surface reflectance values will result. If the surface + reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. \n\n* + Unusually low reflectance around other bright land targets: While the HLS atmospheric + correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT + over bright targets can be unrealistically high in some cases, similar to cloud + or snow. If this unrealistically high AOT is used to derive the surface reflectance + of the neighboring pixels, very low surface reflectance values can result as shown + in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA + value of -9999 is used. These types of bright targets are mostly man-made, such + as buildings, parking lots, and roads. \n\n* Dark plumes over water: The HLS atmospheric + correction does not attempt aerosol retrieval over water. For water pixels, the + AOT retrieved from the nearest land pixels is used to derive the surface reflectance, + but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create + dark stripes over water, as shown in Figure 3. This happens more often over large + water bodies, such as lakes and bays, than over narrow rivers. \n\n* Landsat WRS-2 + Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat + Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before + the derived surface reflectance is reprojected into Military Grid Reference System + (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining + clear pixels might be used for the atmospheric correction of the entire image. The + AOT that is used can be quite different from the value for the adjacent row in the + same path, which results in an artificial abrupt change from one row to the next, + as shown in Figure 4. This occurrence is very rare. \n \n* Landsat WRS2 path/row + boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying + thresholds to the histograms of some metrics for each path/row independently. If + two adjacent rows in the same path have distinct distributions within the metrics, + abrupt changes in masking patterns can appear across the row boundary, as shown + in Figure 5. This occurrence is very rare. \n\n* Fmask configuration was deficient + for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary + digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water + Occurrence data for a 2-3 month run in 2021. This impacted the masking results over + water and in mountainous regions. \n\n* The reflectance “scale_factor” and “offset” + for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 + and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF + (COG) files of some bands for a small number of granules. The lack of this information + creates a problem for automatic conversion of the reflectance data, requiring explicit + scaling in applications. The problem has been corrected, but the affected granules + have not been reprocessed. \n\n* Incomplete map projection information: For a time, + HLS imagery was produced with an incomplete coordinate reference system (CRS). The + metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary + to geolocate pixels within the image but might not be in a standard form, especially + for granules produced early in the HLS mission. As a result, an error will occur + in certain image processing packages due to the incomplete CRS. The simplest solution + is to update to the latest version of Geospatial Data Abstraction Library (GDAL) + and/or rasterio, which use the available information without error. \n\n* False + northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false + northing for the UTM projection, and the angle data are supposed to follow the same + convention. However, the L30 angle data incorrectly uses a false northing of 10^7. + There is no problem with the angle data itself, but the false northing needs to + be set to 0 for it to be aligned with the reflectance.\n\n* L30 from Landsat L1GT + scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. + However, some scenes made it through screening for a short period of HLS production. + L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated + using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 + granule by examining the HLS cmr.xml metadata file.\n\n* The UTC dates in the L30/S30 + filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological + Survey (USGS) in naming their Level 1 images, and HLS processing retains this information + to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia + and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 + or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end + of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired + in the 22nd hour of day 117 of year 2016 in UTC, but the time was 10:15:52 of day + 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 + was acquired in the next orbit in eastern Australia. \n\n This issue also occurs + for Landsat. For example, HLS.L30.T59HQS.2016117T221209.v2.0 was acquired on the + same day as the first S30 example given above, but both on day 118 of 2016 locally. + Adding to the confusion for L30, in the same region, Landsat 8 and 9 can each overpass + once in one of the two adjacent WRS-2 Paths (91/92/93) over a two-day period on + a local calendar, but based on UTC time, the two overpasses can appear to be on + the same day. For example, in the following seemingly same-day pair, the second + L30 is actually for day 168 locally: \n HLS.L30.T55GCN.2023167T000407.v2.0 + \ \n HLS.L30.T55GCN.2023167T235747.v2.0 \n Bear in mind, the date peculiarity + for the data occurs when the overpass time is during the late hours of a UTC day. + \ \n\n* The atmospheric ancillary data from the wrong date was used for LaSRC: Related + to the above, for eastern Australia and New Zealand, L30 and S30 surface reflectance + on certain days was created using the atmospheric ancillary data from a date that + was one day too early. The exact geographic extent of the affected HLS products + and the impact on the surface reflectance quality are under investigation. Practice + caution when using data with overpass times during the late hours of a UTC day.\n\n* + Duplicates in L30: The Landsat 9 acquisitions from October 2021 to March 2023 in + Landsat Collection 2 were reprocessed by USGS in March 2023. This reprocessing updated + the overpass time by a fraction of a second for some scenes. Since HLS uses overpass + time as part of the L30 filename, the older L30 granules were not automatically + overwritten due to the different filenames. For example, the first L30 granule in + the following pair originated from an older version of L1TP of Landsat 9 with the + second granule originating from the reprocessed version. \nHLS.L30.T11SLC.2022166T182646.v2.0 + \ \nHLS.L30.T11SLC.2022166T182645.v2.0 \nThere are other causes of duplicate L30 + granules, but the overall number of duplicates is very small.\n\n* Poor Geolocation: + A large amount of granules that were processed for May through July 2023 were created + with L1GT input scenes which were deemed undesirable due to a poor geolocation issue. + These granules were removed from the archive. (see the full list of removed [granules](https://lpdaac.usgs.gov/documents/2161/L30_L1GT_granules_May_July_2023.csv))\n\nImprovements/Changes + from Previous Versions\n\n* Aerosol QA bits from the USGS Land Surface Reflectance + Code (LaSRC) model output have been added into the Function of Mask (Fmask) data + layer. The added two bits indicate the aerosol levels: high, medium, low, and climatology + aerosol.\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/HLS/HLSS30.002 +Contact: 'User Services: lpdaac@usgs.gov' +ManagedBy: NASA +UpdateFrequency: From 2015-11-28 to Ongoing +Tags: + - aws-pds + - cog + - datacenter + - earth observation + - geospatial + - global + - hdf + - ice + - land + - metadata + - orbit + - satellite imagery + - stac + - surface water + - tiles + - water + - xml +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily + Global 30m v2.0.' + ARN: arn:aws:s3:::lp-prod-protected/HLSS30.020 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Getting Started with Cloud-Native HLS Data in Python + URL: https://github.com/nasa/HLS-Data-Resources/blob/main/python/tutorials/HLS_Tutorial.ipynb + AuthorName: Mahsa Jami, Erik A. Bolch, Cole K. Krehbiel, Aaron M. Friesz, Brianna M. Lind diff --git a/datasets/nasa-imergprecipcanadaalaska2097.yaml b/datasets/nasa-imergprecipcanadaalaska2097.yaml new file mode 100644 index 000000000..ce0164685 --- /dev/null +++ b/datasets/nasa-imergprecipcanadaalaska2097.yaml @@ -0,0 +1,32 @@ +Name: 'ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020' +Description: |- + This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated. + Read our doc on how to get AWS Credentials to retrieve this data: https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.3334/ORNLDAAC/2097 +Contact: 'ORNL DAAC User Services Office: uso@daac.ornl.gov.' +ManagedBy: NASA +UpdateFrequency: From 2000-06-01 to 2020-12-31 +Tags: + - aws-pds + - atmosphere + - cog + - earth observation + - global + - land + - radar + - cog +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and + Canada, 2000-2020.' + ARN: arn:aws:s3:::ornl-cumulus-prod-protected/above/IMERG_Precip_Canada_Alaska/data + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.ornldaac.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: Accessing Data through ORNL DAAC Web Services + URL: https://github.com/ornldaac/web_services_data_access/blob/master/web_services_data_access.ipynb + AuthorName: ORNL DAAC diff --git a/datasets/nasa-m2i3npasm.yaml b/datasets/nasa-m2i3npasm.yaml new file mode 100644 index 000000000..e863ee10e --- /dev/null +++ b/datasets/nasa-m2i3npasm.yaml @@ -0,0 +1,126 @@ +Name: 'MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields' +Description: "M2I3NPASM (or inst3_3d_asm_Np) is an instantaneous 3-dimensional 3-hourly + data collection in Modern-Era Retrospective analysis for Research and Applications + version 2 (MERRA-2). This collection consists of assimilations of meteorological + parameters at 42 pressure levels, such as temperature, wind components, vertical + pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The data + field is available every three hours starting from 00:00 UTC, e.g.: 00:00, 03:00, + … , 21:00 UTC. The information on the pressure levels can be found in the section + 4.2 of the MERRA-2 File Specification document. \n\nMERRA-2 is the latest version + of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling + and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) + version 5.12.4. The dataset covers the period of 1980-present with the latency + of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check “Records + of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” + tab on this page. Note that a reprocessed data filename is different from the original + file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of + data, changing of tools and services, as well as data announcements from GMAO. Contact + the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: + If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 + Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on + this page. If that does not answer your question, you may post your question to + the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk + (gsfc-dl-help-disc@mail.nasa.gov).\nRead our doc on how to get AWS Credentials to + retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/QBZ6MG944HW0 +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov' +ManagedBy: NASA +UpdateFrequency: From 1980-01-01 to Ongoing +Tags: + - aws-pds + - agriculture + - air temperature + - atmosphere + - biodiversity + - climate + - coastal + - datacenter + - ecosystems + - global + - hydrology + - ice + - land + - metadata + - netcdf + - opendap + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated + Meteorological Fields 0.625 x 0.5 degree V5.12.4 (M2I3NPASM) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NPASM.5.12.4/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: The Modern-Era Retrospective Analysis for Research and Applications, + Version 2 (MERRA-2). + URL: https://doi.org/10.1175/JCLI-D-16-0758.1 + AuthorName: Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, + C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al. + - Title: 'Development of the GEOS-5 atmospheric general circulation model: evolution + from MERRA to MERRA2.' + URL: https://doi.org/10.5194/gmd-8-1339-2015 + AuthorName: Molod, A., L. Takacs, M. Suarez, and J. Bacmeister + - Title: 'The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description + and Data Assimilation Evaluation.' + URL: https://doi.org/10.1175/JCLI-D-16-0609.1 + AuthorName: Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, + R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and + C.J. Flynn + - Title: 'The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and + case studies.' + URL: https://doi.org/10.1175/JCLI-D-16-0613.1 + AuthorName: Buchard V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, + R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, H. Yu + - Title: Land Surface Precipitation in MERRA-2. + URL: https://doi.org/10.1175/JCLI-D-16-0570.1 + AuthorName: Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, + and G.S. Partyka + - Title: Assessment of MERRA-2 Land Surface Hydrology Estimates. + URL: https://doi.org/10.1175/JCLI-D-16-0720.1 + AuthorName: Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, + R. D. Koster, and G. J. M. De Lannoy + - Title: '2015b: MERRA-2: Initial Evaluation of the Climate' + URL: https://ntrs.nasa.gov/api/citations/20160005045/downloads/20160005045.pdf + AuthorName: Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. + Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, + L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, + S. Pawson, O. Reale, S. D. Schubert, and M. Suarez + - Title: Data assimilation using incremental analysis updates + URL: https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2 + AuthorName: Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina + - Title: Documentation and Validation of the Goddard Earth Observing System (GEOS) + Data Assimilation System - Version 4 + URL: https://ntrs.nasa.gov/citations/20050175690 + AuthorName: Bloom, S., A. da Silva, D. Dee, M. Bosilovich, J.-D. Chern, S. Pawson, + S. Schubert, M. Sienkiewicz, I. Stajner, W.-W. Tan, M.-L. Wu + - Title: Design and implementation of components in the Earth System Modeling + Framework + URL: https://doi.org/10.1177/1094342005056120 + AuthorName: Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. + Balaji, P. Li, W. Yang, C. Hill, and A. da Silva + - Title: A catchment-based approach to modeling land surface processes in a GCM, + Part 1, Model Structure + URL: https://doi.org/10.1029/2000JD900327 + AuthorName: Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar + - Title: 'Numerical aspects of the application of recursive filters to variational + statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian + covariances' + URL: https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2 + AuthorName: Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts + - Title: 'Numerical aspects of the application of recursive filters to variational + statistical analysis. Part II: Spatially inhomogeneous and anisotropic general + covariances' + URL: https://doi.org/10.1175//2543.1 + AuthorName: Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts + - Title: Three-dimensional variational analysis with spatially inhomogeneous covariances + URL: https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2 + AuthorName: Wu, W.-S., R.J. Purser and D.F. Parrish + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-m2i3nvaer.yaml b/datasets/nasa-m2i3nvaer.yaml new file mode 100644 index 000000000..bd9d7f6aa --- /dev/null +++ b/datasets/nasa-m2i3nvaer.yaml @@ -0,0 +1,128 @@ +Name: 'MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio 0.625 x 0.5 degree' +Description: "M2I3NVAER (or inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly + data collection in Modern-Era Retrospective analysis for Research and Applications + version 2 (MERRA-2). This collection consists of assimilations of aerosol mixing + ratio parameters at 72 model layers, such as dust, sulphur dioxide, sea salt, black + carbon, and organic carbon. The data field is available every three hour starting + from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File + Specification document provides pressure values nominal for a 1000 hPa surface pressure + and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 + is for the bottom (or surface) model layer. \n\nMERRA-2 is the latest version of + global atmospheric reanalysis for the satellite era produced by NASA Global Modeling + and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) + version 5.12.4. The dataset covers the period of 1980-present with the latency + of ~3 weeks after the end of a month. \n\nData Reprocessing: Please check “Records + of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” + tab on this page. Note that a reprocessed data filename is different from the original + file.\n\nMERRA-2 Mailing List: Sign up to receive information on reprocessing of + data, changing of tools and services, as well as data announcements from GMAO. Contact + the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.\n\nQuestions: + If you have a question, please read \"MERRA-2 File Specification Document\", “MERRA-2 + Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on + this page. If that does not answer your question, you may post your question to + the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk + (gsfc-dl-help-disc@mail.nasa.gov).\nRead our doc on how to get AWS Credentials to + retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/LTVB4GPCOTK2 +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov' +ManagedBy: NASA +UpdateFrequency: From 1980-01-01 to Ongoing +Tags: + - aws-pds + - agriculture + - air quality + - atmosphere + - biodiversity + - carbon + - climate + - coastal + - datacenter + - ecosystems + - global + - hydrology + - ice + - land + - metadata + - netcdf + - opendap + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol + Mixing Ratio 0.625 x 0.5 degree V5.12.4 (M2I3NVAER) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NVAER.5.12.4/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: The Modern-Era Retrospective Analysis for Research and Applications, + Version 2 (MERRA-2). + URL: https://doi.org/10.1175/JCLI-D-16-0758.1 + AuthorName: Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, + C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al. + - Title: 'Development of the GEOS-5 atmospheric general circulation model: evolution + from MERRA to MERRA2.' + URL: https://doi.org/10.5194/gmd-8-1339-2015 + AuthorName: Molod, A., L. Takacs, M. Suarez, and J. Bacmeister + - Title: 'The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description + and Data Assimilation Evaluation.' + URL: https://doi.org/10.1175/JCLI-D-16-0609.1 + AuthorName: Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, + R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and + C.J. Flynn + - Title: 'The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and + case studies.' + URL: https://doi.org/10.1175/JCLI-D-16-0613.1 + AuthorName: Buchard V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, + R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, H. Yu + - Title: Land Surface Precipitation in MERRA-2. + URL: https://doi.org/10.1175/JCLI-D-16-0570.1 + AuthorName: Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, + and G.S. Partyka + - Title: Assessment of MERRA-2 Land Surface Hydrology Estimates. + URL: https://doi.org/10.1175/JCLI-D-16-0720.1 + AuthorName: Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, + R. D. Koster, and G. J. M. De Lannoy + - Title: '2015b: MERRA-2: Initial Evaluation of the Climate' + URL: https://ntrs.nasa.gov/api/citations/20160005045/downloads/20160005045.pdf + AuthorName: Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. + Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, + L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, + S. Pawson, O. Reale, S. D. Schubert, and M. Suarez + - Title: Data assimilation using incremental analysis updates + URL: https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2 + AuthorName: Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina + - Title: Documentation and Validation of the Goddard Earth Observing System (GEOS) + Data Assimilation System - Version 4 + URL: https://ntrs.nasa.gov/citations/20050175690 + AuthorName: Bloom, S., A. da Silva, D. Dee, M. Bosilovich, J.-D. Chern, S. Pawson, + S. Schubert, M. Sienkiewicz, I. Stajner, W.-W. Tan, M.-L. Wu + - Title: Design and implementation of components in the Earth System Modeling + Framework + URL: https://doi.org/10.1177/1094342005056120 + AuthorName: Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. + Balaji, P. Li, W. Yang, C. Hill, and A. da Silva + - Title: A catchment-based approach to modeling land surface processes in a GCM, + Part 1, Model Structure + URL: https://doi.org/10.1029/2000JD900327 + AuthorName: Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar + - Title: 'Numerical aspects of the application of recursive filters to variational + statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian + covariances' + URL: https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2 + AuthorName: Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts + - Title: 'Numerical aspects of the application of recursive filters to variational + statistical analysis. Part II: Spatially inhomogeneous and anisotropic general + covariances' + URL: https://doi.org/10.1175//2543.1 + AuthorName: Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts + - Title: Three-dimensional variational analysis with spatially inhomogeneous covariances + URL: https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2 + AuthorName: Wu, W.-S., R.J. Purser and D.F. Parrish + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. diff --git a/datasets/nasa-m2i3nvasm.yaml b/datasets/nasa-m2i3nvasm.yaml new file mode 100644 index 000000000..c705a48d2 --- /dev/null +++ b/datasets/nasa-m2i3nvasm.yaml @@ -0,0 +1,127 @@ +Name: 'MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields 0.625 x 0.5 degree' +Description: "M2I3NVASM (or inst3_3d_asm_Nv) is an instantaneous 3-dimensional 3-hourly + data collection in Modern-Era Retrospective analysis for Research and Applications + version 2 (MERRA-2). This collection consists of assimilations of meteorological + parameters at 72 model layers, such as temperature, wind components, vertical pressure + velocity, water vapor, and layer height. The data field is available every three + hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of + the MERRA-2 File Specification document provides pressure values nominal for a 1000 + hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the + top layer, and lev=72 is for the bottom (or surface) model layer. \n\nMERRA-2 is + the latest version of global atmospheric reanalysis for the satellite era produced + by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing + System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present + with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please + check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the + “Documentation” tab on this page. Note that a reprocessed data filename is different + from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information + on reprocessing of data, changing of tools and services, as well as data announcements + from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be + added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File + Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked + from the ”Documentation” tab on this page. If that does not answer your question, + you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) + or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\nRead our doc + on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/WWQSXQ8IVFW8 +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov' +ManagedBy: NASA +UpdateFrequency: From 1980-01-01 to Ongoing +Tags: + - aws-pds + - agriculture + - air temperature + - atmosphere + - biodiversity + - climate + - coastal + - datacenter + - ecosystems + - global + - hydrology + - ice + - land + - metadata + - netcdf + - opendap + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated + Meteorological Fields 0.625 x 0.5 degree V5.12.4 (M2I3NVASM) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2I3NVASM.5.12.4/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: The Modern-Era Retrospective Analysis for Research and Applications, + Version 2 (MERRA-2). + URL: https://doi.org/10.1175/JCLI-D-16-0758.1 + AuthorName: Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, + C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al. + - Title: 'Development of the GEOS-5 atmospheric general circulation model: evolution + from MERRA to MERRA2.' + URL: https://doi.org/10.5194/gmd-8-1339-2015 + AuthorName: Molod, A., L. Takacs, M. Suarez, and J. Bacmeister + - Title: 'The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description + and Data Assimilation Evaluation.' + URL: https://doi.org/10.1175/JCLI-D-16-0609.1 + AuthorName: Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, + R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and + C.J. Flynn + - Title: 'The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and + case studies.' + URL: https://doi.org/10.1175/JCLI-D-16-0613.1 + AuthorName: Buchard V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, + R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, H. Yu + - Title: Land Surface Precipitation in MERRA-2. + URL: https://doi.org/10.1175/JCLI-D-16-0570.1 + AuthorName: Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, + and G.S. Partyka + - Title: Assessment of MERRA-2 Land Surface Hydrology Estimates. + URL: https://doi.org/10.1175/JCLI-D-16-0720.1 + AuthorName: Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, + R. D. Koster, and G. J. M. De Lannoy + - Title: '2015b: MERRA-2: Initial Evaluation of the Climate' + URL: https://ntrs.nasa.gov/api/citations/20160005045/downloads/20160005045.pdf + AuthorName: Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. + Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, + L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, + S. Pawson, O. Reale, S. D. Schubert, and M. Suarez + - Title: Data assimilation using incremental analysis updates + URL: https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2 + AuthorName: Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina + - Title: Documentation and Validation of the Goddard Earth Observing System (GEOS) + Data Assimilation System - Version 4 + URL: https://ntrs.nasa.gov/citations/20050175690 + AuthorName: Bloom, S., A. da Silva, D. Dee, M. Bosilovich, J.-D. Chern, S. Pawson, + S. Schubert, M. Sienkiewicz, I. Stajner, W.-W. Tan, M.-L. Wu + - Title: Design and implementation of components in the Earth System Modeling + Framework + URL: https://doi.org/10.1177/1094342005056120 + AuthorName: Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. + Balaji, P. Li, W. Yang, C. Hill, and A. da Silva + - Title: A catchment-based approach to modeling land surface processes in a GCM, + Part 1, Model Structure + URL: https://doi.org/10.1029/2000JD900327 + AuthorName: Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar + - Title: 'Numerical aspects of the application of recursive filters to variational + statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian + covariances' + URL: https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2 + AuthorName: Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts + - Title: 'Numerical aspects of the application of recursive filters to variational + statistical analysis. Part II: Spatially inhomogeneous and anisotropic general + covariances' + URL: https://doi.org/10.1175//2543.1 + AuthorName: Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts + - Title: Three-dimensional variational analysis with spatially inhomogeneous covariances + URL: https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2 + AuthorName: Wu, W.-S., R.J. Purser and D.F. Parrish + Tutorials: + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shre diff --git a/datasets/nasa-m2t1nxslv.yaml b/datasets/nasa-m2t1nxslv.yaml new file mode 100644 index 000000000..913045bec --- /dev/null +++ b/datasets/nasa-m2t1nxslv.yaml @@ -0,0 +1,131 @@ +Name: 'MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics 0.625 x 0.5 degree' +Description: "M2T1NXSLV (or tavg1_2d_slv_Nx) is an hourly time-averaged 2-dimensional + data collection in Modern-Era Retrospective analysis for Research and Applications + version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly + used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, + 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850 hPa, + 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water + vapor (or ice water, liquid water). The data field is time-stamped with the central + time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC.\n\nMERRA-2 + is the latest version of global atmospheric reanalysis for the satellite era produced + by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing + System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present + with the latency of ~3 weeks after the end of a month. \n\nData Reprocessing: Please + check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the + “Documentation” tab on this page. Note that a reprocessed data filename is different + from the original file.\n\nMERRA-2 Mailing List: Sign up to receive information + on reprocessing of data, changing of tools and services, as well as data announcements + from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be + added to the list.\n\nQuestions: If you have a question, please read \"MERRA-2 File + Specification Document\", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked + from the ”Documentation” tab on this page. If that does not answer your question, + you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) + or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).\nRead our doc + on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/VJAFPLI1CSIV +Contact: 'GES DISC HELP DESK SUPPORT GROUP: gsfc-dl-help-disc@mail.nasa.gov. Home Page: https://disc.gsfc.nasa.gov/, GLOBAL MODELING AND ASSIMILATION OFFICE: data@gmao.gsfc.nasa.gov' +ManagedBy: NASA +UpdateFrequency: From 1980-01-01 to Ongoing +Tags: + - aws-pds + - agriculture + - air temperature + - atmosphere + - biodiversity + - climate + - coastal + - datacenter + - ecosystems + - global + - hydrology + - ice + - land + - metadata + - oceans + - opendap + - water + - netcdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level + Diagnostics 0.625 x 0.5 degree V5.12.4 (M2T1NXSLV) at GES DISC.' + ARN: arn:aws:s3:::gesdisc-cumulus-prod-protected/MERRA2/M2T1NXSLV.5.12.4/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.gesdisc.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: The Modern-Era Retrospective Analysis for Research and Applications, + Version 2 (MERRA-2). + URL: https://doi.org/10.1175/JCLI-D-16-0758.1 + AuthorName: Gelaro, R., W. McCarty, M. J. Suárez, R. Todling, A. Molod, L. Takacs, + C. A. Randles, A. Darmenov, M. G. Bosilovich, R. Reichle, et al. + - Title: 'Development of the GEOS-5 atmospheric general circulation model: evolution + from MERRA to MERRA2.' + URL: https://doi.org/10.5194/gmd-8-1339-2015 + AuthorName: Molod, A., L. Takacs, M. Suarez, and J. Bacmeister + - Title: 'The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description + and Data Assimilation Evaluation.' + URL: https://doi.org/10.1175/JCLI-D-16-0609.1 + AuthorName: Randles, C. A., A. M. da Silva, V. Buchard, P. R. Colarco, A. Darmenov, + R. Govindaraju, A. Smirnov, B. Holben, R. Ferrare, J. Hair, Y.Shinozuka, and + C.J. Flynn + - Title: 'The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and + case studies.' + URL: https://doi.org/10.1175/JCLI-D-16-0613.1 + AuthorName: Buchard V., C. A. Randles, A. M. da Silva, A. Darmenov, P. R. Colarco, + R. Govindaraju, R. Ferrare, J. Hair, A. J. Beyersdorf, L. D. Ziemba, H. Yu + - Title: Land Surface Precipitation in MERRA-2. + URL: https://doi.org/10.1175/JCLI-D-16-0570.1 + AuthorName: Reichle, R.H., Q. Liu, R.D. Koster, C.S. Draper, S.P.P. Mahanama, + and G.S. Partyka + - Title: Assessment of MERRA-2 Land Surface Hydrology Estimates. + URL: https://doi.org/10.1175/JCLI-D-16-0720.1 + AuthorName: Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, + R. D. Koster, and G. J. M. De Lannoy + - Title: '2015b: MERRA-2: Initial Evaluation of the Climate' + URL: https://ntrs.nasa.gov/api/citations/20160005045/downloads/20160005045.pdf + AuthorName: Bosilovich, M. G., S. Akella, L. Coy, R. Cullather, C. Draper, R. + Gelaro, R. Kovach, Q.Liu, A. Molod, P. Norris, K. Wargan, W. Chao, R. Reichle, + L. Takacs, Y. Vikhliaev, S. Bloom, A. Collow, S. Firth, G. Labow, G. Partyka, + S. Pawson, O. Reale, S. D. Schubert, and M. Suarez + - Title: Data assimilation using incremental analysis updates + URL: https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2 + AuthorName: Bloom, S., L. Takacs, A. DaSilva, and D. Ledvina + - Title: Documentation and Validation of the Goddard Earth Observing System (GEOS) + Data Assimilation System - Version 4 + URL: https://ntrs.nasa.gov/citations/20050175690 + AuthorName: Bloom, S., A. da Silva, D. Dee, M. Bosilovich, J.-D. Chern, S. Pawson, + S. Schubert, M. Sienkiewicz, I. Stajner, W.-W. Tan, M.-L. Wu + - Title: Design and implementation of components in the Earth System Modeling + Framework + URL: https://doi.org/10.1177/1094342005056120 + AuthorName: Collins, N., G. Theurich, C. DeLuca, M. Suarez, A. Trayanov, V. + Balaji, P. Li, W. Yang, C. Hill, and A. da Silva + - Title: A catchment-based approach to modeling land surface processes in a GCM, + Part 1, Model Structure + URL: https://doi.org/10.1029/2000JD900327 + AuthorName: Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar + - Title: 'Numerical aspects of the application of recursive filters to variational + statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian + covariances' + URL: https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2 + AuthorName: Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts + - Title: 'Numerical aspects of the application of recursive filters to variational + statistical analysis. Part II: Spatially inhomogeneous and anisotropic general + covariances' + URL: https://doi.org/10.1175//2543.1 + AuthorName: Purser, R. J., W.-S. Wu, D. F. Parrish, and N. M. Roberts + - Title: Three-dimensional variational analysis with spatially inhomogeneous covariances + URL: https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2 + AuthorName: Wu, W.-S., R.J. Purser and D.F. Parrish + Tutorials: + - Title: How to Read and Plot NetCDF MERRA-2 Data in Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Read_and_Plot_NetCDF_MERRA-2_data_in_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. + - Title: How to Access GES DISC Data Using Python + URL: https://github.com/nasa/gesdisc-tutorials/blob/main/notebooks/How_to_Access_GES_DISC_Data_Using_Python.ipynb + AuthorName: James Acker, Jerome Alfred, Helen Amos, Chris Battisto, Thomas Hearty, Alexis Hunzinger, Lena Iredell, Christoph Keller, Binita KC, Carlee Loeser, Ariana Louise, Kristan Morgan, Dieu My T. Nguyen, Dana Ostrenga, Xiaohua Pan, Kanan Patel, Brianna R. Pagán, Andrey Savtchenko, Elliot Sherman, + Suhung Shen, Jian Su,Joseph Wysk, Rupesh Shrestha. \ No newline at end of file diff --git a/datasets/nasa-mcd43a1.yaml b/datasets/nasa-mcd43a1.yaml new file mode 100644 index 000000000..240dac694 --- /dev/null +++ b/datasets/nasa-mcd43a1.yaml @@ -0,0 +1,55 @@ +Name: MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global - 500m V061 +Description: "The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version + 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model + Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data + at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the + retrieval period which is reflected in the Julian date in the file name. MCD43A1 + provides the three model weighting parameters (isotropic, volumetric, and geometric) + used to derive the Albedo ([MCD43A3](https://doi.org/10.5067/MODIS/MCD43A3.061)) + and Nadir BRDF-Adjusted Reflectance (NBAR) ([MCD43A4](https://doi.org/10.5067/MODIS/MCD43A4.061)) + products.\n\nUsers are urged to use the band specific quality flags to isolate the + highest quality full inversion results for their own science applications as described + in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a1-brdfalbedo-model-parameters-product/).\n\nThe + MCD43A1 provides the three model weighting parameters for MODIS spectral bands 1 + through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along + with the three-dimensional parameter layers for these bands are the simplified mandatory + quality layers for each of the 10 bands. Essential quality information provided + in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.061) data file + should be consulted when using this product. \n\nKnown Issues\n\n* For complete + information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment + website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61).\n\nImprovements/Changes + from Previous Versions\n\n* The Version 6.1 Level-1B (L1B) products have been improved + by undergoing various calibration changes that include: changes to the response-versus-scan + angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections + to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections + to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* + A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead + our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/MODIS/MCD43A1.061 +Contact: 'User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact' +ManagedBy: NASA +UpdateFrequency: From 2000-02-16 to Ongoing (Daily - < Weekly) +Tags: + - aws-pds + - earth observation + - geospatial + - global + - land + - opendap + - tiles + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global - + 500m V061.' + ARN: arn:aws:s3:::lp-prod-protected/MCD43A1.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC \ No newline at end of file diff --git a/datasets/nasa-mcd43a3.yaml b/datasets/nasa-mcd43a3.yaml new file mode 100644 index 000000000..2f9465edf --- /dev/null +++ b/datasets/nasa-mcd43a3.yaml @@ -0,0 +1,45 @@ +Name: MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global - 500m V061 +Description: |- + The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6.1 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name. + + Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a3-albedo-product/). + + The MCD43A3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) data at local solar noon for MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. Along with the albedo layers are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.061) data file should be consulted when using this product. + + Known Issues + + * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61). + + Improvements/Changes from Previous Versions + + * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. + * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). + Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/MODIS/MCD43A3.061 +Contact: 'User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac' +ManagedBy: NASA +UpdateFrequency: From 2000-02-16 to Ongoing (Daily - < Weekly) +Tags: + - aws-pds + - earth observation + - geospatial + - global + - land + - opendap + - satellite imagery + - tiles + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global - 500m V061.' + ARN: arn:aws:s3:::lp-prod-protected/MCD43A3.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC \ No newline at end of file diff --git a/datasets/nasa-mcd43a4.yaml b/datasets/nasa-mcd43a4.yaml new file mode 100644 index 000000000..373c35bbc --- /dev/null +++ b/datasets/nasa-mcd43a4.yaml @@ -0,0 +1,46 @@ +Name: MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global - 500m V061 +Description: |- + The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. + + Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43a4-nbar-product/). + + The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding [MCD43A2](https://doi.org/10.5067/MODIS/MCD43A2.061) data file should be consulted when using this product. + + Known Issues + + * For complete information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=TerraAqua&as=61). + + Improvements/Changes from Previous Versions + + * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. + * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). + Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/MODIS/MCD43A4.061 +Contact: 'User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact' +ManagedBy: NASA +UpdateFrequency: From 2000-02-16 to Ongoing (Daily - < Weekly) +Tags: + - aws-pds + - earth observation + - geospatial + - global + - land + - opendap + - satellite imagery + - tiles + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global + - 500m V061.' + ARN: arn:aws:s3:::lp-prod-protected/MCD43A4.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC diff --git a/datasets/nasa-mi1b2e.yaml b/datasets/nasa-mi1b2e.yaml new file mode 100644 index 000000000..c09e05b93 --- /dev/null +++ b/datasets/nasa-mi1b2e.yaml @@ -0,0 +1,42 @@ +Name: MISR Level 1B2 Ellipsoid Data V004 +Description: "MI1B2E_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level + 1B2 Ellipsoid Data Version 4 product. It contains Ellipsoid-projected Top-of-Atmosphere + (TOA) Radiance, resampled at the surface and topographically corrected, as well + as geometrically corrected by PGE22. Data collection for this product is ongoing.\r\n\r\nMISR + itself is an instrument designed to view Earth with cameras pointed in 9 different + directions. As the instrument flies overhead, each piece of Earth's surface below + is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, + red, and near-infrared). The goal of MISR is to improve our understanding of the + affects of sunlight on Earth, as well as distinguish different types of clouds, + particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term + trends in three areas: 1) amount and type of atmospheric particles (aerosols), including + those formed by natural sources and by human activities; 2) amounts, types, and + heights of clouds, and 3) distribution of land surface cover, including vegetation + canopy structure.\nRead our doc on how to get AWS Credentials to retrieve this data: + https://data.asdc.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/TERRA/MISR/MI1B2E_L1.004 +Contact: 'ASDC USER SERVICES: support-asdc@earthdata.nasa.gov. Home Page: https://asdc.larc.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 1999-12-18 to Ongoing +Tags: + - aws-pds + - atmosphere + - climate + - cyclone typhoon hurricane + - datacenter + - earth observation + - global + - land + - opendap + - orbit +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MISR Level 1B2 Ellipsoid Data V004. (Format: netCDF-4)' + ARN: arn:aws:s3:::asdc-prod-protected/MISR/MI1B2E_004 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.asdc.earthdata.nasa.gov/s3credentialsREADME +DataAtWork: + Publications: ~ + Tutorials: ~ diff --git a/datasets/nasa-mod02hkm.yaml b/datasets/nasa-mod02hkm.yaml new file mode 100644 index 000000000..f1505c114 --- /dev/null +++ b/datasets/nasa-mod02hkm.yaml @@ -0,0 +1,78 @@ +Name: MODIS/Terra Calibrated Radiances 5-Min L1B Swath 500m +Description: "The MODIS/Terra Calibrated Radiances 5Min L1B Swath 500m data set contains + calibrated and geolocated at-aperture radiances for 7 discrete bands located in + the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated + from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical + units of W/(m^2 um sr). Additional data are provided including quality flags, error + estimates and calibration data.\r\n\r\nVisible, shortwave infrared, and near infrared + measurements are only made during the daytime (except band 26), while radiances + for the thermal infrared region (bands 20-25, 27-36) are measured continuously.\r\n\r\nChannels + 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, + for the MODIS L1B 500 m product, the 250 m band radiance data and their associated + uncertainties have been aggregated to 500m resolution. Thus the entire channel data + set has been co-registered to the same spatial scale in the 500 m product. Separate + L1B products are available for the 250 m resolution channels (MOD02QKM) and 1 km + resolution channels (MOD021KM). For the latter product, the 250 m and 500 m channel + data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values.\r\n + \ \r\nSpatial resolution for pixels at nadir is 500 km, degrading to 2.4 + km in the along-scan direction at the scan extremes. However, thanks to the overlapping + of consecutive swaths and respectively pixels there, the resulting resolution at + the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of + 705 km results in a 2330 km orbital swath width and provides global coverage every + one to two days. A single MODIS Level 1B 500 m granule will contain a scene built + from 203 scans sampled 2708 times in the cross-track direction, corresponding to + approximately 5 minutes worth of data; thus 288 granules will be produced per day. + Since an individual MODIS scan will contain 20 along-track spatial elements for + the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting + in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there + will be increasing scan overlap beyond about 20 degrees scan angle. \r\n\r\nTo + summarize, the MODIS L1B 500 m data product consists of:\r\n \r\n1. Calibrated + radiances, uncertainties and number of samples for (2) 250 m reflected solar bands + aggregated to 500 m resolution\r\n \r\n2. Calibrated radiances and uncertainties + for (5) 500 m reflected solar bands\r\n \r\n3. Geolocation for 1km pixels, + that must be interpolated to get 500 m pixel locations. For the relationship of + 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf.\r\n + \ \r\n4. Calibration data for all channels (scale and offset) \r\n \r\n5. + Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter + attributes of the data, as well as auxiliary information pertaining to instrument + status and data quality characterization users requiring all geolocation and solar/satellite + geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation + product (MOD03) from LAADS https://ladsweb.modaps.eosdis.nasa.gov/ . \r\n \r\nThe + shortname for this product is MOD02HKM and is stored in the Earth Observing System + Hierarchical Data Format (HDF-EOS). A typical MOD02HKM file size is approximately + 135 MB.\r\n \r\nEnvironmental information derived from MODIS L1B measurements + will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and + ocean phenomenology for a wide and diverse community of users throughout the world.\r\n\r\nSee + the MODIS Characterization Support Team webpage for more C6 product information + at:\r\n\r\nhttps://mcst.gsfc.nasa.gov/l1b/product-information\r\n\r\n\r\nor visit + Science Team homepage at:\r\nhttps://modis.gsfc.nasa.gov/data/dataprod/\nRead our + doc on how to get AWS Credentials to retrieve this data: https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/MODIS/MOD02HKM.061 +Contact: 'MODAPS USER SUPPORT TEAM: MODAPSUSO@lists.nasa.gov. Home Page: https://modaps.modaps.eosdis.nasa.gov/, MODIS Characterization Support Team (MCST): https://mcst.gsfc.nasa.gov/contact' +ManagedBy: NASA +UpdateFrequency: From 2000-02-24 to Ongoing +Tags: + - aws-pds + - atmosphere + - datacenter + - earth observation + - environmental + - global + - metadata + - opendap + - orbit + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Terra Calibrated Radiances 5-Min L1B Swath 500m.' + ARN: 'arn:aws:s3:::prod-lads/MOD02HKM' + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: MODIS Level 1B - Calibrated Radiances - Natural Color RGB - 500m + URL: https://fire.trainhub.eumetsat.int/docs/figure1_MODIS_L1B.html + AuthorName: EUMETSAT \ No newline at end of file diff --git a/datasets/nasa-mod09a1.yaml b/datasets/nasa-mod09a1.yaml new file mode 100644 index 000000000..fed483bf3 --- /dev/null +++ b/datasets/nasa-mod09a1.yaml @@ -0,0 +1,45 @@ +Name: MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V061 +Description: "The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 + Version 6.1 product provides an estimate of the surface spectral reflectance of + Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, + aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance + bands are two quality layers and four observation bands. For each pixel, a value + is selected from all the acquisitions within the 8-day composite period. The criteria + for the pixel choice include cloud and solar zenith. When several acquisitions meet + the criteria the pixel with the minimum channel 3 (blue) value is used. \n\nKnown + Issues\n* For complete information about known issues please refer to the [MODIS/VIIRS + Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes + from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved + by undergoing various calibration changes that include: changes to the response-versus-scan + angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections + to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections + to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* + A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead + our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/MODIS/MOD09A1.061 +Contact: 'User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact' +ManagedBy: NASA +UpdateFrequency: From 2000-02-18 to Ongoing (Weekly - < Monthly) +Tags: + - aws-pds + - earth observation + - geospatial + - global + - land + - opendap + - satellite imagery + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V061.' + ARN: arn:aws:s3:::lp-prod-protected/MOD09A1.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC \ No newline at end of file diff --git a/datasets/nasa-mod09ga.yaml b/datasets/nasa-mod09ga.yaml new file mode 100644 index 000000000..8b926f3a0 --- /dev/null +++ b/datasets/nasa-mod09ga.yaml @@ -0,0 +1,46 @@ +Name: MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061 +Description: "The MOD09GA Version 6.1 product provides an estimate of the surface + spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) + Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, + and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, + observation, and quality bands are a set of ten 1 kilometer (km) observation bands + and geolocation flags. The reflectance layers from the MOD09GA are used as the source + data for many of the MODIS land products. \n\nKnown Issues\n* For complete information + about known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes + from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved + by undergoing various calibration changes that include: changes to the response-versus-scan + angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections + to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections + to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* + A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead + our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/MODIS/MOD09GA.061 +Contact: 'User Services: lpdaac@usgs.gov' +ManagedBy: NASA +UpdateFrequency: From 2000-02-24 to Ongoing +Tags: + - aws-pds + - datacenter + - earth observation + - geospatial + - global + - hdf + - ice + - land + - opendap + - satellite imagery +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN + Grid V061.' + ARN: arn:aws:s3:::lp-prod-protected/MOD09GA.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC + AuthorURL: https://www.earthdata.nasa.gov/centers/lp-daac diff --git a/datasets/nasa-mod09gq.yaml b/datasets/nasa-mod09gq.yaml new file mode 100644 index 000000000..31b62f12c --- /dev/null +++ b/datasets/nasa-mod09gq.yaml @@ -0,0 +1,44 @@ +Name: MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V061 +Description: "The MOD09GQ Version 6.1 product provides an estimate of the surface + spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) + 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, + aerosols, and Rayleigh scattering. Along with the 250 m surface reflectance bands + are the Quality Assurance (QA) layer and five observation layers. This product is + intended to be used in conjunction with the quality and viewing geometry information + of the 500 m product (MOD09GA). \n\nKnown Issues\n* For complete information about + known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes + from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved + by undergoing various calibration changes that include: changes to the response-versus-scan + angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections + to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections + to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* + A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead + our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/MODIS/MOD09GQ.061 +Contact: 'User Services: lpdaac@usgs.gov' +ManagedBy: NASA +UpdateFrequency: From 2000-02-24 to Ongoing (Daily - < Weekly) +Tags: + - aws-pds + - datacenter + - earth observation + - geospatial + - global + - hdf + - ice + - land + - opendap +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V061.' + ARN: arn:aws:s3:::lp-prod-protected/MOD09GQ.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC + AuthorURL: https://www.earthdata.nasa.gov/centers/lp-daac diff --git a/datasets/nasa-mod13q1.yaml b/datasets/nasa-mod13q1.yaml new file mode 100644 index 000000000..00c7bdaed --- /dev/null +++ b/datasets/nasa-mod13q1.yaml @@ -0,0 +1,51 @@ +Name: MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061 +Description: "The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation + Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) + spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary + vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) + which is referred to as the continuity index to the existing National Oceanic and + Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) + derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), + which has improved sensitivity over high biomass regions. The algorithm chooses + the best available pixel value from all the acquisitions from the 16 day period. + The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.\n\nAlong + with the vegetation layers and the two quality layers, the HDF file will have MODIS + reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as + well as four observation layers. \n\nKnown Issues\n* For complete information about + known issues please refer to the [MODIS/VIIRS Land Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes + from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved + by undergoing various calibration changes that include: changes to the response-versus-scan + angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections + to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections + to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* + A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead + our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/MODIS/MOD13Q1.061 +Contact: 'User Services: lpdaac@usgs.gov' +ManagedBy: NASA +UpdateFrequency: From 2000-02-18 to Ongoing (Weekly - < Monthly) +Tags: + - aws-pds + - datacenter + - earth observation + - geospatial + - global + - hdf + - ice + - land + - opendap + - satellite imagery +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061.' + ARN: arn:aws:s3:::lp-prod-protected/MOD13Q1.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC + AuthorURL: https://www.earthdata.nasa.gov/centers/lp-daac diff --git a/datasets/nasa-mod16a2.yaml b/datasets/nasa-mod16a2.yaml new file mode 100644 index 000000000..1ccc336b8 --- /dev/null +++ b/datasets/nasa-mod16a2.yaml @@ -0,0 +1,57 @@ +Name: MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061 +Description: "The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product + is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm + used for the MOD16 data product collection is based on the logic of the Penman-Monteith + equation, which includes inputs of daily meteorological reanalysis data along with + Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products + such as vegetation property dynamics, albedo, and land cover. \n\nProvided in the + MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux + (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. + Two low resolution browse images, ET and LE, are also available for each MOD16A2 + granule.\n\nThe pixel values for the two Evapotranspiration layers (ET and PET) + are the sum of all eight days within the composite period and the pixel values for + the two Latent Heat layers (LE and PLE) are the average of all eight days within + the composite period. Note that the last acquisition period of each year is a 5 + or 6-day composite period, depending on the year.\n\nKnown Issues\n* Operational + and uncertainty issues are provided under Section 3 in the User Guide.\n* For complete + information about known issues please refer to the [MODIS/VIIRS Land Quality Assessment + website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Terra&as=61).\n\nImprovements/Changes + from Previous Versions\n* The Version 6.1 Level-1B (L1B) products have been improved + by undergoing various calibration changes that include: changes to the response-versus-scan + angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections + to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections + to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* + A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\n* + The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR.\nRead + our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/MODIS/MOD16A2.061 +Contact: 'User Services: lpdaac@usgs.gov. Home Page: https://www.earthdata.nasa.gov/centers/lp-daac/contact' +ManagedBy: NASA +UpdateFrequency: From 2021-01-01 to Ongoing (Weekly - < Monthly) +Tags: + - aws-pds + - atmosphere + - earth observation + - evapotranspiration + - geospatial + - global + - land + - land cover + - opendap + - water + - hdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid + V061.' + ARN: arn:aws:s3:::lp-prod-protected/MOD16A2.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Publications: ~ + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/7f58ad3abeca7f0d17637ddb812642c0120a57ab/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC \ No newline at end of file diff --git a/datasets/nasa-modis-t-jpl-l2p-v2019-0.yaml b/datasets/nasa-modis-t-jpl-l2p-v2019-0.yaml new file mode 100644 index 000000000..4f431e7eb --- /dev/null +++ b/datasets/nasa-modis-t-jpl-l2p-v2019-0.yaml @@ -0,0 +1,39 @@ +Name: GHRSST Level 2P Global Sea Surface Skin Temperature from the MODIS on the NASA Terra satellite (GDS2) +Description: |- + NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project, and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDT-2PJ02 + Read our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/GHMDT-2PJ19 +Contact: 'Help Desk: podaac@podaac.jpl.nasa.gov. Home Page: https://podaac.jpl.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 2000-02-24 to Ongoing (Hourly - < Daily) +Tags: + - aws-pds + - atmosphere + - datacenter + - earth observation + - global + - land + - marine + - metadata + - oceans + - orbit + - netcdf +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GHRSST Level 2P Global Sea Surface Skin Temperature from the Moderate + Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra satellite (GDS2).' + ARN: arn:aws:s3:::podaac-ops-cumulus-protected/MODIS_T-JPL-L2P-v2019.0/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://archive.podaac.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: A decade of sea surface temperature from MODIS + URL: http://dx.doi.org/10.1016/j.rse.2015.04.023 + AuthorName: Kilpatrick, K.A., Podestá, G., Walsh, S., Williams, E., Halliwell, + V., Szczodrak, M., Brown, O.B., Minnett, P.J., & Evans, R. + Tutorials: + - Title: Direct S3 Access tutorial + URL: https://github.com/podaac/tutorials/blob/3d2ac9cb3626b656802638f864631a68beb11823/notebooks/s3/S3-Access.ipynb#L33 + AuthorName: PODAAC \ No newline at end of file diff --git a/datasets/nasa-mur-jpl-l4-glob-v41.yaml b/datasets/nasa-mur-jpl-l4-glob-v41.yaml new file mode 100644 index 000000000..b49733597 --- /dev/null +++ b/datasets/nasa-mur-jpl-l4-glob-v41.yaml @@ -0,0 +1,43 @@ +Name: GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1) +Description: |- + A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.This dataset is funded by the NASA MEaSUREs program ( http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata "history:" attribute to determine if a granule is near-realtime or retrospective. + Read our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/GHGMR-4FJ04 +Contact: 'Help Desk: podaac@podaac.jpl.nasa.gov' +ManagedBy: NASA +UpdateFrequency: From 2002-05-31 to Ongoing (Hourly - < Daily) +Tags: + - aws-pds + - datacenter + - earth observation + - global + - ice + - metadata + - oceans + - parquet + - us + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis + (v4.1). (Format: netCDF-4)' + ARN: arn:aws:s3:::podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME +DataAtWork: + Publications: + - Title: A multi-scale high-resolution analysis of global sea surface temperature + URL: https://doi.org/10.1016/j.rse.2017.07.029 + AuthorName: Chin, T.M, J. Vazquez-Cuervo, and E.M. Armstrong + Tutorials: + - Title: MUR Sea Surface Temperature Analysis of Washington State + URL: https://podaac.github.io/tutorials/notebooks/datasets/MUR_SST_Washington_Comparison.html + AuthorName: Zoë Walschots + NotebookURL: https://github.com/podaac/tutorials/blob/master/notebooks/datasets/MUR_SST_Washington_Comparison.ipynb + - Title: Using Sea Surface Temperature and Sea Surface Height Data for Hurricane + Helene + URL: https://podaac.github.io/tutorials/notebooks/DataStories/HurricaneHelene_SST_SSH_Notebook.html + AuthorName: Julie Sanchez + NotebookURL: https://github.com/podaac/tutorials/blob/4466294936f7c992a78bd2954a9f4909784bf0ba/notebooks/DataStories/HurricaneHelene_SST_SSH_Notebook.ipynb diff --git a/datasets/nasa-myd09ga.yaml b/datasets/nasa-myd09ga.yaml new file mode 100644 index 000000000..b27acb2dc --- /dev/null +++ b/datasets/nasa-myd09ga.yaml @@ -0,0 +1,53 @@ +Name: MODIS/Aqua Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061 +Description: "The MYD09GA Version 6.1 product provides an estimate of the surface + spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) + Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, + and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, + observation, and quality bands are a set of ten 1 kilometer observation bands and + geolocation flags. The reflectance layers from the MYD09GA are used as the source + data for many of the MODIS land products. \n\nKnown Issues\n* Prior to the Aqua + MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded + seriously after launch and presently a majority of the Band 6 detectors are non-functional. + Science users should read and use the non-functional detector flags and decide for + themselves the optimum manner to handle non-functional detector \"gaps\" for their + products. For complete information please refer to the [MODIS Characterization Support + Team (MCST) website](https://mcst.gsfc.nasa.gov/time-dependent-list-non-functional-or-noisy-detector).\n* + For complete information about known issues please refer to the [MODIS/VIIRS Land + Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n\nImprovments/Changes + from Previous Version\n* The Version 6.1 Level-1B (L1B) products have been improved + by undergoing various calibration changes that include: changes to the response-versus-scan + angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections + to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections + to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* + A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead + our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/MODIS/MYD09GA.061 +Contact: 'User Services: lpdaac@usgs.gov' +ManagedBy: NASA +UpdateFrequency: From 2002-07-04 to Ongoing (Daily - < Weekly) +Tags: + - aws-pds + - datacenter + - earth observation + - geospatial + - global + - hdf + - ice + - land + - opendap + - satellite imagery +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Aqua Surface Reflectance Daily L2G Global 1km and 500m SIN + Grid V061.' + ARN: arn:aws:s3:::lp-prod-protected/MYD09GA.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC + AuthorURL: https://www.earthdata.nasa.gov/centers/lp-daac diff --git a/datasets/nasa-myd09gq.yaml b/datasets/nasa-myd09gq.yaml new file mode 100644 index 000000000..7cf2aa2af --- /dev/null +++ b/datasets/nasa-myd09gq.yaml @@ -0,0 +1,50 @@ +Name: MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid V061 +Description: "The MYD09GQ Version 6.1 product provides an estimate of the surface + spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) + 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, + aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance + (QA) layer and five observation layers. This product is intended to be used in conjunction + with the quality and viewing geometry information of the 500 m product (MYD09GA). + \n\nKnown Issues\n* Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous + detectors. Band 6 performance degraded seriously after launch and presently a majority + of the Band 6 detectors are non-functional. Science users should read and use the + non-functional detector flags and decide for themselves the optimum manner to handle + non-functional detector \"gaps\" for their products. For complete information please + refer to the [MODIS Characterization Support Team (MCST) website](https://mcst.gsfc.nasa.gov/time-dependent-list-non-functional-or-noisy-detector).\n* + For complete information about known issues please refer to the [MODIS/VIIRS Land + Quality Assessment website](https://landweb.modaps.eosdis.nasa.gov/knownissue?sensor=MODIS&sat=Aqua&as=61).\n\nImprovments/Changes + from Previous Version\n* The Version 6.1 Level-1B (L1B) products have been improved + by undergoing various calibration changes that include: changes to the response-versus-scan + angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections + to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections + to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.\n* + A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).\nRead + our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/MODIS/MYD09GQ.061 +Contact: 'User Services: lpdaac@usgs.gov' +ManagedBy: NASA +UpdateFrequency: From 2002-07-04 to Ongoing (Daily - < Weekly) +Tags: + - aws-pds + - datacenter + - earth observation + - geospatial + - global + - hdf + - ice + - land + - opendap +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid V061.' + ARN: arn:aws:s3:::lp-prod-protected/MYD09GQ.061 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Download Files from S3 Using boto3 + URL: https://github.com/nasa/LPDAAC-Data-Resources/blob/main/python/how-tos/Earthdata_Cloud__Download_file_from_S3.ipynb + AuthorName: LPDAAC + AuthorURL: https://www.earthdata.nasa.gov/centers/lp-daac diff --git a/datasets/nasa-operal2cslc-s1-staticv1.yaml b/datasets/nasa-operal2cslc-s1-staticv1.yaml new file mode 100644 index 000000000..8aea817af --- /dev/null +++ b/datasets/nasa-operal2cslc-s1-staticv1.yaml @@ -0,0 +1,39 @@ +Name: OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1) +Description: |- + The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) Static Layers (CSLC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CSLC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static layers generation algorithm. Each OPERA CSLC-S1-STATIC product is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers and product metadata and corresponds to matching CSLC-S1 products with the same burst ID. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The CSLC-S1 products are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1) dataset. + Read our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME +Documentation: https://doi.org/10.5067/SNWG/OPERA_L2_CSLC-S1-STATIC_V1 +Contact: 'Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/' +ManagedBy: NASA +UpdateFrequency: From 2014-04-03 to Ongoing +Tags: + - aws-pds + - coastal + - earth observation + - hdf + - ice + - land + - metadata + - oceans + - orbit + - radar + - sentinel-1 + - synthetic aperture radar + - xml +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers + validated product (Version 1).' + ARN: arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L2_CSLC-S1_STATIC/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://cumulus.asf.alaska.edu/s3credentials +DataAtWork: + Tutorials: + - Title: Generate inteferograms without the need to download OPERA CSLC-S1 products locally + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/CSLC/Discover/Create_Interferogram_by_Streaming_CSLC-S1.ipynb + AuthorName: M. Grace Bato and K. Devlin + - Title: Generate interferograms and map the lava flow emplacement using OPERA CSLC-S1 + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/CSLC/Volcano/Map_Deformation_LavaFlow_using_CSLC-S1.ipynb + AuthorName: M. Grace Bato diff --git a/datasets/nasa-operal2cslc-s1v1.yaml b/datasets/nasa-operal2cslc-s1v1.yaml new file mode 100644 index 000000000..e2128a349 --- /dev/null +++ b/datasets/nasa-operal2cslc-s1v1.yaml @@ -0,0 +1,63 @@ +Name: OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1) +Description: "The Observational Products for End-Users from Remote Sensing Analysis + (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product + consists of Single Look Complex (SLC) images which contain both amplitude and phase + information of the complex radar return. The amplitude is primarily determined by + ground surface properties (e.g., terrain slope, surface roughness, and physical + properties), and phase primarily represents the distance between the radar and ground + targets corrected for the geometrical distance between the two based on the knowledge + from Digital Elevation Model and platform’s position, i.e., the CSLC phase represents + residual geometrical distance between the sensor and target, the atmospheric propagation + delay and the target movements. The CSLC-S1 product is derived from Copernicus Sentinel-1A + and Sentinel-1B Interferometric Wide (IW) SLC data. \n\nThe CSLC images are precisely + aligned or “coregistered” to a pre-defined UTM/Polar stereographic map projection + systems and posted at 5x10 m spacing in east and north direction, respectively. + \ Each CSLC-S1 product corresponds to a single S1 burst and is distributed as a + Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing + both data raster layers (e.g., geocoded complex backscatter, low-resolution correction + look-up tables) and product metadata. OPERA CSLC-S1 products are available over + North America which includes the USA and U.S. Territories, Canada within 200 km + of the U.S. border, and all mainland countries from the southern U.S. border down + to and including Panama. The OPERA CSLC-S1 product contains modified Copernicus + Sentinel data (2016-2025).\n\nDue to the S1 mission’s narrow orbital tube, radar-geometry + layers vary slightly over time for each position on the ground, and therefore are + considered static. These static layers are provided separately from the OPERA CLSLC-S1 + product, as they are produced only once or a limited number of times. The static + layers are available in the associated OPERA Coregistered Single-Look Complex from + Sentinel-1 Static Layers validated product (Version 1).\nRead our doc on how to + get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME" +Documentation: https://doi.org/10.5067/SNWG/OPERA_L2_CSLC-S1_V1 +Contact: 'Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/' +ManagedBy: NASA +UpdateFrequency: From 2014-06-15 to Ongoing +Tags: + - aws-pds + - coastal + - earth observation + - hdf + - ice + - land + - metadata + - oceans + - orbit + - radar + - sentinel-1 + - synthetic aperture radar + - xml +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Coregistered Single-Look Complex from Sentinel-1 validated + product (Version 1).' + ARN: arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L2_CSLC-S1/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://cumulus.asf.alaska.edu/s3credentialsREADME +DataAtWork: + Tutorials: + - Title: Generate inteferograms without the need to download OPERA CSLC-S1 products locally + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/CSLC/Discover/Create_Interferogram_by_Streaming_CSLC-S1.ipynb + AuthorName: M. Grace Bato and K. Devlin + - Title: Generate interferograms and map the lava flow emplacement using OPERA CSLC-S1 + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/CSLC/Volcano/Map_Deformation_LavaFlow_using_CSLC-S1.ipynb + AuthorName: M. Grace Bato diff --git a/datasets/nasa-operal2rtc-s1-staticv1.yaml b/datasets/nasa-operal2rtc-s1-staticv1.yaml new file mode 100644 index 000000000..f784de02c --- /dev/null +++ b/datasets/nasa-operal2rtc-s1-staticv1.yaml @@ -0,0 +1,47 @@ +Name: OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1) +Description: |- + The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) Static Layers (RTC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) (RTC-S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. Static layers are provided as single-band cloud-optimized GeoTIFF (COG) files, with map grid matching RTC-S1 products with the same burst ID. The standard OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 (S1) interferometric wide (IW) single-look complex (SLC) data, provided by the European Space Agency, with a temporal sampling coincident with the availability of Sentinel-1A and Sentinel-1B SLC data. The OPERA RTC-S1-STATIC and RTC-S1 products are provided at a near global scope (land masses excluding Antarctica). The RTC-S1 products are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1) dataset. + Read our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME +Documentation: https://doi.org/10.5067/SNWG/OPERA_L2_RTC-S1-STATIC_V1 +Contact: 'Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/' +ManagedBy: NASA +UpdateFrequency: From 2014-04-03 to Ongoing +Tags: + - aws-pds + - coastal + - cog + - earth observation + - geoscience + - global + - ice + - land + - metadata + - oceans + - orbit + - radar + - sentinel-1 + - synthetic aperture radar + - tiff + - xml +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 + Static Layers validated product (Version 1).' + ARN: arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L2_RTC-S1_STATIC/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://cumulus.asf.alaska.edu/s3credentialsREADME +DataAtWork: + Publications: + - Title: An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction + and Geocoding + URL: https://doi.org/10.1109/TGRS.2022.3147472 + AuthorName: Gustavo H. X. Shiroma, Marco Lavalle, and Sean M. Buckley + Tutorials: + - Title: Load, Mosaic, and Visualize OPERA RTC-S1 Data + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/RTC/notebooks/RTC_notebook.ipynb + AuthorName: K. Venkataramani + - Title: RTC Landslide Example + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/RTC/notebooks/RTC_landslide_example.ipynb + AuthorName: K. Venkataramani diff --git a/datasets/nasa-operal2rtc-s1v1.yaml b/datasets/nasa-operal2rtc-s1v1.yaml new file mode 100644 index 000000000..1ea9c9299 --- /dev/null +++ b/datasets/nasa-operal2rtc-s1v1.yaml @@ -0,0 +1,74 @@ +Name: OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1) +Description: "The Observational Products for End-Users from Remote Sensing Analysis + (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) + validated product consists of radar backscatter normalized with respect to the topography. + The product maps signals related to the physical properties of ground scattering + objects, such as surface roughness and soil moisture and/or vegetation. The OPERA + RTC-S1 product is derived from Copernicus Sentinel-1 Interferometric Wide (IW) Single + Look Complex (SLC) data with a near global scope and temporal sampling coincident + with the availability of S1 SLC data. \n\nEach OPERA RTC-S1 product corresponds + to a single S1 burst projected onto a pre-defined UTM/Polar stereographic map projection + system map grid with a 30-meter spacing. The Copernicus global 30 m (GLO-30) Digital + Elevation Model (DEM) is the reference DEM used to correct for the impacts of topography + and to geocode the product. The OPERA RTC-S1 product is normalized to the backscatter + coefficient gamma-naught, ɣ0, obtained from the original radar brightness beta-naught, + β0, through radiometric terrain correction. The RTC-S1 product is distributed + as cloud optimized GeoTIFFs with one GeoTIFF file per processed polarization. The + RTC-S1 product metadata is provided in the Hierarchical Data Format version 5 (HDF5) + format. The OPERA RTC-S1 product contains modified Copernicus Sentinel data (2016-2025).\n\nDue + to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence + angle, local incidence angle, number of looks, and RTC Area Normalization Factor + (ANF) vary slightly over time for each position on the ground, and therefore are + considered static. These static layers are provided separately from the OPERA RTC-S1 + product, as they are produced only once or a limited number of times, to account + for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. + The static layers are available in the associated OPERA Radiometric Terrain Corrected + SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1) dataset.\nRead + our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME" +Documentation: https://doi.org/10.5067/SNWG/OPERA_L2_RTC-S1_V1 +Contact: 'Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/' +ManagedBy: NASA +UpdateFrequency: From 2016-04-14 to Ongoing +Tags: + - aws-pds + - coastal + - earth observation + - geoscience + - global + - hdf + - ice + - land + - metadata + - oceans + - orbit + - radar + - sentinel-1 + - soil moisture + - synthetic aperture radar + - tiff + - xml +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 + validated product (Version 1).' + ARN: arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L2_RTC-S1/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://cumulus.asf.alaska.edu/s3credentials +DataAtWork: + Publications: + - Title: An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction + and Geocoding + URL: https://doi.org/10.1109/TGRS.2022.3147472 + AuthorName: Gustavo H. X. Shiroma, Marco Lavalle, and Sean M. Buckley + - Title: Thermal Denoising of Products Generated by the S-1 IPF + URL: https://sentinels.copernicus.eu/documents/247904/2142675/Thermal-Denoising-of-Products-Generated-by-Sentinel-1-IPF.pdf + AuthorName: Riccardo Piantanida + Tutorials: + - Title: Load, Mosaic, and Visualize OPERA RTC-S1 Data + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/RTC/notebooks/RTC_notebook.ipynb + AuthorName: K. Venkataramani + - Title: RTC Landslide Example + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/RTC/notebooks/RTC_landslide_example.ipynb + AuthorName: K. Venkataramani diff --git a/datasets/nasa-operal3disp-s1v1.yaml b/datasets/nasa-operal3disp-s1v1.yaml new file mode 100644 index 000000000..36cbcb5e6 --- /dev/null +++ b/datasets/nasa-operal3disp-s1v1.yaml @@ -0,0 +1,48 @@ +Name: OPERA Surface Displacement from Sentinel-1 validated product (Version 1) +Description: "The Level-3 OPERA Sentinel-1 Surface Displacement (DISP) product is + generated through interferometric time-series analysis of Level-2 Coregistered Sentinel-1 + Single Look Complex (CSLC) datasets. Using a hybrid Persistent Scatterer (PS) and + Distributed Scatterer (DS) approach, this product quantifies Earth's surface displacement + in the radar line-of-sight. The DISP products enable the detection of anthropogenic + and natural surface changes, including subsidence, tectonic deformation, and landslides. + \n\nThe OPERA DISP suite comprises complementary datasets derived from Sentinel-1 + and NISAR inputs, designated as DISP-S1 and DISP-NI, respectively. Each product, + created per acquisition, adheres to a consistent structure, HDF5 file format, file-naming + convention, and a 30 m spatial posting. This collection specifically includes DISP-S1 + products, derived from Sentinel-1 data. \n\nDISP-S1 products provide spatial coverage + across North America, encompassing the United States, U.S. territories within 200 + km of the U.S. border, Canada, and mainland countries from the southern U.S. border + to Panama. These products are generated from Sentinel-1 Interferometric Wide (IW) + swath mode acquisitions starting in mid-2016. \n\nThe OPERA DISP-S1 product contains + modified Copernicus Sentinel data (2016-2025).\nRead our doc on how to get AWS Credentials + to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME" +Documentation: https://doi.org/10.5067/SNWG/OPL3DISPS1-V1 +Contact: 'Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/' +ManagedBy: NASA +UpdateFrequency: From 2016-07-01 to Ongoing +Tags: + - aws-pds + - earth observation + - land + - metadata + - netcdf + - orbit + - radar + - sentinel-1 + - synthetic aperture radar + - xml + - zarr +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Surface Displacement from Sentinel-1 validated product (Version + 1).' + ARN: arn:aws:s3:::asf-cumulus-prod-opera-products/OPERA_L3_DISP-S1_V1/ + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://cumulus.asf.alaska.edu/s3credentials +DataAtWork: + Tutorials: + - Title: Inspect DISP-S1 Layers + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DISP/Timeseries/opera_disp/inspect_DISP-S1_layers.ipynb + AuthorName: M. Grace Bato diff --git a/datasets/nasa-operal3dist-alert-hlsprovisionalv0.yaml b/datasets/nasa-operal3dist-alert-hlsprovisionalv0.yaml new file mode 100644 index 000000000..dcf283261 --- /dev/null +++ b/datasets/nasa-operal3dist-alert-hlsprovisionalv0.yaml @@ -0,0 +1,57 @@ +Name: OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 provisional product (Version 0) +Description: "The OPERA_L3_DIST-ALERT-HLS Version 0 data product was decommissioned + on April 25, 2025. Users are encouraged to use the [OPERA_L3_DIST-ALERT-HLS V1](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) + data product which was released on March 14, 2024, and has achieved stage 1 validation.\n\nThe + Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface + Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) provisional data product + Version 0 maps vegetation disturbance alerts from data collected by Landsat 8 and + Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C + Multi-Spectral Instrument (MSI). Vegetation disturbance alert is detected at 30 + meter (m) spatial resolution when there is an indicated decrease in vegetation cover + within an HLS pixel. The product also provides auxiliary generic disturbance information + as determined from the variations of the reflectance through the HLS scenes to provide + information about more general disturbance trends. HLS data represent the highest + temporal frequency data available at medium spatial resolution. The combined observations + will provide greater sensitivity to land changes, whether of large magnitude/short + duration, or small magnitude/long duration. \n\nThe OPERA_L3_DIST-ALERT-HLS (or + DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and + each layer is distributed as a separate file. There are 19 layers contained within + in the DIST-ALERT product: vegetation disturbance status, current vegetation cover + indicator, current vegetation anomaly value, historical vegetation cover indicator, + max vegetation anomaly value, vegetation disturbance confidence layer, date of initial + vegetation disturbance, number of detected vegetation loss anomalies, and vegetation + disturbance duration. See the Product Specification for a more detailed description + of the individual layers provided in the DIST-ALERT product. \n\nKnown Issues\n* + Additional usage constraints are provided under Section 5 of the Algorithm Theoretical + Basis Document (ATBD).\nRead our doc on how to get AWS Credentials to retrieve this + data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_PROVISIONAL_V0.000 +Contact: 'Email: lpdaac@usgs.gov. Home Page: https://lpdaac.usgs.gov/' +ManagedBy: NASA +UpdateFrequency: From 2022-01-01 to 2024-02-26 +Tags: + - aws-pds + - cog + - earth observation + - environmental + - global + - land + - land cover + - land use +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 + provisional product (Version 0).' + ARN: arn:aws:s3:::lp-protected/OPERA_DIST-ALERT-HLS_PROVISIONAL_V0.000 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Getting Started with OPERA DIST-ALERT-HLS Products + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ALERT/Discover/Stream_and_Viz_DIST-ALERT-folium.ipynb + AuthorName: R. Dhillon and M. Grace Bato + - Title: Getting Started with OPERA DIST Product + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ALERT/Wildfire/Intro_To_DIST.ipynb + AuthorName: M. Grace Bato and R. Dhillon diff --git a/datasets/nasa-operal3dist-alert-hlsv1.yaml b/datasets/nasa-operal3dist-alert-hlsv1.yaml new file mode 100644 index 000000000..f5c485c2b --- /dev/null +++ b/datasets/nasa-operal3dist-alert-hlsv1.yaml @@ -0,0 +1,42 @@ +Name: OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1) +Description: |- + The Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. [HLS](https://lpdaac.usgs.gov/product_search/?collections=HLS&status=Operational&view=list) data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration. + + The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ALERT product. + + The OPERA_L3_DIST-ALERT-HLS product contains modified Copernicus Sentinel data (2020-2025). + + Known Issues + * Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD). + Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME +Documentation: https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001 +Contact: 'Email: lpdaac@usgs.gov. Home Page: https://lpdaac.usgs.gov/' +ManagedBy: NASA +UpdateFrequency: From 2022-01-01 to Ongoing (Daily - < Weekly) +Tags: + - aws-pds + - cog + - earth observation + - environmental + - global + - land + - land cover + - land use + - satellite imagery +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 + product (Version 1).' + ARN: arn:aws:s3:::lp-protected/OPERA_L3_DIST-ALERT-HLS_V1.001 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Getting Started with OPERA DIST-ALERT-HLS Products + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ALERT/Discover/Stream_and_Viz_DIST-ALERT-folium.ipynb + AuthorName: R. Dhillon and M. Grace Bato + - Title: Getting Started with OPERA DIST Product + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ALERT/Wildfire/Intro_To_DIST.ipynb + AuthorName: M. Grace Bato and R. Dhillon diff --git a/datasets/nasa-operal3dist-ann-hlsv1.yaml b/datasets/nasa-operal3dist-ann-hlsv1.yaml new file mode 100644 index 000000000..bad381dfc --- /dev/null +++ b/datasets/nasa-operal3dist-ann-hlsv1.yaml @@ -0,0 +1,64 @@ +Name: OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 product (Version 1) +Description: "The Observational Products for End-Users from Remote Sensing Analysis + ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Annual from + Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the [DIST-ALERT](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) + data product into an annual vegetation disturbance data product. Vegetation disturbance + is mapped when there is an indicated decrease in vegetation cover within an HLS + Version 2 pixel. The product also provides auxiliary generic disturbance information + as determined from the variations of the reflectance through the DIST-ALERT scenes + to provide information about more general disturbance trends. The DIST-ANN product + tracks changes at the annual scale, aggregating changes identified in the DIST-ALERT + product. Only confirmed disturbances from the associated year are reported together + with the date of initial disturbance. As confirmed disturbances are determined using + subsequent cloud-free observations to determine if the loss detections persist, + the required number of HLS scenes depends on visibility of the target. Due to this + dependency, summarizing the DIST-ALERT in the DIST-ANN product will have some latency + contingent on the algorithmic calibration and is detailed in the Algorithm Theoretical + Basis Document (ATBD).\n\nThe OPERA_L3_DIST-ANN-HLS (or DIST-ANN) data product is + provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed + as a separate COG. There are 21 layers contained within the DIST-ANN product: vegetation + disturbance status, historical vegetation cover indicator, maximum vegetation cover + indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, + date of initial vegetation disturbance, number of detected vegetation loss anomalies, + vegetation disturbance duration, date of last observation assessed for vegetation + disturbance, and several generic disturbance layers. Each product layer is gridded + to the same resolution and tiling system as HLS V2: 30 meter (m) and Military Grid + Reference System (MGRS). See the Product Specification Document (PSD) for a more + detailed description of the individual layers provided in the DIST-ANN product. + \n\nThe OPERA_L3_DIST-ANN-HLS product contains modified Copernicus Sentinel data + (2020-2025).\n\nKnown Issues\n* Additional usage constraints are provided under + Section 5 of the Algorithm Theoretical Basis Document (ATBD).\nRead our doc on how + to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ANN-HLS_V1.001 +Contact: 'Email: lpdaac@usgs.gov. Home Page: https://lpdaac.usgs.gov/' +ManagedBy: NASA +UpdateFrequency: From 2022-01-01 to Ongoing (Annual) +Tags: + - aws-pds + - cog + - earth observation + - environmental + - global + - land + - land cover + - land use +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 + product (Version 1).' + ARN: arn:aws:s3:::lp-protected/OPERA_L3_DIST-ANN-HLS_V1.001 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Visualization and Exploration of OPERA DIST-ANN-HLS Product Layers + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ANN/Intro_To_DIST_ANN.ipynb + AuthorName: C. Speed and M. Grace Bato + - Title: Visualizing and Analyzing the OPERA DIST-ANN-HLS Product to Explore Land-Use Change in Brazil + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ANN/Land_Use_Change_DIST_ANN.ipynb + AuthorName: C. Speed and M. Grace Bato + - Title: Visualizing and Analyzing the OPERA DIST-ANN-HLS Product to Visualize Wildfire Impact in Northern Quebec + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DIST/DIST_ANN/Wildfires_DIST_ANN.ipynb + AuthorName: C. Speed and M. Grace Bato diff --git a/datasets/nasa-operal3dswx-hlsv1.yaml b/datasets/nasa-operal3dswx-hlsv1.yaml new file mode 100644 index 000000000..0c3818c04 --- /dev/null +++ b/datasets/nasa-operal3dswx-hlsv1.yaml @@ -0,0 +1,71 @@ +Name: OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 product (Version 1) +Description: "This dataset contains Level-3 Dynamic OPERA surface water extent product + version 1. The data are validated surface water extent observations beginning April + 2023. Known issues and caveats on usage are described under Documentation. The input + dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C + (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from + the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral + Instrument (MSI) aboard the Sentinel-2A/B/C satellite. The surface water extent + products are distributed over projected map coordinates using the Universal Transverse + Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. + This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each + product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) + files including water classification, associated confidence, land cover classification, + terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model + (DEM), and Diagnostic layer.\n

\nThe digital elevation model (DEM) provided + as a layer of the DSWx-HLS product (band 10) was generated using the Copernicus + DEM 30-m and Copernicus DEM 90-m models provided by the European Space Agency. The + Copernicus DEM 30-m and Copernicus DEM 90-m were produced using Copernicus WorldDEM-30 + © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under + COPERNICUS by the European Union and ESA; all rights reserved. The organizations + in charge of the OPERA project, the Copernicus programme, and Airbus Defence and + Space GmbH by law or by delegation do not assume any legal responsibility or liability, + whether express or implied, arising from the use of this DEM.\n

\nThe OPERA + DSWx-HLS product contains modified Copernicus Sentinel data (2023-2025).\n

\nTo + access the calibration/validation database for OPERA Dynamic Surface Water Extent + Products, please contact podaac@podaac.jpl.nasa.gov \nRead our doc on how to get + AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/OPDSW-PL3V1 +Contact: 'Help Desk: podaac@podaac.jpl.nasa.gov. Home Page: https://podaac.jpl.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 2023-04-04 to Ongoing (Daily - < Weekly) +Tags: + - aws-pds + - cog + - datacenter + - earth observation + - ice + - land + - land cover + - metadata + - surface water + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 + product (Version 1).' + ARN: arn:aws:s3:::podaac-ops-cumulus-protected/OPERA_L3_DSWX-HLS_V1 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://archive.podaac.earthdata.nasa.gov/s3credentials +DataAtWork: + Publications: + - Title: Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic + Surface Water Extent (DSWE) Partial Surface Water Tests + URL: https://doi.org/10.3390/rs11040374 + AuthorName: Jones, John W + Tutorials: + - Title: Working with OPERA Dynamic Surface Water Extent (DSWx) Data + URL: https://podaac.github.io/tutorials/notebooks/datasets/OPERA_GIS_Cloud.html + AuthorName: Nicholas Tarpinian + NotebookURL: https://github.com/podaac/tutorials/blob/master/notebooks/datasets/OPERA_GIS_Cloud.ipynb + - Title: Access DSWx-HLS S3 + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DSWx/Discover/Access_DSWx-HLS_S3.ipynb + AuthorName: M. Grace Bato + - Title: Stream and Viz DSWx-HLS via Direct HTTPS + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DSWx/Discover/Stream_and_Viz_DSWx-HLS_viaDirectHTTPS.ipynb + AuthorName: M. Grace Bato + - Title: Getting Started with OPERA DSWx Product + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DSWx/Reservoir/Intro_to_DSWx.ipynb + AuthorName: K. Devlin and M. Grace Bato diff --git a/datasets/nasa-operal3dswx-s1v1.yaml b/datasets/nasa-operal3dswx-s1v1.yaml new file mode 100644 index 000000000..7aa2340e4 --- /dev/null +++ b/datasets/nasa-operal3dswx-s1v1.yaml @@ -0,0 +1,48 @@ +Name: OPERA Dynamic Surface Water Extent from Sentinel-1 (Version 1) +Description: "This dataset contains Level-3 Dynamic OPERA Surface Water Extent from + Sentinel-1 (DSWx-S1) product version 1. DSWx-S1 provides near-global geographical + mapping of surface water extent over land at a spatial resolution of 30 meters over + the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency + between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland + water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud + conditions and daylight illumination that often pose challenges to optical sensors. + Forward production of the DSWx-S1 data record began in Sept 2024. Each product + is distributed as a set of 3 GeoTIFF (Geographic Tagged Image File Format) files + including water classification and associated confidence layers.\n

\nThe + OPERA DSWx-S1 product contains modified Copernicus Sentinel data (2024-2025).\n

\nTo + access the calibration/validation database for OPERA Dynamic Surface Water Extent + Products, please contact podaac@podaac.jpl.nasa.gov \nRead our doc on how to get + AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME" +Documentation: https://doi.org/10.5067/OPDSWS1-L3V1 +Contact: 'Help Desk: podaac@podaac.jpl.nasa.gov. Home Page: https://podaac.jpl.nasa.gov/' +ManagedBy: NASA +UpdateFrequency: From 2024-08-01 to Ongoing (Daily - < Weekly) +Tags: + - aws-pds + - cog + - datacenter + - earth observation + - global + - land + - orbit + - radar + - sentinel-1 + - surface water + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'OPERA Dynamic Surface Water Extent from Sentinel-1 (Version 1).' + ARN: arn:aws:s3:::podaac-ops-cumulus-protected/OPERA_L3_DSWX-S1_V1 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://archive.podaac.earthdata.nasa.gov/s3credentials +DataAtWork: + Tutorials: + - Title: Working with OPERA Dynamic Surface Water Extent (DSWx) Data + URL: https://podaac.github.io/tutorials/notebooks/datasets/OPERA_GIS_Cloud.html + AuthorName: Nicholas Tarpinian + NotebookURL: https://github.com/podaac/tutorials/blob/master/notebooks/datasets/OPERA_GIS_Cloud.ipynb + - Title: Generate Flood Maps without downloading OPERA DSWx-S1 products locally + URL: https://github.com/OPERA-Cal-Val/OPERA_Applications/blob/main/DSWx/Flood/Brazil_DSWx-S1_FloodProduct.ipynb + AuthorName: S. Sangha diff --git a/datasets/nasa-sentinel-1adpgrdhigh.yaml b/datasets/nasa-sentinel-1adpgrdhigh.yaml new file mode 100644 index 000000000..11c815e5b --- /dev/null +++ b/datasets/nasa-sentinel-1adpgrdhigh.yaml @@ -0,0 +1,43 @@ +Name: SENTINEL-1A_DUAL_POL_GRD_HIGH_RES +Description: |- + Sentinel-1A Dual-pol ground projected high and full resolution images + Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME +Documentation: https://webdocs.asf.alaska.edu/Sentinel-1/Sentinel-1-User-Guide.pdf +Contact: 'Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/' +ManagedBy: NASA +UpdateFrequency: From 2014-04-03 to Ongoing +Tags: + - aws-pds + - agriculture + - coastal + - earth observation + - earthquakes + - ecosystems + - ice + - land + - land cover + - land use + - metadata + - oceans + - radar + - sentinel-1 + - stac + - surface water + - synthetic aperture radar + - tiff + - urban + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'SENTINEL-1A_DUAL_POL_GRD_HIGH_RES.' + ARN: arn:aws:s3:::asf-ngap2w-p-s1-grd-7d1b4348 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://sentinel1.asf.alaska.edu/s3credentials +DataAtWork: + Tutorials: + - Title: Interferometric Synthetic Aperture Radar Tutorial + URL: https://github.com/live-eo/sentinel1-slc/blob/main/docs/tutorial_InSAR.md + AuthorName: LiveEO + AuthorURL: https://live-eo.com/ diff --git a/datasets/nasa-sentinel-1aslc.yaml b/datasets/nasa-sentinel-1aslc.yaml new file mode 100644 index 000000000..80fe7436a --- /dev/null +++ b/datasets/nasa-sentinel-1aslc.yaml @@ -0,0 +1,42 @@ +Name: SENTINEL-1A_SLC +Description: |- + Sentinel-1A slant-range product + Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME +Documentation: https://webdocs.asf.alaska.edu/Sentinel-1/Sentinel-1-User-Guide.pdf +Contact: 'Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/' +ManagedBy: NASA +UpdateFrequency: From 2014-04-03 to Ongoing +Tags: + - aws-pds + - coastal + - earthquakes + - ecosystems + - ice + - land + - land cover + - land use + - metadata + - oceans + - orbit + - radar + - sentinel-1 + - stac + - surface water + - synthetic aperture radar + - tiff + - urban + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'SENTINEL-1A_SLC.' + ARN: arn:aws:s3:::asf-ngap2w-p-s1-slc-7b420b89 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://sentinel1.asf.alaska.edu/s3credentials +DataAtWork: + Tutorials: + - Title: Interferometric Synthetic Aperture Radar Tutorial + URL: https://github.com/live-eo/sentinel1-slc/blob/main/docs/tutorial_InSAR.md + AuthorName: LiveEO + AuthorURL: https://live-eo.com/ diff --git a/datasets/nasa-sentinel-1bdpgrdhigh.yaml b/datasets/nasa-sentinel-1bdpgrdhigh.yaml new file mode 100644 index 000000000..e6b3bd03b --- /dev/null +++ b/datasets/nasa-sentinel-1bdpgrdhigh.yaml @@ -0,0 +1,42 @@ +Name: SENTINEL-1B_DUAL_POL_GRD_HIGH_RES +Description: |- + Sentinel-1B Dual-pol ground projected high and full resolution images + Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME +Documentation: https://webdocs.asf.alaska.edu/Sentinel-1/Sentinel-1-User-Guide.pdf +Contact: 'Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/' +ManagedBy: NASA +UpdateFrequency: From 2016-04-25 to 2021-12-24 +Tags: + - aws-pds + - agriculture + - coastal + - earthquakes + - ecosystems + - ice + - land + - land cover + - land use + - metadata + - oceans + - radar + - sentinel-1 + - stac + - surface water + - synthetic aperture radar + - tiff + - urban + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'SENTINEL-1B_DUAL_POL_GRD_HIGH_RES.' + ARN: arn:aws:s3:::asf-ngap2w-p-s1-grd-7d1b4348 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://sentinel1.asf.alaska.edu/s3credentials +DataAtWork: + Tutorials: + - Title: Interferometric Synthetic Aperture Radar Tutorial + URL: https://github.com/live-eo/sentinel1-slc/blob/main/docs/tutorial_InSAR.md + AuthorName: LiveEO + AuthorURL: https://live-eo.com/ diff --git a/datasets/nasa-sentinel-1bslc.yaml b/datasets/nasa-sentinel-1bslc.yaml new file mode 100644 index 000000000..b178c3ee0 --- /dev/null +++ b/datasets/nasa-sentinel-1bslc.yaml @@ -0,0 +1,43 @@ +Name: SENTINEL-1B_SLC +Description: |- + Sentinel-1B slant-range product + Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME +Documentation: https://webdocs.asf.alaska.edu/Sentinel-1/Sentinel-1-User-Guide.pdf +Contact: 'Email: uso@asf.alaska.edu. Home Page: https://www.asf.alaska.edu/' +ManagedBy: NASA +UpdateFrequency: From 2016-04-25 to 2021-12-24 +Tags: + - aws-pds + - agriculture + - coastal + - earthquakes + - ecosystems + - ice + - land + - land cover + - land use + - metadata + - oceans + - orbit + - radar + - sentinel-1 + - stac + - surface water + - synthetic aperture radar + - tiff + - urban + - water +License: '[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)' +Resources: + - Description: 'SENTINEL-1B_SLC.' + ARN: arn:aws:s3:::asf-ngap2w-p-s1-slc-7b420b89 + Region: us-west-2 + Type: S3 Bucket + RequesterPays: false + ControlledAccess: https://sentinel1.asf.alaska.edu/s3credentials +DataAtWork: + Tutorials: + - Title: Interferometric Synthetic Aperture Radar Tutorial + URL: https://github.com/live-eo/sentinel1-slc/blob/main/docs/tutorial_InSAR.md + AuthorName: LiveEO + AuthorURL: https://live-eo.com/ diff --git a/datasets/nifs-lhd.yaml b/datasets/nifs-lhd.yaml index 2168f4eed..6c1df1935 100644 --- a/datasets/nifs-lhd.yaml +++ b/datasets/nifs-lhd.yaml @@ -4,6 +4,10 @@ Documentation: https://www-lhd.nifs.ac.jp/pub/Repository_en.html Contact: For any questions regarding data delivery or any general questions regarding the LHD Experiment data repository, please send email to the Data Acquisition and Analysis group at Comp_DAE@nifs.ac.jp. ManagedBy: "[NIFS](https://www.nifs.ac.jp/)" UpdateFrequency: Archived data files are updated nightly when new or revised data are generated in LHD experiment. +Collabs: + ASDI: + Tags: + - energy Tags: - analytics - anomaly detection diff --git a/datasets/noaa-cris-hist.yaml b/datasets/noaa-cris-hist.yaml new file mode 100644 index 000000000..c7ba0353d --- /dev/null +++ b/datasets/noaa-cris-hist.yaml @@ -0,0 +1,40 @@ +Name: NOAA nClimGrid and Livneh Gridded Historical Climate Observation Thresholds +Description: | + Livneh and nClimGrid are gridded observed historical climatology data that were used in the LOCA2 and STAR-ESDM downscaling process of global climate models as part of the 5th National Climate Assessment. The original Livneh and nClimGrid daily temperature and precipitation observations have been converted to a series of decision-relevant thresholds as part of the [(U.S. Climate Resilience Information System (CRIS))](https://cris.climate.gov/pages/about). These thresholds, such as days with extreme heat or precipitation, have been calculated to match the future projections from LOCA2 and STAR, also available in CRIS. +Documentation: | + For information, please consult https://cris.climate.gov/pages/about-the-data. +Contact: | + For any questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. + We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov +ManagedBy: "[NOAA](http://www.noaa.gov/)" +UpdateFrequency: | + None +Collabs: + ASDI: + Tags: + - climate +Tags: + - aws-pds + - agriculture + - climate + - environmental + - meteorological + - weather +License: | + NOAA data disseminated through NODD are open to the public and can be used as desired.

NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. +Resources: + - Description: nClimGrid and Livneh Gridded Historical Observation Thresholds + ARN: arn:aws:s3:::noaa-cris-hist-pds + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://noaa-cris-hist-pds.s3.amazonaws.com/index.html)' + - Description: New data notifications for nClimGride and Livneh Gridded Historical Observation Thresholds, only Lambda and SQS protocols allowed + ARN: arn:aws:sns:us-west-2:123901341784:NewCRIS-HISTObject + Region: us-east-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: U.S. CRIS Resources + URL: https://cris.climate.gov/pages/developers + AuthorName: U.S. CRIS diff --git a/datasets/noaa-gfs-bdp-pds.yaml b/datasets/noaa-gfs-bdp-pds.yaml index 002d759aa..142ddbe8a 100644 --- a/datasets/noaa-gfs-bdp-pds.yaml +++ b/datasets/noaa-gfs-bdp-pds.yaml @@ -74,6 +74,9 @@ Resources: Type: SNS Topic DataAtWork: Tutorials: + - Title: "NOAA Global Forecast System (GFS) quickstart notebook on AWS" + URL: https://github.com/aws-samples/aws-opendata-samples/blob/main/notebooks/noaa-gfs/noaa_gfs_quickstart.ipynb + AuthorName: Benoit de Chateauvieux Tools & Applications: Publications: - Title: GFS Warm Restart Files Additional Information diff --git a/datasets/noaa-historicalcharts.yaml b/datasets/noaa-historicalcharts.yaml index 5a1109924..f504f51db 100644 --- a/datasets/noaa-historicalcharts.yaml +++ b/datasets/noaa-historicalcharts.yaml @@ -2,10 +2,14 @@ Name: NOAA Historical Maps and Charts Description: Historical Charts are not for Navigation. The collection primarily consists of historic charts and maps produced by NOAA's Coast Survey and its predecessors, especially the U.S. Coast and Geodetic Survey and the U.S. Lake Survey (previously under the Department of War). The collection also includes bathymetric maps, land sketches, Civil War battle maps, aeronautical charting from the 1930s to the 1950s, and other drawings and photographs. Documentation: https://historicalcharts.noaa.gov/about.php Contact: | - For any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Big Data Program, email noaa.bdp@noaa.gov.

+ For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov.

For general questions or feedback about the data, please submit inquiries through the NOAA Office of Coast Survey (OCS) ASSIST Tool at https://www.nauticalcharts.noaa.gov/customer-service/assist/. ManagedBy: "[NOAA](http://www.noaa.gov/)" UpdateFrequency: Periodic manual updates when historic charts are added to the collection. +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - history @@ -21,3 +25,4 @@ Resources: Type: S3 Bucket Explore: - '[Browse Bucket](https://noaa-nos-historicalcharts-pds.s3.amazonaws.com/index.html)' + diff --git a/datasets/noaa-mrms-pds.yaml b/datasets/noaa-mrms-pds.yaml index 64bc5c953..adb4a58ab 100644 --- a/datasets/noaa-mrms-pds.yaml +++ b/datasets/noaa-mrms-pds.yaml @@ -48,6 +48,10 @@ Resources: Region: us-east-1 Type: SNS Topic DataAtWork: + Tools & Applications: + - Title: Collection of Jupyter Notebooks using Python for working with MRMS Data + URL: https://projectpythia.org/mrms-cookbook/ + AuthorName: Project Pythia Community Publications: - Title: "Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities" URL: https://journals.ametsoc.org/view/journals/bams/97/4/bams-d-14-00174.1.xml diff --git a/datasets/noaa-nbm-parallel.yaml b/datasets/noaa-nbm-parallel.yaml new file mode 100644 index 000000000..b9410b4a5 --- /dev/null +++ b/datasets/noaa-nbm-parallel.yaml @@ -0,0 +1,36 @@ +Name: NOAA National Blend of Models (NBM) Parallel +Description: | + The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on a blend of both NWS and non-NWS numerical weather prediction model data and post-processed model guidance. The goal of the NBM is to create a highly accurate, skillful and consistent starting point for the gridded forecast. This dataset contains data from the current parallel version of the NBM which is a test version, featuring many changes, that is a candidate to be implemented into operations following a careful vetting process. +Documentation: | + https://vlab.noaa.gov/web/mdl/nbm +Contact: | + For any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. + We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov +ManagedBy: "[NOAA](http://www.noaa.gov/)" +UpdateFrequency: | + Once per hour. +Collabs: + ASDI: + Tags: + - weather +Tags: + - aws-pds + - agriculture + - climate + - disaster response + - environmental + - meteorological + - weather +License: | + NOAA data disseminated through NODD are open to the public and can be used as desired.

NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. +Resources: + - Description: National Blend of Models (NBM) Parallel + ARN: arn:aws:s3:::noaa-nbm-para-pds + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://noaa-nbm-para-pds.s3.amazonaws.com/index.html)' + - Description: New data notifications for NBM Parallel, only Lambda and SQS protocols allowed + ARN: arn:aws:sns:us-east-1:123901341784:NewNBMParaObject + Region: us-east-1 + Type: SNS Topic diff --git a/datasets/noaa-ncn.yaml b/datasets/noaa-ncn.yaml index 47c68d147..c46db048d 100644 --- a/datasets/noaa-ncn.yaml +++ b/datasets/noaa-ncn.yaml @@ -6,7 +6,6 @@ Description: | - [NOAA-NCN on AWS](https://noaa-cors-pds.s3.amazonaws.com/index.html) - [NGS server: https://geodesy.noaa.gov/corsdata/](https://geodesy.noaa.gov/corsdata/) - [NGS's customized data request service (UFCORS)](https://geodesy.noaa.gov/UFCORS/) - - [NGS Anonymous ftp://geodesy.noaa.gov/cors/ - This service is going away on August 02, 2021!](ftp://geodesy.noaa.gov/cors/) - #### NCN Data and Products - **RINEX**: The GPS/GNSS data collected at NCN stations are made available to the public by NGS in Receiver INdependent EXchange (RINEX) format. Most data are available within 1 hour (60 minutes) from when they were recorded at the remote site, and a few sites have a delay of 24 hours (1440 minutes).
RINEX data can be found at: *rinex/`YYYY`/`DDD`/`ssss`/* - **Station logs**: diff --git a/datasets/noaa-nesdis-tcprimed-pds.yaml b/datasets/noaa-nesdis-tcprimed-pds.yaml index caf22df47..0fb9df2ac 100644 --- a/datasets/noaa-nesdis-tcprimed-pds.yaml +++ b/datasets/noaa-nesdis-tcprimed-pds.yaml @@ -4,6 +4,10 @@ Documentation: https://rammb-data.cira.colostate.edu/tcprimed/TCPRIMED_v01r01_do Contact: CIRA_tcprimed [at] colostate [dot] edu ManagedBy: "[CIRA](https://www.cira.colostate.edu/)" UpdateFrequency: Annually for the final version, several months after the conclusion of the Northern Hemisphere tropical cyclone season and daily for the preliminary version, several days after the dissipation of a tropical cyclone. +Collabs: + ASDI: + Tags: + - climate Tags: - atmosphere - aws-pds diff --git a/datasets/noaa-nexrad.yaml b/datasets/noaa-nexrad.yaml index 990578f4b..7e5373c5c 100644 --- a/datasets/noaa-nexrad.yaml +++ b/datasets/noaa-nexrad.yaml @@ -1,5 +1,12 @@ Name: NEXRAD on AWS -Description: Real-time and archival data from the Next Generation Weather Radar (NEXRAD) network. +Description: | + Real-time and archival data from the Next Generation Weather Radar (NEXRAD) network. +
+

Update

+ The NEXRAD Level II archive data is moving to a new bucket: unidata-nexrad-level2 + and SNS topic: arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2Archive. The old + bucket and SNS topic are now deprecated and will no longer be available starting September 1, 2025. +

Documentation: https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad Contact: support-level2@unidata.ucar.edu ManagedBy: "[Unidata](https://www.unidata.ucar.edu/)" @@ -18,11 +25,11 @@ Tags: License: NOAA data disseminated through NODD are open to the public and can be used as desired.

NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. Resources: - Description: NEXRAD Level II archive data - ARN: arn:aws:s3:::noaa-nexrad-level2 + ARN: arn:aws:s3:::unidata-nexrad-level2 Region: us-east-1 Type: S3 Bucket Explore: - - '[Browse Bucket](https://noaa-nexrad-level2.s3.amazonaws.com/index.html)' + - '[Browse Bucket](https://unidata-nexrad-level2.s3.amazonaws.com/index.html)' - Description: NEXRAD Level II real-time data ARN: arn:aws:s3:::unidata-nexrad-level2-chunks Region: us-east-1 @@ -37,8 +44,8 @@ Resources: ARN: arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2ObjectFilterable Region: us-east-1 Type: SNS Topic - - Description: Notifications for the Level II archival bucket - ARN: arn:aws:sns:us-east-1:811054952067:NewNEXRADLevel2Archive + - Description: Notifications for the new Level II archival bucket + ARN: arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2Archive Region: us-east-1 Type: SNS Topic - Description: Notifications for the Level III bucket diff --git a/datasets/noaa-nos-cora.yaml b/datasets/noaa-nos-cora.yaml index 766572d57..436e105d5 100644 --- a/datasets/noaa-nos-cora.yaml +++ b/datasets/noaa-nos-cora.yaml @@ -1,32 +1,61 @@ -Name: NOAA's Coastal Ocean Reanalysis (CORA) Dataset +Name: "NOAA's Coastal Ocean Reanalysis (CORA) Dataset: 1979-2022" + Description: | - NOAA's Coastal Ocean Reanalysis (CORA) for the Gulf of Mexico and East Coast (GEC) is produced using verified hourly water levels from the Center of Operational Oceanographic Products & Services (CO-OPS), through hydrodynamic modeling from Advanced Circulation "[ADCIRC](https://adcirc.org/)" and Simulating WAves Nearshore "[SWAN](https://swanmodel.sourceforge.io/)" models. Data are assimilated, processed, corrected, and processed again before quality assurance and skill assessment with additional verified tide station-based observations. -
-
- Details for CORA Dataset -
-
- **Timeseries** - 1979 to 2022 -
- **Size** - Approx. 20.5TB -
- **Domain** - Lat 5.8 to 45.8 ; Long -98.0 to -53.8 -
- **Nodes** - 1813443 centroids, 3564104 elements -
- **Grid cells** - Currently apporximately 505 -
- **Spatial Resolution** - 500m, 1983 Contiguous USA Albers projection (EPSG:5070) -
-Documentation: https://tidesandcurrents.noaa.gov/ + NOAA's [Coastal Ocean Reanalysis (CORA)](https://tidesandcurrents.noaa.gov/cora.html) for the Gulf, East Coast/Atlantic, and Caribbean (GEC) is produced using verified hourly water levels from the National Ocean Service’s [Center of Operational Oceanographic Products & Services](https://tidesandcurrents.noaa.gov/) (CO-OPS). [ADvanced CIRCulation Model (ADCIRC)](https://www.erdc.usace.army.mil/Media/Fact-Sheets/Fact-Sheet-Article-View/Article/476698/advanced-circulation-model/) and [Simulating WAves Nearshore (SWAN)](https://www.tudelft.nl/en/ceg/about-faculty/departments/hydraulic-engineering/sections/environmental-fluid-mechanics/research/swan) models are coupled to model coastal water levels and nearshore waves. Hourly water level observations are used for data assimilation and validation to improve the accuracy of modeled water levels and wave datasets. +

+ Additional Details:
+ Metadata associated with model domain and time span: + - Timeseries - 1979 to 2022 + - Size - Approx. 44.6 TB + - Domain - Lat 5.8 to 45.8 ; Long -98.0 to -53.8 + - Nodes - [CORA Metadata Library](https://www.fisheries.noaa.gov/inport/item/75048) + - Grid cells - [CORA Metadata Library](https://www.fisheries.noaa.gov/inport/item/75048) + - Spatial Resolution: + - Centroids: 300-400 meters + - Gridded: 500 meters + - Projection: 1983 Contiguous USA Albers projection (EPSG:5070) +

+ + Datasets:
+ Water level and wave datasets resulting from the computation, assimilation, validation, and optimization reanalysis datasets. All products are available in NetCDF (.nc) format: + - fort.63.nc - Water level elevation + - fort.73.nc - Atmospheric pressure at sea level + - fort.74.nc - Wind Velocity - 10 m elevation + - maxele.63.nc - Maximum water elevation + - swan_DIR.63.nc - Spectral mean wave direction + - swan_TMM10.63.nc - Spectral mean wave period + - swan_TPS.63.nc - Spectral peak wave period + - swan_HS.63.nc - Spectral zeroth moment wave height + - swan_HS_max.63.nc - Maximum spectral zeroth moment wave height +

+ + Derived Products:
+ Datasets resulting from the computation, modeling, or other processing using existing/collected data. All products are available in NetCDF (.nc) format: + - CORA-V1.1-fort.63: Hourly water levels + - CORA-V1.1-swan_DIR.63: Hourly mean wave direction + - CORA-V1.1-swan_TPS.63: Hourly peak wave periods + - CORA-V1.1-swan_HS.63: Hourly significant wave heights + - CORA-V1.1-Grid: Hourly water levels interpolated from model nodes to uniform 500-meter resolution grid +

+ +Documentation: | + [NOAA Technical Report NOS CO-OPS 108: NOAA’s Coastal Ocean Reanalysis: Gulf of Mexico, Atlantic, and Caribbean (January 2025)](https://doi.org/10.25923/5ypp-4e84) + +UpdateFrequency: Product dependent. At minimum, annually. + +License: | + NOAA data disseminated through NODD are open to the public and can be used as desired. + + NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. + +ManagedBy: | + [NOAA’s National Ocean Service, The Center for Operational Oceanographic Products and Services (CO-OPS)](https://tidesandcurrents.noaa.gov/about_us.html) + Contact: | For questions regarding data content or quality, email CO-OPS.UserServices@noaa.gov -
This data is made available to the public through the NOAA Open Data Dissemination (NODD) Program. For questions regarding this program, email nodd@noaa.gov. -
- We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA NODD team at NODD@NOAA.GOV. -ManagedBy: "[NOAA’s National Ocean Service, The Center for Operational Oceanographic Products and Services (CO-OPS)](https://tidesandcurrents.noaa.gov/about_us.html)" -UpdateFrequency: Monthly, quarterly, and annually, depending on the dataset. + We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA NODD team at nodd@noaa.gov. + Collabs: ASDI: Tags: @@ -41,21 +70,37 @@ Tags: - agriculture - transportation - oceans -License: NOAA data disseminated through NODD are open to the public and can be used as desired.

NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. + Resources: - - Description: NOAA’s Coastal Ocean Reanalysis (CORA) Dataset NetCDF + - Description: "NOAA’s Coastal Ocean Reanalysis (CORA) Dataset NetCDF" ARN: arn:aws:s3:::noaa-nos-cora-pds Region: us-east-1 Type: S3 Bucket Explore: - '[Browse Bucket](https://noaa-nos-cora-pds.s3.amazonaws.com/index.html)' - - Description: NOAA’s Coastal Ocean Reanalysis (CORA) Dataset Notifications + - Description: "NOAA’s Coastal Ocean Reanalysis (CORA) Dataset Notifications" ARN: arn:aws:sns:us-east-1:709902155096:NewNOSCORAObject Region: us-east-1 Type: SNS Topic + DataAtWork: Tutorials: - - Title: Notebooks for working with CORA Data - URL: https://github.com/NOAA-CO-OPS/CORA-Coastal-Ocean-ReAnalysis-CORA - AuthorName: John Ratcliff - AuthorURL: https://www.linkedin.com/in/johndratcliff/ + - Title: "Using Python to Access Coastal Ocean Reanalysis (CORA) Data" + URL: https://github.com/NOAA-CO-OPS/CORA-Coastal-Ocean-Reanalysis-CORA + AuthorName: "NOAA's Center for Operational Oceanographic Products and Services" + AuthorURL: https://tidesandcurrents.noaa.gov/ + + Tools & Applications: + - Title: Coastal Ocean Reanalysis Use cases + URL: https://tidesandcurrents.noaa.gov/cora.html#usecase + AuthorName: "NOAA's Center for Operational Oceanographic Products and Services" + AuthorURL: https://tidesandcurrents.noaa.gov/ + + Publications: + - Title: "NOAA Technical Report NOS CO-OPS 108: NOAA’s Coastal Ocean Reanalysis: Gulf of Mexico, Atlantic, and Caribbean (January 2025)" + URL: https://doi.org/10.25923/5ypp-4e84 + AuthorName: Keeney, Analise; Dusek, Gregory; Callahan, John; Ratcliff, John; Jima, Tigist; Brooks, William; Marcy, Doug; Blanton, Brian; Tilson, Jeffrey; Asher, Taylor G.; Leuttich, Richard A.; Widlansky, Matthew J.; Rose, Linta; Morse, Cheryl; Haddad, Jana; & Waring, Blake + + - Title: "Assessment of water levels from 43 years of NOAA’s Coastal Ocean Reanalysis (CORA) for the Gulf of Mexico and East Coasts" + URL: https://doi.org/10.3389/fmars.2024.1381228 + AuthorName: Rose, Linta; Widlansky, Matthew J.; Feng, Xue; Thompson, Thompson; Asher, Taylor G.; Dusek, Gregory; Blanton, Blanton; Luettich, Richard A. Jr.; Callahan, John; Brooks, William; Keeney, Analise; Haddad, Jana; Sweet, William; Genz, Ayesha; Hovenga, Paige; Marra, John & Tilson, Jeffrey diff --git a/datasets/noaa-nws-hafs.yaml b/datasets/noaa-nws-hafs.yaml index 3ed6b9f8c..d2e9b2f56 100644 --- a/datasets/noaa-nws-hafs.yaml +++ b/datasets/noaa-nws-hafs.yaml @@ -15,7 +15,7 @@ Contact: | For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.
We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov ManagedBy: "[NOAA](http://www.noaa.gov/)" -UpdateFrequency: Event Driven +UpdateFrequency: Event Driven.

As of August 2025, a few forecast cycles for Hurricane Fiona (07L) and Hurricane Nicole (17L) from the 2022 Atlantic hurricane season have been made available. These files can be found under the subdirectories of hfsa_retro and hfsb_retro. Additional forecast files from the 2022 hurricane season can be made available upon user request. Collabs: ASDI: Tags: diff --git a/datasets/noaa-nws-naqfc-pds.yaml b/datasets/noaa-nws-naqfc-pds.yaml index 8a8dc9403..dd22a24d2 100644 --- a/datasets/noaa-nws-naqfc-pds.yaml +++ b/datasets/noaa-nws-naqfc-pds.yaml @@ -1,23 +1,23 @@ Name: NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance Description: | - The National Air Quality Forecasting Capability (NAQFC) dataset contains model-generated Air-Quality (AQ) forecast guidance from three different prediction systems. The first system is a coupled weather and atmospheric chemistry numerical forecast model, known as the Air Quality Model (AQM). It is used to produce forecast guidance for ozone (O3) and particulate matter with diameter equal to or less than 2.5 micrometers (PM2.5) using meteorological forecasts based on NCEP’s operational weather forecast models such as North American Mesoscale Models (NAM) and Global Forecast System (GFS), and atmospheric chemistry based on the EPA’s Community Multiscale Air Quality (CMAQ) model. In addition, the modeling system incorporates information related to chemical emissions, including anthropogenic emissions provided by the EPA and fire emissions from NOAA/NESDIS. The NCEP NAQFC AQM output fields in this archive include 72-hr forecast products of model raw and bias-correction predictions, extending back to 1 January 2020. All of the output was generated by the contemporaneous operational AQM, beginning with AQMv5 in 2020, with upgrades to AQMv6 on 20 July 2021, and AQMv7 on 14 May 2024. The history of AQM upgrades is documented [here](https://www.emc.ncep.noaa.gov/mmb/aq/AQChangelog.html) + The National Air Quality Forecasting Capability (NAQFC) dataset contains model-generated air quality (AQ) forecast guidance from three different prediction systems. The first system is a coupled weather and atmospheric chemistry numerical forecast model, known as the Air Quality Model (AQM). It is used to produce forecast guidance for ozone (O3) and particulate matter that is less than or equal to 2.5 micrometers in diameter (PM2.5). Prior to May 14, 2024, AQM predictions were derived using the EPA’s Community Multiscale Air Quality (CMAQ) model, driven by meteorological fields from NCEP’s operational weather forecast models, specifically the North American Mesoscale Model (NAM; prior to 20 July 2021) and the Global Forecast System (GFS; beginning 20 July 2021). Since May 14, 2024, AQM guidance has been produced by a unique application within the community-based Unified Forecast System (UFS). The core model components in this application are derived directly from the fully online-coupled UFS-based weather and CMAQ-based chemistry models. In addition, it incorporates information related to chemical and particle source emissions as it integrates forward in time, including anthropogenic chemical emissions provided by the EPA, fire emissions from NOAA/NESDIS, and airborne particles generated by human activities and those predicted to be generated by wind-driven erosion and biosphere at ground level. The NCEP NAQFC AQM output fields in this archive include model raw and bias-corrected predictions dating back to 1 January 2020, all generated by the contemporaneous operational AQM, beginning with AQMv5 in 2020, transitioning to AQMv6 on 20 July 2021, and to AQMv7 on 14 May 2024. The length of each forecast was 48 hours prior to the implementation of AQMv6, and has been 72 hours ever since. The history of AQM upgrades is documented [here](https://www.emc.ncep.noaa.gov/mmb/aq/AQChangelog.html)

- The second prediction is known as the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT). It is a widely used atmospheric transport and dispersion model containing an internal dust-generation module. It provides forecast guidance for atmospheric dust concentration and, prior to 28 June 2022, it also provided the NAQFC forecast guidance for smoke. Since that date, the third prediction system, a regional numerical weather prediction (NWP) model known as the Rapid Refresh (RAP) model, has subsumed HYSPLIT for operational smoke guidance, simulating the emission, transport, and deposition of smoke particles that originate from biomass burning (fires) and anthropogenic sources. + The second prediction is known as the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT). It is a widely used atmospheric transport and dispersion model containing an internal dust-generation module. It provides forecast guidance for atmospheric dust concentration and, prior to 28 June 2022, it also provided the NAQFC forecast guidance for smoke. Starting on that date, the third prediction system, a regional numerical weather prediction (NWP) model known as the Rapid Refresh (RAP) model, subsumed HYSPLIT for operational smoke guidance, simulating the emission, transport, and deposition of smoke particles that originate from biomass burning (fires) and anthropogenic sources.

- The output from each of these modeling systems is generated over three separate domains, one covering CONUS, one Alaska, and the other Hawaii. Currently, for this archive, the ozone, (PM2.5), and smoke output is available over all three domains, while dust products are available only over the CONUS domain. The predicted concentrations of all species in the lowest model layer (i.e., the layer in contact with the surface) are available, as are vertically integrated values of smoke and dust. The data is gridded horizontally within each domain, with a grid spacing of approximately 5 km over CONUS, 6 km over Alaska, and 2.5 km over Hawaii. Ozone concentrations are provided in parts per billion (PPB), while the concentrations of all other species are quantified in units of micrograms per cubic meter (ug/m3), except for the column-integrated smoke values which are expressed in units of mg/m2. + The output from each of these modeling systems is generated over three separate domains, one covering CONUS, another over Alaska, and the other over Hawaii. Currently, for this archive, the O3, PM2.5, and smoke output is available over all three domains, while dust products are available only over the CONUS domain. The predicted concentrations of all species in the lowest model layer (i.e., the layer in contact with the surface) are available, as are vertically integrated values of smoke and dust. The data is gridded horizontally within each domain, with a grid spacing of approximately 5 km over CONUS, 6 km over Alaska, and 2.5 km over Hawaii. O3 concentrations are provided in parts per billion (PPB), while the concentrations of all other species are quantified in units of micrograms per cubic meter (ug/m3), except for the column-integrated smoke values which are expressed in units of milligrams per square meter (mg/m2).

- Temporally, O3 and PM2.5 are available as maximum and/or averaged values over various time periods. Specifically, O3 is available in both 1-hour and 8-hour (backward calculated) averages, as well as preceding 1-hour and 8-hour maximum values. Similarly, PM2.5 is available in 1-hour and 24-hour average values and 24-hour maximum values. In addition, all O3 and PM2.5 fields are available with bias-corrected magnitudes, based on derived model biases relative to observations. + Temporally, O3 and PM2.5 are available as maximum and/or averaged values over various time periods, selected in part for consistency with the EPA’s National Ambient Air Quality Standards. Specifically, O3 is available in both 1-hour and 8-hour (backward calculated) averages, as well as preceding 1-hour and 8-hour maximum values. Similarly, PM2.5 is available in 1-hour and 24-hour average values and 24-hour maximum values. In addition, all O3 and PM2.5 fields are available with bias-corrected magnitudes, based on derived historical model biases relative to observations.

- The AQM produces hourly forecast guidance for O3 and PM2.5 out to 72 hours twice per day, starting at 0600 and 1200 UTC. Smoke guidance is available out to 51 hours from once-per-day RAP forecasts initialized at 0300 UTC, while dust guidance from HYSPLIT is available out to 48 hours from initialization times of 0600 and 1200 UTC. + The AQM produces hourly forecast guidance for O3 and PM2.5 up to 72 hours twice per day. Smoke guidance is available up to 51 hours from once-per-day RAP forecasts, while dust guidance from HYSPLIT is available up to 48 hours. Documentation: https://vlab.noaa.gov/web/osti-modeling/air-quality Contact: For questions regarding data content or quality, visit the NCEP AQM Products website. For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.
We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov ManagedBy: "[NOAA](http://www.noaa.gov/)" -UpdateFrequency: 2 times per day, 0600 and 1200 UTC for O3, PM2.5, and dust; once per day, 0300 UTC for smoke +UpdateFrequency: Two times per day, 0600 and 1200 UTC for O3, PM2.5, and dust; once per day, 0300 UTC for smoke Collabs: ASDI: Tags: @@ -38,6 +38,10 @@ Resources: Type: S3 Bucket Explore: - '[Browse Bucket](https://noaa-nws-naqfc-pds.s3.amazonaws.com/index.html)' + - Description: New data notifications for NAQFC, only Lambda and SQS protocols allowed + ARN: arn:aws:sns:us-east-1:709902155096:NewNWSAirQualityObject + Region: us-east-1 + Type: SNS Topic DataAtWork: Tutorials: Tools & Applications: diff --git a/datasets/noaa-nws-wam-ipe.yaml b/datasets/noaa-nws-wam-ipe.yaml index 1f0215043..be2a3401f 100644 --- a/datasets/noaa-nws-wam-ipe.yaml +++ b/datasets/noaa-nws-wam-ipe.yaml @@ -23,6 +23,10 @@ Contact: |
We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov ManagedBy: "[NOAA](http://www.noaa.gov/)" UpdateFrequency: The update frequencies of the WAM-IPE dataset range from 10 minutes to 6 hours depending on the CONOPS. +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - climate diff --git a/datasets/noaa-ocs-hydrodata.yaml b/datasets/noaa-ocs-hydrodata.yaml index 2093bd341..a7cac737d 100644 --- a/datasets/noaa-ocs-hydrodata.yaml +++ b/datasets/noaa-ocs-hydrodata.yaml @@ -28,6 +28,8 @@ Resources: Type: S3 Bucket Explore: - '[Browse Bucket](https://noaa-ocs-hydrodata-pds.s3.amazonaws.com/index.html)' + - '[STAC Catalog](https://noaa-ocs-hydrodata-pds.s3.amazonaws.com/catalog.json)' + - '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/noaa-ocs-hydrodata-pds.s3.amazonaws.com/catalog.json?.language=en)' - Description: NOAA Office of Coast Survey Hydrographic Survey Data New Object Notification ARN: arn:aws:sns:us-east-1:709902155096:NewOCSHYDROObject Region: us-east-1 diff --git a/datasets/noaa-s104.yaml b/datasets/noaa-s104.yaml new file mode 100644 index 000000000..4f226d2b1 --- /dev/null +++ b/datasets/noaa-s104.yaml @@ -0,0 +1,48 @@ +Name: "NOAA S-104 Water Level Data" +Description: | + S-104 is a data and metadata encoding specification that is part of the [S-100 Universal Hydrographic Data Model](https://iho.int/en/s-100-universal-hydrographic-data-model), an international standard for hydrographic data. This collection of data contains water level forecast guidance from [NOAA's Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global)](https://polar.ncep.noaa.gov/estofs/), an operational hydrodynamic nowcast and forecast modeling system for global water level conditions. These datasets are encoded as HDF-5 files conforming to the S-104 specification, and are geospatially subset into individual tiles conforming to the NOAA/OCS Nautical Product Tiling Scheme, with filenames indicating the corresponding NOAA Electronic Navigational Chart (ENC) Cell Identifier. A set of prototype S-104 tiles has been created for the Charleston, SC area for a select model run cycle. Each individual S-104 (HDF-5) file contains all forecast projections from a single model run for that geographic area. A single S-104 file will contain multiple gridded arrays each containing a forecast valid at a distinct time in the future, out to the forecast horizon of STOFS-2D-Global, which is 180 hours or 7.5 days. The water level forecast guidance includes the combined effects of storm surge (sub-tidal) and tides (astronomical tide predictions). +Documentation: | + https://noaa-s104-pds.s3.amazonaws.com/README.html +UpdateFrequency: | + Static +License: | + NOAA data disseminated through NODD are open to the public and can be used as desired. + NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. +ManagedBy: | + "[NOAA](http://www.noaa.gov/)" +Contact: | + For any data delivery issues, please contact the NOAA Open Data Dissemination Team at: nodd@noaa.gov. For general questions or feedback about the data, please submit inquiries through the NOAA Office of Coast Survey (OCS) ASSIST Tool at https://www.nauticalcharts.noaa.gov/customer-service/assist/. + We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA NODD team at nodd@noaa.gov. +Collabs: + ASDI: + Tags: + - weather +Tags: + - aws-pds + - marine navigation + - hydrography + - oceans + - coastal + - water + +Resources: + - Description: "NOAA S-104 Water Level for Surface Navigation Datasets" + ARN: arn:aws:s3:::noaa-s104-pds + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://noaa-s104-pds.s3.amazonaws.com/index.html)' + - Description: "NOAA S-104 Water Level for Surface Navigation Datasets" + ARN: arn:aws:sns:us-east-1:123901341784:NewS104Object + Region: us-east-1 + Type: SNS Topic + +DataAtWork: + Tutorials: + - Title: "NOAA Precision Marine Navigation Program: Developing Next-Gen Data Svcs for the Maritime Community" + URL: https://www.youtube.com/watch?v=laC0Du6-x3k + AuthorName: NOAA + - Title: "NOAA nowCOAST" + URL: https://nowcoast.noaa.gov/ + AuthorName: NOAA + diff --git a/datasets/noaa-space-weather.yaml b/datasets/noaa-space-weather.yaml index c44ec2aca..ebdcf087c 100644 --- a/datasets/noaa-space-weather.yaml +++ b/datasets/noaa-space-weather.yaml @@ -1,31 +1,35 @@ -Name: NOAA Space Weather Forecast and Observation Data -Description: > - Space weather forecast and observation data is collected and disseminated by NOAA’s Space Weather Prediction Center (SWPC) in Boulder, CO. SWPC produces forecasts for multiple space weather phenomenon types and the resulting impacts to Earth and human activities. A variety of products are available that provide these forecast expectations, and their respective measurements, in formats that range from detailed technical forecast discussions to NOAA Scale values to simple bulletins that give information in laymen's terms. - Forecasting is the prediction of future events, based on analysis and modeling of the past and present conditions of the environment you are interested in. In Space Weather, persistence and recurrence of active regions on the sun over the 27-day solar rotational period play an important role in accurately forecasting the space environment. -Documentation: https://www.swpc.noaa.gov/products-and-data -Contact: | - For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. -
We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov -ManagedBy: "[NOAA](http://www.noaa.gov/)" -UpdateFrequency: The update frequencies of the space weather dataset range from one minute observations to daily and monthly updates of more slowly-varying indices -Tags: - - aws-pds - - climate - - meteorological - - solar - - weather -License: NOAA data disseminated through NODD are open to the public and can be used as desired. -
-
- NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. -Resources: - - Description: NOAA Space Weather Prediction Center Forecasts - ARN: arn:aws:s3:::noaa-swpc-pds - Region: us-east-1 - Type: S3 Bucket - Explore: - - '[Browse Bucket](https://noaa-swpc-pds.s3.amazonaws.com/index.html)' - - Description: New data notifications for NOAA Space Weather Prediction Center Forecasts, only Lambda and SQS protocols allowed - ARN: arn:aws:sns:us-east-1:123901341784:NewSWPCObject - Region: us-east-1 - Type: SNS Topic +Name: NOAA Space Weather Forecast and Observation Data +Description: > + Space weather forecast and observation data is collected and disseminated by NOAA’s Space Weather Prediction Center (SWPC) in Boulder, CO. SWPC produces forecasts for multiple space weather phenomenon types and the resulting impacts to Earth and human activities. A variety of products are available that provide these forecast expectations, and their respective measurements, in formats that range from detailed technical forecast discussions to NOAA Scale values to simple bulletins that give information in laymen's terms. + Forecasting is the prediction of future events, based on analysis and modeling of the past and present conditions of the environment you are interested in. In Space Weather, persistence and recurrence of active regions on the sun over the 27-day solar rotational period play an important role in accurately forecasting the space environment. +Documentation: https://www.swpc.noaa.gov/products-and-data +Contact: | + For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. +
We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov +ManagedBy: "[NOAA](http://www.noaa.gov/)" +UpdateFrequency: The update frequencies of the space weather dataset range from one minute observations to daily and monthly updates of more slowly-varying indices +Collabs: + ASDI: + Tags: + - climate +Tags: + - aws-pds + - climate + - meteorological + - solar + - weather +License: NOAA data disseminated through NODD are open to the public and can be used as desired. +
+
+ NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. +Resources: + - Description: NOAA Space Weather Prediction Center Forecasts + ARN: arn:aws:s3:::noaa-swpc-pds + Region: us-east-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://noaa-swpc-pds.s3.amazonaws.com/index.html)' + - Description: New data notifications for NOAA Space Weather Prediction Center Forecasts, only Lambda and SQS protocols allowed + ARN: arn:aws:sns:us-east-1:123901341784:NewSWPCObject + Region: us-east-1 + Type: SNS Topic diff --git a/datasets/noaa-wod.yaml b/datasets/noaa-wod.yaml index 650e9b6cb..af8013170 100644 --- a/datasets/noaa-wod.yaml +++ b/datasets/noaa-wod.yaml @@ -3,8 +3,8 @@ Description: > The World Ocean Database (WOD) is the largest uniformly formatted, quality-controlled, publicly available historical subsurface ocean profile database. From Captain Cook's second voyage in 1772 to today's automated Argo floats, global aggregation of ocean variable information including temperature, salinity, oxygen, nutrients, and others vs. depth allow for study and understanding of the changing physical, chemical, and to some extent biological state of the World's Oceans. Browse the bucket via the AWS S3 explorer: https://noaa-wod-pds.s3.amazonaws.com/index.html Documentation: https://www.nodc.noaa.gov/OC5/WOD/pr_wod.html Contact: | - For any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Big Data Program, email noaa.bdp@noaa.gov. -
We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NOAA BDP team here: noaa.bdp@noaa.gov + For any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. + We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov ManagedBy: "[NOAA](http://www.noaa.gov/)" UpdateFrequency: Data is update on a quarterly basis Collabs: @@ -34,3 +34,4 @@ DataAtWork: - Title: The World Ocean Database User's Manual URL: https://data.nodc.noaa.gov/woa/WOD/DOC/wodreadme.pdf AuthorName: Hernan E. Garcia, Tim P. Boyer, Ricardo A. Locarnini, Olga K. Baranova, Melissa M. Zweng + diff --git a/datasets/nrel-pds-building-stock.yaml b/datasets/nrel-pds-building-stock.yaml index ecd709cda..38fffdda3 100644 --- a/datasets/nrel-pds-building-stock.yaml +++ b/datasets/nrel-pds-building-stock.yaml @@ -8,7 +8,7 @@ Description: | and demand flexibility upgrades applied. Documentation: https://www.nrel.gov/buildings/end-use-load-profiles.html Contact: ComStock@nrel.gov and ResStock@nrel.gov -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: Twice per year Collabs: ASDI: @@ -53,4 +53,3 @@ DataAtWork: - Title: "End-Use Load Profiles for the U.S. Building Stock" URL: https://www.nrel.gov/docs/fy22osti/80889.pdf AuthorName: E. Wilson, A. Parker, A. Fontanini, et al. - diff --git a/datasets/nrel-pds-dsgrid.yaml b/datasets/nrel-pds-dsgrid.yaml index ef2663c04..bf8468c51 100644 --- a/datasets/nrel-pds-dsgrid.yaml +++ b/datasets/nrel-pds-dsgrid.yaml @@ -7,7 +7,7 @@ Description: | production cost models. Documentation: https://www.nrel.gov/analysis/dsgrid.html Contact: elaine.hale@nrel.gov -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: Annually Collabs: ASDI: @@ -42,6 +42,12 @@ Resources: Type: S3 Bucket Explore: - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)' + - Description: '[Demand-Side Grid Model (dsgrid) Building Load Profiles](https://data.openei.org/submissions/8446)' + ARN: arn:aws:s3:::nrel-pds-dsgrid/building/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid&prefix=building%2F)' DataAtWork: Tutorials: - Title: dsgrid Documentation diff --git a/datasets/nrel-pds-ncdb.yaml b/datasets/nrel-pds-ncdb.yaml index 7f75c64d3..12f7da8ae 100644 --- a/datasets/nrel-pds-ncdb.yaml +++ b/datasets/nrel-pds-ncdb.yaml @@ -6,10 +6,9 @@ Description: | The NCDB seeks to maintain the inherent relationship between the various parameters that are needed to model solar, wind, hydrology and load and provide data for multiple important climate scenarios. - Documentation: https://nsrdb.nrel.gov/ Contact: Manajit.Sengupta@nrel.gov -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: As needed Collabs: ASDI: @@ -46,14 +45,13 @@ Resources: Explore: - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fncdb%2F)' DataAtWork: - Tutorials: Tools & Applications: - Title: NCDB Website URL: https://climate.nrel.gov AuthorName: NREL NCDB Team - - Title: HSDS Examples - URL: https://github.com/NREL/hsds-examples - AuthorName: Caleb Phillips, Caroline Draxl, John Readey, Jordan Perr-Sauer, Michael Rossol + - Title: NCDB HSDS Examples + URL: https://github.com/NREL/hsds-examples/blob/master/notebooks/10_NCDB_introduction.ipynb + AuthorName: Reid Olson Publications: - Title: Regridding uncertainty for statistical downscaling of solar radiation URL: https://ascmo.copernicus.org/articles/9/103/2023/ diff --git a/datasets/nrel-pds-nsrdb.yaml b/datasets/nrel-pds-nsrdb.yaml index 69eae4656..027fca976 100644 --- a/datasets/nrel-pds-nsrdb.yaml +++ b/datasets/nrel-pds-nsrdb.yaml @@ -9,7 +9,7 @@ Description: | spatial scales to accurately represent regional solar radiation climates. Documentation: https://nsrdb.nrel.gov/ Contact: nsrdb@nrel.gov -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: Annually Collabs: ASDI: diff --git a/datasets/nrel-pds-porotomo.yaml b/datasets/nrel-pds-porotomo.yaml index 1f38d6bcd..9f1d4d959 100644 --- a/datasets/nrel-pds-porotomo.yaml +++ b/datasets/nrel-pds-porotomo.yaml @@ -7,7 +7,7 @@ Description: | Energy (EERE), U.S. Department of Energy. Documentation: https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Contact: Thomas Coleman (thomas.coleman@silixa.com) -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: As needed Collabs: ASDI: diff --git a/datasets/nrel-pds-sup3rcc.yaml b/datasets/nrel-pds-sup3rcc.yaml index ddaee520b..533939ad4 100644 --- a/datasets/nrel-pds-sup3rcc.yaml +++ b/datasets/nrel-pds-sup3rcc.yaml @@ -18,7 +18,7 @@ Description: | quantify this uncertainty. Documentation: https://github.com/NREL/sup3r Contact: Grant Buster (grant.buster@nrel.gov) -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: Annual Collabs: ASDI: @@ -38,18 +38,90 @@ Resources: Type: S3 Bucket Explore: - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc)' - - Description: 'Sup3rCC - CONUS - MRI ESM 2.0 - SSP585 - r1i1p1f1' + - Description: 'Sup3rCC Generative Models' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/models/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=models%2F)' + - Description: 'Sup3rCC - CONUS - EC-Earth3 - SSP585 - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_ecearth3_ssp585_r1i1p1f1%2F/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_ecearth3_ssp585_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - EC-Earth3-CC - Historical - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_ecearth3cc_historical_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_ecearth3cc_historical_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - EC-Earth3-CC - SSP245 - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_ecearth3cc_ssp245_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_ecearth3cc_ssp245_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - EC-Earth3-Veg - Historical - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_ecearth3veg_historical_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_ecearth3veg_historical_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - EC-Earth3-Veg - SSP245 - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_ecearth3veg_ssp245_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_ecearth3veg_ssp245_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - GFDL-CM4 - Historical - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_gfdlcm4_historical_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_gfdlcm4_historical_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - GFDL-CM4 - SSP245 - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_gfdlcm4_ssp245_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_gfdlcm4_ssp245_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - MPI-ESM1.2-HR - Historical - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_mpiesm12hr_historical_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mpiesm12hr_historical_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - MPI-ESM1.2-HR - SSP245 - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_mpiesm12hr_ssp245_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mpiesm12hr_ssp245_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - MRI-ESM2.0 - Historical - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_mriesm20_historical_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mriesm20_historical_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - MRI-ESM2.0 - SSP585 - r1i1p1f1' ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/ Region: us-west-2 Type: S3 Bucket Explore: - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mriesm20_ssp585_r1i1p1f1%2F)' - - Description: 'Sup3rCC Generative Models' - ARN: arn:aws:s3:::nrel-pds-sup3rcc/models/ + - Description: 'Sup3rCC - CONUS - TaiESM1 - Historical - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_taiesm1_historical_r1i1p1f1/ Region: us-west-2 Type: S3 Bucket Explore: - - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=models%2F)' + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_taiesm1_historical_r1i1p1f1%2F)' + - Description: 'Sup3rCC - CONUS - TaiESM1 - SSP245 - r1i1p1f1' + ARN: arn:aws:s3:::nrel-pds-sup3rcc/conus_taiesm1_ssp245_r1i1p1f1/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_taiesm1_ssp245_r1i1p1f1%2F)' DataAtWork: Tutorials: - Title: Using the Sup3rCC Data diff --git a/datasets/nrel-pds-windai.yaml b/datasets/nrel-pds-windai.yaml index e5ad7eefc..5583f3e6b 100644 --- a/datasets/nrel-pds-windai.yaml +++ b/datasets/nrel-pds-windai.yaml @@ -11,7 +11,7 @@ Description: | documentation that show how to access the data for ML modeling. Documentation: https://github.com/NREL/windAI_bench Contact: Ryan King (ryan.king@nrel.gov) -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: Annually Collabs: ASDI: diff --git a/datasets/nrel-pds-wtk.yaml b/datasets/nrel-pds-wtk.yaml index 06bf752b7..5af640d77 100644 --- a/datasets/nrel-pds-wtk.yaml +++ b/datasets/nrel-pds-wtk.yaml @@ -7,7 +7,7 @@ Description: | integration studies. Documentation: https://www.nrel.gov/grid/wind-toolkit.html Contact: wind-toolkit@nrel.gov -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: As Needed Collabs: ASDI: @@ -230,6 +230,12 @@ Resources: Type: S3 Bucket Explore: - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=wtk-led%2F)' + - Description: Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis (Sup3rWind) + ARN: arn:aws:s3:::nrel-pds-wtk/sup3rwind/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=sup3rwind%2F)' DataAtWork: Tutorials: - Title: HSDS Examples diff --git a/datasets/nsf-ncar-era5.yaml b/datasets/nsf-ncar-era5.yaml index f01db9a52..f213e2d14 100644 --- a/datasets/nsf-ncar-era5.yaml +++ b/datasets/nsf-ncar-era5.yaml @@ -4,6 +4,10 @@ Documentation: https://doi.org/10.5065/BH6N-5N20 Contact: rdahelp@ucar.edu ManagedBy: "[NSF National Center for Atmospheric Research](https://ncar.ucar.edu/)" UpdateFrequency: Monthly, with a 3-4 month lag from realtime +Collabs: + ASDI: + Tags: + - climate Tags: - climate - model diff --git a/datasets/nuview-state.yaml b/datasets/nuview-state.yaml new file mode 100644 index 000000000..04d30d235 --- /dev/null +++ b/datasets/nuview-state.yaml @@ -0,0 +1,44 @@ +Name: NUVIEW - Multi-State Geospatial Data +Description: | + NUVIEW hosts and manages a unified collection of geospatial datasets from multiple U.S. states and agencies + (LiDAR, orthophoto imagery, DEM/DSM, and derivative products). Data are organized in a + single S3 bucket with a logical sub-folder hierarchy: `/state_or_agency_product_type/acqusition_project_name/...`. All assets + are cloud-optimized (COG GeoTIFFs, COPC (Cloud Optimized Point Cloud) LAZ point clouds, etc.) and available under open licenses. +Documentation: Documentation is available for this data at the [s22s/nuview-state-opendata GitHub repository](https://github.com/s22s/nuview-state-opendata) maintained by NUVIEW. +Contact: support@nuview.space +ManagedBy: "[NUVIEW](https://nuview.space/)" +UpdateFrequency: Project-based updates. +Tags: + - aws-pds + - geospatial + - satellite imagery + - natural resource + - sustainability + - disaster response + - dem + - lidar +License: CC0 "Public Domain" (or state/agency-specific open data licenses) +Resources: + - Description: Imagery + ARN: arn:aws:s3:::nuview-state-opendata + Region: us-west-2 + Type: S3 Bucket + - Description: New data notifications + ARN: arn:aws:sns:us-west-2:830737158982:nuview-state-opendata-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Get to Know a Dataset - NUVIEW State Open Data + URL: https://github.com/s22s/nuview-state-opendata/ + NotebookURL: https://github.com/s22s/nuview-state-opendata/blob/main/get-to-know-a-dataset.ipynb + AuthorName: NUVIEW, Inc. + AuthorURL: https://nuview.space + Tools & Applications: + - Title: NUVIEW Geospatial Platform for Alaska + URL: https://alaska.nuview.space/ + AuthorName: NUVIEW, Inc. + AuthorURL: https://nuview.space +ADXCategories: + - Environmental Data + - Public Sector Data diff --git a/datasets/nyc-tlc-trip-records-pds.yaml b/datasets/nyc-tlc-trip-records-pds.yaml index 3cd725b3d..7de425541 100644 --- a/datasets/nyc-tlc-trip-records-pds.yaml +++ b/datasets/nyc-tlc-trip-records-pds.yaml @@ -4,6 +4,10 @@ Documentation: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page Contact: research@tlc.nyc.gov ManagedBy: City of New York Taxi and Limousine Commission UpdateFrequency: As soon as new data is available to be shared publicly. +Collabs: + ASDI: + Tags: + - infrastructure Tags: - aws-pds - cities diff --git a/datasets/nz-elevation.yaml b/datasets/nz-elevation.yaml index aa7af0cf4..556b2abfe 100644 --- a/datasets/nz-elevation.yaml +++ b/datasets/nz-elevation.yaml @@ -8,6 +8,10 @@ Contact: elevation@linz.govt.nz ManagedBy: "[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)" UpdateFrequency: New elevation data will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps. +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - elevation diff --git a/datasets/nz-imagery.yaml b/datasets/nz-imagery.yaml index d7d69625e..c844fdad2 100644 --- a/datasets/nz-imagery.yaml +++ b/datasets/nz-imagery.yaml @@ -7,6 +7,10 @@ Contact: imagery@linz.govt.nz ManagedBy: "[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)" UpdateFrequency: New imagery will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps. +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - aerial imagery diff --git a/datasets/obis.yaml b/datasets/obis.yaml index beeda716d..1e09f3d2c 100644 --- a/datasets/obis.yaml +++ b/datasets/obis.yaml @@ -4,6 +4,10 @@ Documentation: Documentation for this dataset is available at https://github.com Contact: helpdesk@obis.org ManagedBy: The Ocean Biodiversity Information System (OBIS) UpdateFrequency: Weekly +Collabs: + ASDI: + Tags: + - biodiversity Tags: - biodiversity - coastal diff --git a/datasets/oceanomics.yaml b/datasets/oceanomics.yaml index 6bf315676..b78db3d08 100644 --- a/datasets/oceanomics.yaml +++ b/datasets/oceanomics.yaml @@ -2,8 +2,12 @@ Name: OceanOmics Description: "Minderoo Foundation OceanOmics aims to establish environmental DNA (eDNA) as a tool to measure, understand, and protect oceans. OceanOmics mainly generates two types of data: eDNA sequencing data (metabarcoding, metagenomics), and genome assembly data (marine vertebrates)." Documentation: https://edna.minderoo.org Contact: oceanomics@minderoo.org -ManagedBy: Minderoo Foundation OceanOmics (Dr Shannon Corrigan, Dr Philipp Bayer) +ManagedBy: Minderoo Foundation OceanOmics, Dr Shannon Corrigan, Dr Philipp Bayer UpdateFrequency: Data will be continually updated as it is generated. +Collabs: + ASDI: + Tags: + - oceans Tags: - biodiversity - bioinformatics diff --git a/datasets/oedi-data-lake.yaml b/datasets/oedi-data-lake.yaml index 308ecb917..c483cc47b 100644 --- a/datasets/oedi-data-lake.yaml +++ b/datasets/oedi-data-lake.yaml @@ -8,7 +8,7 @@ Description: | analysis and advance innovation. Documentation: https://github.com/openEDI/documentation/ Contact: https://github.com/openEDI/documentation/issues -ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)' +ManagedBy: '[National Laboratory of the Rockies](https://www.nrel.gov/)' UpdateFrequency: As needed Collabs: ASDI: @@ -120,6 +120,30 @@ Resources: Type: S3 Bucket Explore: - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-bench)' + - Description: "[Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI)](https://data.openei.org/submissions/6220)" + ARN: arn:aws:s3:::oedi-data-lake/sup3ruhi/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=sup3ruhi%2F)' + - Description: "[Buildings Sector Scenarios (BSS)](https://data.openei.org/submissions/8558)" + ARN: arn:aws:s3:::oedi-data-lake/building-sector-scenarios/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-sector-scenarios%2F)' + - Description: "[U.S. Agrivoltaic Irradiance Database](https://data.openei.org/submissions/8568)" + ARN: arn:aws:s3:::oedi-data-lake/inspire/agrivoltaics_irradiance/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=inspire%2Fagrivoltaics_irradiance%2F)' + - Description: "[Wind and Structural Loads on Heliostats](https://data.openei.org/submissions/8601)" + ARN: arn:aws:s3:::oedi-data-lake/crescent_dunes/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=crescent_dunes%2F)' DataAtWork: Tools & Applications: - Title: "Tracking the Sun Tool" diff --git a/datasets/ogs-arco-ocean.yaml b/datasets/ogs-arco-ocean.yaml new file mode 100644 index 000000000..2dd61e280 --- /dev/null +++ b/datasets/ogs-arco-ocean.yaml @@ -0,0 +1,38 @@ +Name: ARCO-OCEAN +Description: | + ARCO-OCEAN is an analysis-ready cloud-optimized dataset providing physical properties of the ocean, waves, and sea ice for a period of about 28 years between the 1st of January 1993 and the 30th of June 2021. The dataset includes also atmospheric and hydrological variables that would be needed as boundary conditions and used to drive a numerical simulation. The dataset is the result of collecting, processing, merging and optimizing for the cloud different data sources, all retrospective analyses (reanalyses) or hindcasts of different Earth system components. The dataset has been designed with machine learning in mind, and takes inspiration from similar datasets derived from ERA5. +Documentation: "[ARCO-OCEAN](https://github.com/inogs/arco-ocean)" +Contact: scampanella@ogs.it +ManagedBy: "[OGS](https://www.ogs.it/en/dynamics-ecosystems-and-computational-oceanography)" +UpdateFrequency: Variable (as needed). +Tags: + - aws-pds + - analysis ready data + - atmosphere + - climate + - hydrology + - ice + - machine learning + - oceans + - physics + - zarr +License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)" +Citation: "Campanella, S., Salon, S., Querin, S., Bortolussi, L., and Stock, J.: ARCO-OCEAN: A dataset of physical properties of the ocean, waves, and sea ice, with hydrological and atmospheric forcing, optimized for machine learning, accessed on DD-MM-YYYY." +Resources: + - Description: Zarr analysis ready data + ARN: arn:aws:s3:::ogs-arco-ocean + Region: eu-south-1 + Type: S3 Bucket + - Description: Notifications for new ARCO-OCEAN data + ARN: arn:aws:sns:eu-south-1:985149164500:ogs-arco-ocean-object_created + Region: eu-south-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Computing the Oceanic El Nino Index (ONI) with Xarray and ARCO-OCEAN + URL: https://github.com/inogs/arco-ocean/blob/main/tutorials/oni.ipynb + NotebookURL: https://github.com/inogs/arco-ocean/blob/main/tutorials/oni.ipynb + AuthorName: OGS + AuthorURL: https://github.com/inogs/ +ADXCategories: + - Environmental Data diff --git a/datasets/ome-zarr-open-scivis.yaml b/datasets/ome-zarr-open-scivis.yaml new file mode 100644 index 000000000..db5961416 --- /dev/null +++ b/datasets/ome-zarr-open-scivis.yaml @@ -0,0 +1,50 @@ +Name: OME-Zarr Open SciVis Datasets +Description: This project provides the Open SciVis Datasets in a chunked, highly-compressed, multi-scale format, encodes metadata in JSON according to the OME-Zarr specification, and hosts the datasets on AWS S3 through the AWS Open Data Program, aiming to serve as a web-based resource for the scientific visualization community to enhance reproducibility and facilitate testing and development of OME-Zarr tools. +Documentation: https://github.com/InsightSoftwareConsortium/OMEZarrOpenSciVisDatasets +Contact: "Matt McCormick " +ManagedBy: "NumFOCUS" +UpdateFrequency: On a biannual basis we update the datasets and sync with OME-Zarr standards. +Tags: + - biology + - image processing + - imaging + - neuroimaging + - neuroscience + - life sciences + - magnetic resonance imaging + - computed tomography + - volumetric imaging + - zarr + - aws-pds +License: CC-BY-4.0 unless otherwise specified +Citation: McCormick, M. (2025). OME-Zarr Open SciVis Datasets (v2025.10.31). Zenodo. https://doi.org/10.5281/zenodo.17495294 +Resources: + - Description: OME-Zarr Open SciVis Datasets + ARN: arn:aws:s3:::ome-zarr-scivis + Region: us-east-1 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Read and Visualize in Python + URL: https://github.com/InsightSoftwareConsortium/OMEZarrOpenSciVisDatasets?tab=readme-ov-file#usage + AuthorName: Matt McCormick + AuthorURL: https://github.com/thewtex + Tools & Applications: + - Title: A list of tools and libraries with OME-Zarr support + URL: https://ngff.openmicroscopy.org/tools/index.html + AuthorName: NGFF community + AuthorURL: https://github.com/ome/ngff + Publications: + - Title: "OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies" + URL: https://www.nature.com/articles/s41592-021-01326-w + AuthorName: Josh Moore, Chris Allan, Sébastien Besson, Jean-Marie Burel, Erin Diel, David Gault, Kevin Kozlowski, Dominik Lindner, Melissa Linkert, Trevor Manz, Will Moore, Constantin Pape, Christian Tischer & Jason R. Swedlow + - Title: "OME-Zarr: a cloud-optimized bioimaging file format with international community support" + URL: https://link.springer.com/article/10.1007/s00418-023-02209-1 + AuthorName: Josh Moore, Daniela Basurto-Lozada, Sébastien Besson, John Bogovic, Jordão Bragantini, Eva M. Brown, Jean-Marie Burel, Xavier Casas Moreno, Gustavo de Medeiros, Erin E. Diel, David Gault, Satrajit S. Ghosh, Ilan Gold, Yaroslav O. Halchenko, Matthew Hartley, Dave Horsfall, Mark S. Keller, Mark Kittisopikul, Gabor Kovacs, Aybüke Küpcü Yoldaş, Koji Kyoda, Albane le Tournoulx de la Villegeorges, Tong Li, Prisca Liberali, Dominik Lindner, Melissa Linkert, Joel Lüthi, Jeremy Maitin-Shepard, Trevor Manz, Luca Marconato, Matthew McCormick, Merlin Lange, Khaled Mohamed, William Moore, Nils Norlin, Wei Ouyang, Bugra Özdemir, Giovanni Palla, Constantin Pape, Lucas Pelkmans, Tobias Pietzsch, Stephan Preibisch, Martin Prete, Norman Rzepka, Sameeul Samee, Nicholas Schaub, Hythem Sidky, Ahmet Can Solak, David R. Stirling, Jonathan Striebel, Christian Tischer, Daniel Toloudis, Isaac Virshup, Petr Walczysko, Alan M. Watson, Erin Weisbart, Frances Wong, Kevin A. Yamauchi, Omer Bayraktar, Beth A. Cimini, Nils Gehlenborg, Muzlifah Haniffa, Nathan Hotaling, Shuichi Onami, Loic A. Royer, Stephan Saalfeld, Oliver Stegle, Fabian J. Theis & Jason R. Swedlow + - Title: Open SciVis Datasets + URL: http://klacansky.com/open-scivis-datasets/ + AuthorName: Pavol Klacansky +DeprecatedNotice: +ADXCategories: + - Healthcare & Life Sciences Data + - Manufacturing Data diff --git a/datasets/ont_basemod_data.yaml b/datasets/ont_basemod_data.yaml new file mode 100644 index 000000000..bd50e2a5c --- /dev/null +++ b/datasets/ont_basemod_data.yaml @@ -0,0 +1,37 @@ +Name: ONT Methylation Benchmarking Datasets +Description: ONT Methylation Benchmarking Datasets are generated to benchmark existing methylation-calling tools on the Oxford Nanopore sequencing platform using their recent R10.4.1 flowcell chemistry. It spans a diverse range of species, including bacteria (E. coli, H. pylori J99, H. pylori 26695, A. variabilis, T. denticola), plants (Rice, Arabidopsis), and mammals (mouse, human).In addition, the dataset includes EMSeq data for E. coli, plant, and mouse samples, which can serve as ground truth for methylation studies. It also provides unmethylated whole-genome amplified (WGA) DNA for H. pylori 26695 and a dam- dcm- double mutant (DM) of E. coli that lacks canonical 5mC and 6mA methylation. These variants, together with their wild-type counterparts, offer value for both training and benchmarking DNA methylation calling models. +Documentation: https://github.com/SowpatiLab/ont-basemod-benchmark-data/blob/main/documentation.md +Contact: "onkar.ccmb@csir.res.in; tej.ccmb@csir.res.in" +ManagedBy: "[CSIR-Centre for Cellular and Molecular Biology](https://www.ccmb.res.in/)" +UpdateFrequency: Datasets will be updated periodically as additional data is generated. +Tags: + - aws-pds + - life sciences + - genomic + - long read sequencing + - bioinformatics + - epigenomics + - benchmark + - bam +License: "[MIT License](https://opensource.org/license/mit)" +Citation: "Please cite Kulkarni et al. Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation. bioRxiv (2024). doi: https://doi.org/10.1101/2024.11.09.622763 when referencing the ONT methylation benchmarking datasets in publications." +Resources: + - Description: ONT Methylation Benchmarking Datasets + ARN: arn:aws:s3:::ont-basemod-benchmark-data + Region: ap-south-1 + Type: S3 Bucket + Explore: + - '[Browse Bucket](https://ont-basemod-benchmark-data.s3.amazonaws.com/index.html)' + - Description: Notifications for object created + ARN: arn:aws:sns:ap-south-1:767415906609:ont-basemod-benchmark-data-object_created + Region: ap-south-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Methylation calling using ONT methylation benchmarking dataset + URL: https://github.com/SowpatiLab/ont-basemod-benchmark-data/blob/main/tutorial.md + AuthorName: Onkar Kulkarni + Publications: + - Title: Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation + URL: https://www.biorxiv.org/content/10.1101/2024.11.09.622763v1 + AuthorName: Kulkarni et al. diff --git a/datasets/open-ceda.yaml b/datasets/open-ceda.yaml index 0add6f10d..4445b922f 100644 --- a/datasets/open-ceda.yaml +++ b/datasets/open-ceda.yaml @@ -1,8 +1,8 @@ Name: Open CEDA by Watershed Description: | CEDA is a multi-regional Environmentally-Extended Input-Output (EEIO) model developed to support a wide range of environmental systems analyses—including corporate carbon accounting and sustainable spend analysis. CEDA provides unparalleled global coverage and granularity, representing 95% of the world's GDP across 148 countries and 400 sectors, enabling robust and geographically comprehensive Scope 3 greenhouse gas (GHG) measurement. - Open CEDA is the publicly avaialable version of CEDA, now easy to download and available for free for all use cases. For more information please visit our website at openceda.org - CEDA 2024, the latest version of CEDA, uses 2022 as its base year, ensuring that emissions factors and economic data reflect the most recent global economic landscape available. To maintain accuracy and relevance, CEDA is updated annually with the latest data releases. + Open CEDA is the publicly avaialable version of CEDA, now easy to download and available for free for all use cases. For more information please visit our website at openceda.org. + This data registry entry contains CEDA 2025 and CEDA 2024 in two separate files. CEDA 2025, the latest version of CEDA, uses 2023 as its base year, ensuring that emissions factors and economic data reflect the most recent global economic landscape available. To maintain accuracy and relevance, CEDA is updated annually with the latest data releases. At its core, CEDA connects economic exchanges to GHG emissions by quantifying the life-cycle emissions of products and services. This is achieved through the integration of input-output tables, which represent the full supply-chain network of the global economy, with GHG emissions data. As a result, CEDA provides users with a powerful tool to assess the environmental impacts embedded in corporate value chains. Documentation: https://openceda.org/ Contact: ceda-support@watershed.com diff --git a/datasets/open-meteo.yaml b/datasets/open-meteo.yaml index aba932244..ec73a37fd 100644 --- a/datasets/open-meteo.yaml +++ b/datasets/open-meteo.yaml @@ -13,6 +13,10 @@ Documentation: https://github.com/open-meteo/open-data Contact: info@open-meteo.com ManagedBy: "[Open-Meteo](https://www.open-meteo.com/)" UpdateFrequency: Hourly +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - agriculture diff --git a/datasets/open-robo-care.yaml b/datasets/open-robo-care.yaml new file mode 100644 index 000000000..eef0e4b96 --- /dev/null +++ b/datasets/open-robo-care.yaml @@ -0,0 +1,34 @@ +Name: OpenRoboCare Multi-Modal Expert Demonstration Dataset for Robot-Assisted Caregiving +Description: A comprehensive multimodal dataset capturing real-world caregiving routines from 21 occupational therapists performing 15 daily caregiving tasks. The dataset includes synchronized RGB-D video, tactile sensing, eye-gaze tracking, pose annotations, and action labels across 315 sessions totaling 19.8 hours of expert demonstrations. Data modalities include anonymized RGB images, depth maps, 44-sensor tactile readings, 2D/3D pose tracking, temporal action annotations, and first/third-person videos, enabling research in robot learning from demonstration, multimodal perception, and safe human-robot interaction for caregiving applications. +Documentation: https://emprise.cs.cornell.edu/robo-care/docs +Contact: https://emprise.cs.cornell.edu/robo-care/ +ManagedBy: "[EmPRISE Lab at Cornell University](https://emprise.cs.cornell.edu/)" +UpdateFrequency: Static dataset - no regular updates planned +Tags: + - computer vision + - robotics + - machine learning + - health + - aws-pds + - life sciences +License: "BSD-3-Clause license - Academic and non-commercial use permitted. See documentation for full terms." +Citation: "Liang, X., Liu, Z., Lin, K., Gu, E., Ye, R., Nguyen, T., Hsu, C., Wu, Z., Yang, X., Cheung, C.S.Y., Soh, H., Dimitropoulou, K., & Bhattacharjee, T. (2025). OpenRoboCare: A Multimodal Multi-Task Expert Demonstration Dataset for Robot Caregiving. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)." +Resources: + - Description: Full Dataset + ARN: arn:aws:s3:::open-robo-care + Region: us-west-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: "Get To Know A Dataset: OpenRoboCare" + URL: https://github.com/empriselab/open-data-examples/blob/main/open-robo-care/get-to-know-a-dataset.ipynb + AuthorName: Cornell University EmPRISE Lab + AuthorURL: https://emprise.cs.cornell.edu/ + - Title: OpenRoboCare Dataset Viewer + URL: https://emprise.cs.cornell.edu/robo-care-viewer/ + AuthorName: Cornell University EmPRISE Lab + AuthorURL: https://emprise.cs.cornell.edu/ + Publications: + - Title: "OpenRoboCare: A Multimodal Multi-Task Expert Demonstration Dataset for Robot Caregiving" + URL: https://emprise.cs.cornell.edu/robo-care/ + AuthorName: Liang X, Liu Z, Lin K, et al. diff --git a/datasets/openaerialmap.yaml b/datasets/openaerialmap.yaml index cdbc871cc..e09392148 100644 --- a/datasets/openaerialmap.yaml +++ b/datasets/openaerialmap.yaml @@ -4,6 +4,10 @@ Documentation: https://docs.openaerialmap.org/ Contact: info@openaerialmap.org ManagedBy: "[Humanitarian OpenStreetMap Team](https://www.hotosm.org/)" UpdateFrequency: New imagery is added as soon as it is uploaded by community contributors. +Collabs: + ASDI: + Tags: + - disaster response Tags: - satellite imagery - aerial imagery diff --git a/datasets/openfoodfacts-images.yaml b/datasets/openfoodfacts-images.yaml index 449c46280..fec58c6a9 100644 --- a/datasets/openfoodfacts-images.yaml +++ b/datasets/openfoodfacts-images.yaml @@ -6,6 +6,10 @@ Contact: contact@openfoodfacts.org ManagedBy: "[Open Food Facts](https://world.openfoodfacts.org)" UpdateFrequency: Monthly License: All data contained in this dataset is licenced under the [Creative Commons Attribution ShareAlike licence](https://creativecommons.org/licenses/by-sa/3.0/deed.en) +Collabs: + ASDI: + Tags: + - agriculture Tags: - aws-pds - machine learning diff --git a/datasets/openhgl.yaml b/datasets/openhgl.yaml new file mode 100644 index 000000000..d5e3453ab --- /dev/null +++ b/datasets/openhgl.yaml @@ -0,0 +1,41 @@ +Name: Open Human Genome Library +Description: > + The Open Human Genome Library (OpenHGL) is a collection of high-quality + *de novo* human assemblies that are publicly available in genomic databases + (e.g. NCBI and CNCB) or from individual research papers. It provides + consistent naming and uniform formats across datasets, supporting efficient + subsequence retrieval and approximate string search. +Documentation: https://lh3.github.io/OpenHGL/ +Contact: https://github.com/lh3/OpenHGL/issues +ManagedBy: Heng Li lab at Dana-Farber Cancer Institute and Harvard Medical School +UpdateFrequency: As new data or new analyses become available +Tags: + - aws-pds + - bioinformatics + - genomic + - biology + - life sciences +License: Creative Commons Zero (CC0) +Resources: + - Description: > + This bucket contains genomic sequences in the AGC format and the + corresponding FM-index in the ropebwt3 format. + ARN: arn:aws:s3:::openhgl + Region: us-east-1 + Type: S3 Bucket + - Description: Notifications for OpenHGL updates + ARN: arn:aws:sns:us-east-1:104240442756:openhgl-object_created + Region: us-east-1 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Using OpenHGL data + URL: https://lh3.github.io/OpenHGL/ + AuthorName: Heng Li + Publications: + - Title: "AGC: compact representation of assembled genomes with fast queries and updates" + URL: https://pubmed.ncbi.nlm.nih.gov/36864624/ + AuthorName: Sebastian Deorowicz, Agnieszka Danek, Heng Li + - Title: BWT construction and search at the terabase scale + URL: https://doi.org/10.1093/bioinformatics/btae717 + AuthorName: Heng Li diff --git a/datasets/opentargets.yaml b/datasets/opentargets.yaml index 501416684..4e5ec456b 100644 --- a/datasets/opentargets.yaml +++ b/datasets/opentargets.yaml @@ -1,45 +1,76 @@ -Deprecated: True -DeprecatedNotice: Amazon is no longer hosting this Data Lakehouse Ready dataset -Name: Open Targets - Data Lakehouse Ready -Description: | - This a Parquet representation of the Open Targets Platform's [latest export](https://www.targetvalidation.org/downloads/data). The Open Targets Platform integrates evidence from genetics, genomics, transcriptomics, drugs, animal models and scientific literature to score and rank target-disease associations for drug target identification. The Open Targets Platform (https://www.targetvalidation.org) is a freely available resource for the integration of genetics, genomics, and chemical data to aid systematic drug target identification and prioritisation. - This dataset is 'Lakehouse Ready'. Meaning, you can query this data in-place straight out of the Registry of Open Data S3 bucket. [Deploy this dataset's corresponding CloudFormation template](https://console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/quickcreate?templateUrl=https%3A%2F%2Faws-roda-hcls-datalake.s3.amazonaws.com%2FOpenTargets.latest.RodaTemplate.json&stackName=OpenTargets-Latest-RODA) to create the AWS Glue catalog entries into your account in about 30 seconds. That one step will enable you to write SQL with AWS Athena, build dashboards and charts with Amazon Quicksight, perform HPC with AWS EMR, or join into your AWS Redshift clusters. More detail in (the documentation)[https://github.com/aws-samples/data-lake-as-code/blob/roda/README.md. -Documentation: https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_install.md -Contact: https://github.com/aws-samples/data-lake-as-code/issues -ManagedBy: "[Amazon Web Services](https://aws.amazon.com/)" -UpdateFrequency: Within two weeks of new Open Targets releases -Tags: - - chemistry +Name: Open Targets +Description: The Open Targets Platform is a comprehensive data integration tool that supports systematic identification and prioritisation of potential therapeutic drug targets. By integrating publicly available datasets including data generated by the Open Targets experimental and informatics research programmes, the Platform provides data and services to assist in the task of therapeutic hypothesis building. +Documentation: https://platform-docs.opentargets.org/ +Contact: outreach@opentargets.org +ManagedBy: Open Targets +UpdateFrequency: The data is released every three months. +Tags: - genetic - - genomic - - molecule - life sciences - - biotech blueprint - - parquet -License: https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_attributions.txt -Resources: - - Description: Latest Open Targets release. Updates within two weeks of new Open Targets version. Information on Open Targets releases can be found [here](https://www.targetvalidation.org/downloads/data). - ARN: arn:aws:s3:::aws-roda-hcls-datalake/opentargets_latest/ - Region: us-east-1 - Type: S3 Bucket - - Description: Open Targets v20.06. Does not update. - ARN: arn:aws:s3:::aws-roda-hcls-datalake/opentargets_20_06/ - Region: us-east-1 + - genomic + - aws-pds + - bioinformatics + - biology + - protein + - drug discovery +License: https://creativecommons.org/publicdomain/zero/1.0/ +Resources: + - Description: Open Targets Data + ARN: arn:aws:s3:::open-targets-public-data-releases + Region: eu-west-1 Type: S3 Bucket - - Description: Open Targets v19.11. Does not update - ARN: arn:aws:s3:::aws-roda-hcls-datalake/opentargets_1911/ - Region: us-east-1 - Type: S3 Bucket + - Description: Notifications for new Open Targets data releases + ARN: arn:aws:sns:eu-west-1:674693859687:open-targets-public-data-releases-object_created + Region: eu-west-1 + Type: SNS Topic DataAtWork: Tutorials: - - Title: Data Lake as Code Deployment Guide - URL: https://github.com/aws-samples/data-lake-as-code/blob/roda/docs/roda_install.md - AuthorName: AWS Biotech Blueprints Team - Services: - - Amazon Athena - - AWS Glue - - AWS Lake Formation + - Title: Platform datasets on AWS + URL: https://platform-docs.opentargets.org/data-access/platform-datasets-on-aws + NotebookURL: https://colab.research.google.com/github/opentargets/notebooks/blob/main/notebooks/reading_data_from_aws.ipynb + AuthorName: Daniel Suveges + AuthorURL: https://www.ebi.ac.uk/people/person/daniel-suveges/ + - Title: Autoimmune colocalisations + URL: https://github.com/opentargets/notebooks + NotebookURL: https://github.com/opentargets/notebooks/blob/main/notebooks/autoimmune_colocalisations.ipynb + AuthorName: Open Targets Team + AuthorURL: https://github.com/opentargets + - Title: Autoimmune credible sets + URL: https://github.com/opentargets/notebooks + NotebookURL: https://github.com/opentargets/notebooks/blob/main/notebooks/autoimmune_credible_set.ipynb + AuthorName: Open Targets Team + AuthorURL: https://github.com/opentargets + - Title: ChEMBL Evidence Data Download + URL: https://github.com/opentargets/notebooks + NotebookURL: https://github.com/opentargets/notebooks/blob/main/notebooks/chembl_evidence_download.ipynb + AuthorName: Open Targets Team + AuthorURL: https://github.com/opentargets + - Title: Exploration of Open Targets datasets + URL: https://github.com/opentargets/notebooks + NotebookURL: https://github.com/opentargets/notebooks/blob/main/notebooks/exploring_ot_datasets.ipynb + AuthorName: Open Targets Team + AuthorURL: https://github.com/opentargets + - Title: Open Targets informatics tools + URL: https://www.ebi.ac.uk/training/online/courses/open-targets-quick-tour/ + AuthorName: Helena Cornu + AuthorURL: https://www.ebi.ac.uk/people/person/helena-cornu/ + - Title: Getting started with the Open Targets Platform GraphQL API + URL: https://www.ebi.ac.uk/training/events/getting-started-open-targets-platform-graphql-api/ + AuthorName: Helena Cornu + AuthorURL: https://www.ebi.ac.uk/people/person/helena-cornu/ + Tools & Applications: + - Title: Open Targets Platform Web Application + URL: https://platform.opentargets.org/ + AuthorName: Open Targets + AuthorURL: https://github.com/opentargets/ot-ui-apps + - Title: Open Targets Platform API + URL: https://api.platform.opentargets.org/ + AuthorName: Open Targets + AuthorURL: https://github.com/opentargets/platform-api Publications: - - Title: Data Lake as Code, Featuring ChEMBL and Open Targets - URL: https://aws.amazon.com/blogs/startups/a-data-lake-as-code-featuring-chembl-and-opentargets/ - AuthorName: Paul Underwood + - Title: "Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery" + URL: https://doi.org/10.1093/nar/gkae1128 + AuthorName: Annalisa Buniello + AuthorURL: https://orcid.org/0000-0002-4623-8642 +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/os-climate-physrisk.yaml b/datasets/os-climate-physrisk.yaml index 4129135db..a4fd74e49 100644 --- a/datasets/os-climate-physrisk.yaml +++ b/datasets/os-climate-physrisk.yaml @@ -4,6 +4,10 @@ Documentation: https://physrisk.readthedocs.io/en/latest/ Contact: https://os-climate.org/contact-us/ ManagedBy: "[OS-Climate](https://os-climate.org/)" UpdateFrequency: Data is updated as new important public domain datasets become available or if corrections are published. +Collabs: + ASDI: + Tags: + - climate Tags: - climate risk - physical diff --git a/datasets/palsar-2-scansar-flooding-in-bangladesh.yaml b/datasets/palsar-2-scansar-flooding-in-bangladesh.yaml index 31da24f06..b06a6c86a 100644 --- a/datasets/palsar-2-scansar-flooding-in-bangladesh.yaml +++ b/datasets/palsar-2-scansar-flooding-in-bangladesh.yaml @@ -5,6 +5,10 @@ License: Data is available for free under the terms of use. Documentation: https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm ManagedBy: "[JAXA](https://www.jaxa.jp/)" Contact: aproject@jaxa.jp +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - agriculture diff --git a/datasets/palsar-2-scansar-flooding-in-rwanda.yaml b/datasets/palsar-2-scansar-flooding-in-rwanda.yaml index 690faaa11..6bb81e5e7 100644 --- a/datasets/palsar-2-scansar-flooding-in-rwanda.yaml +++ b/datasets/palsar-2-scansar-flooding-in-rwanda.yaml @@ -5,6 +5,10 @@ License: Data is available for free under the terms of use. Documentation: https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm ManagedBy: "[JAXA](https://www.jaxa.jp/)" Contact: aproject@jaxa.jp +Collabs: + ASDI: + Tags: + - disaster response Tags: - aws-pds - agriculture diff --git a/datasets/panstarrs.yaml b/datasets/panstarrs.yaml index 24107d68d..75e4c3513 100644 --- a/datasets/panstarrs.yaml +++ b/datasets/panstarrs.yaml @@ -12,7 +12,7 @@ Tags: License: STScI hereby grants the non-exclusive, royalty-free, non-transferable, worldwide right and license to use, reproduce, and publicly display in all media data from the PS1 surveys. Resources: - Description: PS1 DR1 and DR2 image files - ARN: arn:aws:s3:::stpubdata/ps1 + ARN: arn:aws:s3:::stpubdata/panstarrs/ps1 Region: us-east-1 Type: S3 Bucket RequesterPays: False diff --git a/datasets/pasteur-logan.yaml b/datasets/pasteur-logan.yaml index 577bf8b97..9d738b29f 100644 --- a/datasets/pasteur-logan.yaml +++ b/datasets/pasteur-logan.yaml @@ -52,4 +52,7 @@ DataAtWork: URL: https://github.com/asl/f2sz AuthorName: Anton Korobeynikov AuthorURL: https://anton.korobeynikov.info/ - + Publications: + - Title: Logan - Planetary-Scale Genome Assembly Surveys Life’s Diversity + URL: https://www.biorxiv.org/content/10.1101/2024.07.30.605881v2.full + AuthorName: Chikhi R., Lemane T., Loll-Krippleber R., et al (2025) diff --git a/datasets/pdb-3d-structural-biology-data.yaml b/datasets/pdb-3d-structural-biology-data.yaml index 8597c1023..16d3ca9af 100644 --- a/datasets/pdb-3d-structural-biology-data.yaml +++ b/datasets/pdb-3d-structural-biology-data.yaml @@ -11,7 +11,7 @@ Description: > Electron Microscopy Data Bank (wwPDB-designated Archive Keeper: EMDB) Biological Magnetic Resonance Bank (wwPDB-designated Archive Keeper: BMRB) -Documentation: https://www.wwpdb.org/documentation/file-format +Documentation: "https://www.wwpdb.org/documentation/file-format" Contact: https://www.wwpdb.org/about/contact ManagedBy: "[Worldwide Protein Data Bank Partnership](wwpdb.org)" UpdateFrequency: | @@ -54,6 +54,19 @@ Resources: Explore: - '[Browse Bucket](https://pdbsnapshots.s3.us-west-2.amazonaws.com/index.html)' DataAtWork: + Tutorials: + - Title: "Get to Know a Dataset: Protein Data Bank 3D Structural Biology Data" + URL: https://github.com/rcsb/AWS-Open_Data_Registry/blob/master/PDB_3D_Dataset_Tour.ipynb + AuthorName: RCSB PDB + AuthorURL: https://rcsb.org/ + - Title: "PDB 101" + URL: https://pdb101.rcsb.org/ + AuthorName: RCSB PDB + AuthorURL: https://rcsb.org/ + - Title: "File Download Services" + URL: https://www.rcsb.org/docs/programmatic-access/file-download-services + AuthorName: RCSB PDB + AuthorURL: https://rcsb.org/ Publications: - Title: "Announcing the worldwide Protein Data Bank" URL: https://doi.org/10.1038/nsb1203-980 diff --git a/datasets/planette_c3s_seasonal_forecast_data.yaml b/datasets/planette_c3s_seasonal_forecast_data.yaml new file mode 100644 index 000000000..66002ca30 --- /dev/null +++ b/datasets/planette_c3s_seasonal_forecast_data.yaml @@ -0,0 +1,66 @@ +Name: Planette C3S Seasonal Forecast Data +Description: | + The C3S seasonal forecast dataset provides global, daily, probabilistic forecasts of the Earth system, + enabling users to assess the likelihood of future climate states. These forecasts are particularly + valuable for studying slowly evolving climate patterns such as El Niño, La Niña, and the North Atlantic + Oscillation (NAO), which can be predicted with greater skill than the chaotic atmosphere. This dataset + is derived from the Copernicus Climate Change Service (C3S) archive and includes SEAS5 hindcasts + (1981-2016) and forecasts (2017-present) at 1°x1° global resolution. More models from the C3S archive will + be updated as they are processed into cloud native format. The planette C3S archive stores this data in + cloud native format for easy access and analysis. +Documentation: https://github.com/PlanetteAI/planette_c3s_archive/blob/main/README.md +Contact: aodhan.sweeney@planette.ai +ManagedBy: Planette.ai +UpdateFrequency: Monthly +Collabs: + ASDI: + Tags: + - climate + - weather + - forecast +Tags: + - aws-pds + - climate + - weather + - earth observation +License: | + Copernicus Licence (similar to CC-BY-4.0): You are free to share and adapt the material + for any purpose, even commercially, provided that you give appropriate credit. + https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf +Citation: | + Copernicus Climate Change Service (C3S) (2017): C3S seasonal forecast data. + Copernicus Climate Change Service, Climate Data Store (CDS). + https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-original-single-levels +Resources: + - Description: C3S Seasonal Forecast Hindcasts and Forecasts (Zarr format) + ARN: arn:aws:s3:::planette-c3s-seasonal-forecasts/seas5/ + Region: us-east-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://planette-c3s-seasonal-forecasts.s3.amazonaws.com/index.html#seas5)' +DataAtWork: + Tutorials: + - Title: Accessing C3S Seasonal Forecast Data with Python + URL: https://github.com/PlanetteAI/planette_c3s_archive/blob/main/c3s_seasonal_forecast_tutorial.ipynb + AuthorName: "Aodhan Sweeney-Jaramillo" + AuthorURL: https://github.com/AodhanSweeney + Tools & Applications: + - Title: xarray + URL: https://docs.xarray.dev/ + AuthorName: xarray Developers + - Title: zarr-python + URL: https://zarr.dev/ + AuthorName: zarr Developers + - Title: icechunk + URL: https://github.com/earth-mover/icechunk + AuthorName: earth-mover + Publications: + - Title: "SEAS5: The new ECMWF seasonal forecast system" + URL: https://doi.org/10.5194/gmd-12-1087-2019 + AuthorName: Johnson, S. J., et al. + - Title: "C3S Seasonal Forecasts Documentation" + URL: https://climate.copernicus.eu/seasonal-forecasts + AuthorName: Copernicus Climate Change Service +DeprecatedNotice: +ADXCategories: + - Environmental Data diff --git a/datasets/planette_era5_reanalysis.yaml b/datasets/planette_era5_reanalysis.yaml new file mode 100644 index 000000000..589445847 --- /dev/null +++ b/datasets/planette_era5_reanalysis.yaml @@ -0,0 +1,69 @@ +Name: Planette ERA5 Archive +Description: | + The ERA5 archive provides a comprehensive record of global weather and climate from 1940 to present, + with multiple temporal aggregations for flexible analysis. This dataset is derived from + the ECMWF/Copernicus ERA5 reanalysis and includes daily means, 7-day rolling means, + and monthly/seasonal aggregations at 0.25°×0.25° global resolution. The Planette ERA5 archive stores + this data in cloud-native format (Zarr with icechunk) for efficient access and analysis. + + The dataset includes essential atmospheric variables at both surface and pressure levels, enabling a + wide range of climate analyses, from daily weather patterns to long-term climate trends. Daily means + are computed by averaging hourly ERA5 data, while longer temporal aggregations are derived from these + daily means. +Documentation: https://github.com/PlanetteAI/planette_era5_archive/blob/main/README.md +Contact: aodhan.sweeney@planette.ai +ManagedBy: Planette.ai +UpdateFrequency: Monthly +Collabs: + ASDI: + Tags: + - climate + - weather + - forecast +Tags: + - aws-pds + - climate + - weather + - earth observation +License: | + Copernicus Licence (similar to CC-BY-4.0): You are free to share and adapt the material + for any purpose, even commercially, provided that you give appropriate credit. + https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf +Citation: | + Hersbach, H., Bell, B., Berrisford, P., et al. (2020): The ERA5 global reanalysis. + Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. + https://doi.org/10.1002/qj.3803 + +Resources: + - Description: ERA5 Reanalysis Data with Multiple Temporal Aggregations (Zarr format) + ARN: arn:aws:s3:::planette-era5/era5/ + Region: us-east-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://planette-era5.s3.amazonaws.com/index.html#era5/)' +DataAtWork: + Tutorials: + - Title: Accessing ERA5 Data with Python + URL: https://github.com/PlanetteAI/planette_era5_archive/blob/main/planette_era5_tutorial.ipynb + AuthorName: "Aodhan Sweeney-Jaramillo" + AuthorURL: https://github.com/AodhanSweeney + Tools & Applications: + - Title: xarray + URL: https://docs.xarray.dev/ + AuthorName: xarray Developers + - Title: zarr-python + URL: https://zarr.dev/ + AuthorName: zarr Developers + - Title: icechunk + URL: https://github.com/earth-mover/icechunk + AuthorName: earth-mover + Publications: + - Title: "The ERA5 global reanalysis" + URL: https://doi.org/10.1002/qj.3803 + AuthorName: Hersbach, H., et al. + - Title: "ERA5 Documentation" + URL: https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation + AuthorName: ECMWF +DeprecatedNotice: +ADXCategories: + - Environmental Data diff --git a/datasets/pohang-canal-dataset.yaml b/datasets/pohang-canal-dataset.yaml index bb004a3e5..d8a4422fe 100644 --- a/datasets/pohang-canal-dataset.yaml +++ b/datasets/pohang-canal-dataset.yaml @@ -4,6 +4,10 @@ Documentation: https://sites.google.com/view/pohang-canal-dataset/home Contact: morin-lab@kaist.ac.kr ManagedBy: "[MORIN](http://morin.kaist.ac.kr)" UpdateFrequency: Not updated +Collabs: + ASDI: + Tags: + - oceans Tags: - aws-pds - autonomous vehicles diff --git a/datasets/proj-datum-grids.yaml b/datasets/proj-datum-grids.yaml index fdc343ed9..672f09a64 100644 --- a/datasets/proj-datum-grids.yaml +++ b/datasets/proj-datum-grids.yaml @@ -4,6 +4,10 @@ Documentation: https://github.com/OSGeo/proj-datumgrid-geotiff Contact: proj@lists.osgeo.org ManagedBy: "[PROJ](https://proj.org)" UpdateFrequency: New grids are added when made available +Collabs: + ASDI: + Tags: + - infrastructure Tags: - aws-pds - geospatial diff --git a/datasets/proteingym.yaml b/datasets/proteingym.yaml new file mode 100644 index 000000000..13aed7c2f --- /dev/null +++ b/datasets/proteingym.yaml @@ -0,0 +1,36 @@ +Name: ProteinGym +Description: | + ProteinGym is a benchmark suite for assessing the performance of protein fitness prediction and design models. It comprises a large curated collection of 200+ high-throughput experimental assays (~3M mutated sequences), as well as clinical annotations from experts about the pathogenicity of mutants in over 3k human genes. +Documentation: https://github.com/OATML-Markslab/ProteinGym/blob/main/README.md +Contact: pascal_notin@hms.harvard.edu +ManagedBy: "Harvard Medical School, University of Oxford" +UpdateFrequency: Quarterly +Tags: + - aws-pds + - protein + - bioinformatics + - biology + - life sciences + - deep learning + - machine learning +License: MIT License +Resources: + - Description: "ProteinGym dataset including all substitution/indel mutations from Deep Mutational Scanning (DMS) experiments (DMS_substitutions.parquet / DMS_indels.parquet), and all substitution/indel mutations from clinical variant databases (clinical_substitutions.parquet / clinical_indels.parquet)." + ARN: arn:aws:s3:::proteingym + Region: us-east-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + - Title: Scoring ProteinGym assays with TranceptEVE + URL: https://github.com/OATML-Markslab/ProteinGym/blob/main/notebooks/TranceptEVE_example.ipynb + AuthorName: Daniel Ritter + AuthorURL: https://danieldritter.github.io/ + Tools & Applications: + - Title: ProteinGym website + URL: https://proteingym.org/ + AuthorName: Pascal Notin & Daniel Ritter + Publications: + - Title: "ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design" + URL: https://papers.nips.cc/paper_files/paper/2023/hash/cac723e5ff29f65e3fcbb0739ae91bee-Abstract-Datasets_and_Benchmarks.html + AuthorName: "Pascal Notin, et al." + AuthorURL: https://www.pascalnotin.com/ diff --git a/datasets/racecar-dataset.yaml b/datasets/racecar-dataset.yaml index 92a067340..5d66f32d4 100644 --- a/datasets/racecar-dataset.yaml +++ b/datasets/racecar-dataset.yaml @@ -4,6 +4,10 @@ Documentation: https://github.com/linklab-uva/RACECAR_DATA Contact: Prof. Madhur Behl (madhur.behl@viginia.edu) ManagedBy: Amar Kulkarni (ark8su@virginia.edu) UpdateFrequency: This dataset was constructed during a single racing season (2021-22). Future seasons may potentially be added. +Collabs: + ASDI: + Tags: + - infrastructure Tags: - aws-pds - autonomous vehicles diff --git a/datasets/radarsat-1.yaml b/datasets/radarsat-1.yaml index f262cc19f..395400db2 100644 --- a/datasets/radarsat-1.yaml +++ b/datasets/radarsat-1.yaml @@ -1,13 +1,19 @@ Name: RADARSAT-1 Description: "Developed and operated by the Canadian Space Agency, it is Canada's first commercial Earth observation satellite +
+
+ Développé et exploité par l'Agence spatiale canadienne, il s'agit du premier satellite commercial d'observation de la Terre au Canada." Documentation: https://www.asc-csa.gc.ca/eng/satellites/radarsat1/what-is-radarsat1.asp Contact: https://www.eodms-sgdot.nrcan-rncan.gc.ca ManagedBy: "[Natural Resources Canada](https://nrcan.gc.ca/)" -UpdateFrequency: "Products are added on an adhoc basis driven by prioritized foreign repatriation efforts and new processing orders from the raw archive. +UpdateFrequency: "NRCan opened a new [RADARSAT-1 Processing service](https://github.com/eodms-sgdot/radarsat-notebooks/blob/main/examples/radarsat1_l1_processing.ipynb) to the public on December 18, 2025. The processor allows users to produce image products from the original raw data archive free of charge, with the primary goal of making previously unprocessed data accessible for the first time. + +
+
-Les produits sont ajoutés de manière ponctuelle en fonction des efforts prioritaires de rapatriement à l'étranger et des nouvelles commandes de traitement à partir des archives brutes." +RNCan a ouvert un nouveau [service de traitement RADARSAT-1](https://github.com/eodms-sgdot/radarsat-notebooks/blob/main/examples/radarsat1_l1_processing.ipynb) au public le 18 décembre 2025. Le processeur permet aux utilisateurs de produire des produits d'imagerie à partir des archives de données brutes originales gratuitement, avec l'objectif principal de rendre accessible pour la première fois les données jamais traitées auparavant." Collabs: ASDI: Tags: @@ -31,20 +37,11 @@ Resources: Type: S3 Bucket DataAtWork: Tutorials: - - Title: PCI Geomatics Webinar | Cloud enabling earth observation archives - URL: https://www.youtube.com/watch?v=SvejDH5-Hic - AuthorName: CATALYST - AuthorURL: https://catalyst.earth - Services: - Tools & Applications: - - Title: EODMS RAPI Client Python Script - URL: https://github.com/nrcan-eodms-sgdot-rncan/eodms-rapi-orderdownload - AuthorName: Earth Observation Data Management System - Natural Resources Canada - AuthorURL: https://www.eodms-sgdot.nrcan-rncan.gc.ca/index-en.html - - Title: RADARSTAC - URL: https://www.radarstac.com - AuthorName: Sparkgeo Consulting Inc. - AuthorURL: https://sparkgeo.com/ + - Title: RADARSAT-1 Processing Service | Service de traitement RADARSAT-1 + URL: https://github.com/eodms-sgdot/radarsat-notebooks/blob/main/examples/radarsat1_l1_processing.ipynb + AuthorName: Canada Centre for Remote Sensing | Centre canadien de télédétection + AuthorURL: https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing + Tools & Applications: - Title: QGIS URL: https://qgis.org AuthorName: German QGIS user group diff --git a/datasets/radiant.yaml b/datasets/radiant.yaml new file mode 100644 index 000000000..d2e351598 --- /dev/null +++ b/datasets/radiant.yaml @@ -0,0 +1,85 @@ +Name: RADIANT Public Data +Description: > + The Real-time Analysis and Discovery in Integrated And Networked Technologies (RADIANT) + initiative seeks to develop an extensible, federated framework for rapid exchange of + multimodal clinical and research data on behalf of accelerated discovery and patient impact. + Coordination and implementation of initial RADIANT deployments will leverage a network of + more than 35 partnered health care systems and participating patient families within the + Children’s Brain Tumor Network (CBTN) and the Pediatric Neuro-Oncology Consortium (PNOC). + This data set is composed of public multi-modal data provisioned by RADIANT. The initial + bolus of data is from CBTN and consists of clinical data extracted/abstracted from + electronic medical records, omic data such as genomics, transcriptomics and proteomics and + radiology and pathology imaging data. Data are collected or generated as part of consent-based, + IRB-approved observational or interventional studies with the goal of making it available + globally to researchers across a broad number of disciplines. +Documentation: https://cbtn.org/research-resources +Contact: research@cbtn.org +ManagedBy: "[The Center for Data-Driven Discovery in Biomedicine (D3b) at the Children's Hospital of Philadelphia](https://d3b.center/)" +UpdateFrequency: | + Data is updated on a regular basis by the RADIANT teams to make data available as + rapidly as possible. +Tags: + - aws-pds + - life sciences + - cancer + - genetic + - genomic + - transcriptomics + - medical imaging + - radiology + - Homo sapiens + - pediatric + - whole genome sequencing +License: "NIH Genomic Data Sharing Policy: https://grants.nih.gov/grants/guide/notice-files/not-od-14-124.html" +Resources: +- Description: "Children's Brain Tumor Network" + ARN: arn:aws:s3:::opendata-chop-study-us-east-1-prd-sd-bhjxbdqk + Region: us-east-1 + Type: S3 Bucket + ControlledAccess: https://cbtn.org/ +DataAtWork: + Tools & Applications: + - Title: RADIANT Source Code + URL: https://github.com/radiant-network + AuthorName: RADIANT Team + AuthorURL: https://github.com/radiant-network + - Title: CAVATICA + URL: http://cavatica.org + AuthorName: Seven Bridges Genomics + AuthorURL: http://www.sevenbridges.com + - Title: PedcBioPortal + URL: https://pedcbioportal.kidsfirstdrc.org + AuthorName: cBioPortal + AuthorURL: https://www.cbioportal.org/ + - Title: Flywheel (CHOP D3b) + URL: https://chop.flywheel.io + AuthorName: Flywheel + AuthorURL: https://flywheel.io/ + Publications: + - Title: "The children's brain tumor network (CBTN) - Accelerating research in pediatric central nervous system tumors through collaboration and open science." + URL: https://pubmed.ncbi.nlm.nih.gov/36335802/ + AuthorName: Jena V Lilly, Jo Lynne Rokita, Jennifer L Mason, et al. + - Title: "The landscape of primary mismatch repair deficient gliomas in children, adolescents, and young adults: a multi-cohort study" + URL: https://pubmed.ncbi.nlm.nih.gov/39701117/ + AuthorName: Logine Negm, Jiil Chung, Liana Nobre, et al. + - Title: "Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma" + URL: https://pubmed.ncbi.nlm.nih.gov/39747214/ + AuthorName: Anahita Gathi Kazerooni, Adam Kraya, Komal S Rathi, Meen Chul Kim, et al. + - Title: "Multi-scale signaling and tumor evolution in high-grade gliomas" + URL: https://pubmed.ncbi.nlm.nih.gov/38981438/ + AuthorName: Jingxian Liu, Song Cao, Kathleen J Imback, et al. + - Title: "Germline analysis of an international cohort of pediatric diffuse midline glioma patients" + URL: https://pubmed.ncbi.nlm.nih.gov/40072012/ + AuthorName: Marion K Mateos, Pamela Ajuyah, Noemi Fuentes-Bolanos, et al. + - Title: "A road map for the treatment of pediatric diffuse midline glioma" + URL: https://pubmed.ncbi.nlm.nih.gov/38039965/ + AuthorName: Carl Koschmann, Wajd N Al-Holou, Marta M Alonso, et al. + - Title: "Use of External Control Cohorts in Pediatric Brain Tumor Clinical Trials" + URL: https://pubmed.ncbi.nlm.nih.gov/38394473/ + AuthorName: Ashley S Margol, Annette M Molinaro, Arzu Onar-Thomas, et al. + - Title: "OpenPBTA: The Open Pediatric Brain Tumor Atlas" + URL: https://pubmed.ncbi.nlm.nih.gov/37492101/ + AuthorName: Joshua A Shapiro, Krutika S Gaonkar, Stephanie J Spielman, et al. + - Title: "Generation and multi-dimensional profiling of a childhood cancer cell line atlas defines new therapeutic opportunities" + URL: https://pubmed.ncbi.nlm.nih.gov/37001527/ + AuthorName: Claire Xin Sun, Paul Daniel, Gabrielle Bradshaw et al. diff --git a/datasets/rcm-ceos-ard.yaml b/datasets/rcm-ceos-ard.yaml index 6584d6946..e4f58a89c 100644 --- a/datasets/rcm-ceos-ard.yaml +++ b/datasets/rcm-ceos-ard.yaml @@ -14,6 +14,10 @@ UpdateFrequency: "The initial dataset will be Canada-wide, 30M Compact-Polarizat
L'ensemble de données initial couvrira l'ensemble du Canada, une couverture standard de 30 metres de polarisation compacte, tous les 12 jours, par fréquence de revisite de mission." +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - agriculture @@ -38,14 +42,19 @@ Resources: Region: ca-central-1 Type: S3 Bucket Explore: - - '[EODMS STAC for RCM CEOS ARD](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/collections/rcm-ard/items/)' + - '[STAC for RCM CEOS ARD products](https://radiantearth.github.io/stac-browser/#/external/www.eodms-sgdot.nrcan-rncan.gc.ca/stac/collections/rcm-ard?.language=en)' DataAtWork: + Tutorials: + - Title: Workflows for accessing and manipulating RCM ARD SpatioTemporal Asset Catalog (STAC) in JupyterLab Python Notebooks - Flux de travail pour accéder et manipuler le catalogue d'actifs spatio-temporels (STAC) RCM ARD dans les notebooks Python JupyterLab + URL: https://github.com/eodms-sgdot/radarsat-notebooks + AuthorName: Canada Centre for Remote Sensing | Centre canadien de télédétection + AuthorURL: https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing Publications: - Title: Synthetic Aperture Radar (CEOS-ARD SAR) URL: https://ceos.org/ard/files/PFS/SAR/v1.1/CEOS-ARD_PFS_Synthetic_Aperture_Radar_v1.1.pdf - AuthorName: Committee on Earth Observation Satellites (CEOS) for developing the CEOS ARD Standards. Specific acknowledgement to François Charbonneau (NRCan) for contributions to the standard development through CEOS committee membership as well as application to Canadian RADARSAT data. - AuthorURL: - - Title: CEOS Analysis Ready Data + AuthorName: Committee on Earth Observation Satellites (CEOS) for developing the CEOS ARD Standards. Specific acknowledgement to François Charbonneau (NRCan) for contributions to the standard development through CEOS committee membership as well as application to Canadian RADARSAT data. - Comité sur les satellites d'observation de la Terre (CEOS) pour l'élaboration des normes CEOS ARD. Remerciements particuliers à François Charbonneau (RNCan) pour ses contributions au développement des normes par le biais de son appartenance au comité CEOS ainsi que pour l'application aux données canadiennes RADARSAT. + AuthorURL: https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing + - Title: CEOS Analysis Ready Data - Données prêtes à l'analyse du CEOS URL: https://ceos.org/ard/ AuthorName: Committee on Earth Observation Satellites (CEOS) AuthorURL: https://ceos.org/ @@ -56,4 +65,8 @@ DataAtWork: - Title: Copernicus Global Digital Elevation Model URL: https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM AuthorName: European Space Agency (ESA) - AuthorURL: https://www.esa.int/ \ No newline at end of file + AuthorURL: https://www.esa.int/ + - Title: RCM CEOS ARD Dataset on GEO.ca | Ensemble de données RCM CEOS ARD sur GEO.ca + URL: https://app.geo.ca/en-ca/map-browser/record/eodms-rcm-ard + AuthorName: Canada Centre for Remote Sensing | Centre canadien de télédétection + AuthorURL: https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing diff --git a/datasets/real-changesets.yaml b/datasets/real-changesets.yaml index f95ea51e2..b72fc1aae 100644 --- a/datasets/real-changesets.yaml +++ b/datasets/real-changesets.yaml @@ -7,6 +7,10 @@ Documentation: https://github.com/osmus/osmcha-charter-project/blob/main/real-ch Contact: team@openstreetmap.us ManagedBy: OpenStreetMap US UpdateFrequency: Minutely +Collabs: + ASDI: + Tags: + - disaster response Tags: - geospatial - osm diff --git a/datasets/roa.yaml b/datasets/roa.yaml new file mode 100644 index 000000000..7381162c0 --- /dev/null +++ b/datasets/roa.yaml @@ -0,0 +1,53 @@ +Name: Rain over Africa +Description: The Rain over Africa (RoA) dataset consists of spaceborn estimates of precipitation of Rain over Africa using only geostationary imagery and obtained through a convolutional and quantile regression neural network. The dataset also contains some uncertainty estimates. +Documentation: https://github.com/SEE-GEO/roa +Contact: https://github.com/SEE-GEO/roa +ManagedBy: "[Geoscience and Remote Sensing at Chalmers University of Technology](https://www.chalmers.se/en/departments/see/research/geo)" +UpdateFrequency: At most, yearly +Tags: + - aws-pds + - agriculture + - analysis ready data + - atmosphere + - aws-pds + - climate + - deep learning + - earth observation + - geophysics + - geoscience + - hydrology + - machine learning + - precipitation + - satellite imagery + - weather + - zarr +License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)" +Citation: "Please refer to https://github.com/SEE-GEO/roa#5-how-to-cite for instructions on how to cite the RoA data." +Resources: + - Description: RoA expected rain rate and quantiles at levels 5%, 16%, 25%, 50%, 75%, 84%, and 95% in Zarr format + ARN: arn:aws:s3:::rainoverafrica + Region: us-west-2 + Type: S3 Bucket + - Description: Notifications for new Rain over Africa data + ARN: arn:aws:sns:us-west-2:261854712492:rainoverafrica-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Reading RoA data + URL: https://github.com/SEE-GEO/roa?tab=readme-ov-file#22-reading-roa-data + AuthorName: Adrià Amell + Services: + - Amazon S3 + - Title: How to use the data + URL: https://github.com/SEE-GEO/roa?tab=readme-ov-file#3-how-to-use-the-data + AuthorName: Adrià Amell + Services: + - Amazon S3 + Publications: + - Title: Probabilistic near real-time retrievals of Rain over Africa using deep learning + URL: https://doi.org/10.1029/2025JD044595 + AuthorName: Adrià Amell, Lilian Hee, Simon Pfreundschuh, and Patrick Eriksson +DeprecatedNotice: +ADXCategories: + - Environmental Data diff --git a/datasets/roadmapepigenomics.yaml b/datasets/roadmapepigenomics.yaml index 9f0907537..f9dbe7511 100644 --- a/datasets/roadmapepigenomics.yaml +++ b/datasets/roadmapepigenomics.yaml @@ -1,6 +1,6 @@ Name: NIH Roadmap Epigenomics Description: | - The NIH Roadmap Epigenomics Mapping Consortium was launched with the goal of producing a public resource of human epigenomic data to catalyze basic biology and disease-oriented research. The project has generated high-quality, genome-wide maps of several key histone modifications, chromatin accessibility, DNA methylation and mRNA expression across 100s of human cell types and tissues. + The NIH Roadmap Epigenomics Mapping Consortium was launched with the goal of producing a public resource of human epigenomic data to catalyze basic biology and disease-oriented research. The project has generated high-quality, genome-wide maps of several key histone modifications, chromatin accessibility, DNA methylation and mRNA expression across 100s of human cell types and tissues. To see what data is available, please check the directory listing: https://roadmapepigenomics.s3.us-west-2.amazonaws.com/index.html. Contact: dli23@wustl.edu ManagedBy: NIH Roadmap Epigenomics Mapping Consortium, Ting Wang Lab at WashU (https://wang.wustl.edu/) Documentation: https://egg2.wustl.edu/roadmap/web_portal/ @@ -25,8 +25,8 @@ DataAtWork: URL: https://egg2.wustl.edu/roadmap/web_portal/ AuthorName: Anshul Kundaje Lab AuthorURL: https://kundajelab.github.io/ - - Title: Visualize TaRGET data with WashU Epigenome Browser - URL: https://epigenomegateway.wustl.edu/browser/ + - Title: Visualize Roadmp data with WashU Epigenome Browser + URL: https://epigenomegateway.wustl.edu/browser/?genome=hg19&hub=https://vizhub.wustl.edu/public/hg19/new/roadmap9_methylC.md AuthorName: WashU Epigenome Browser AuthorURL: https://epigenomegateway.wustl.edu/browser/ Publications: diff --git a/datasets/rsna-intracranial-aneurysm-detection-dataset.yaml b/datasets/rsna-intracranial-aneurysm-detection-dataset.yaml new file mode 100644 index 000000000..1628878f8 --- /dev/null +++ b/datasets/rsna-intracranial-aneurysm-detection-dataset.yaml @@ -0,0 +1,29 @@ +Name: RSNA Intracranial Aneurysm Detection Dataset (RSNA-ICA) +Description: "The Radiological Society of North America Intracranial Aneurysm Detection (RSNA-ICA) dataset is a collection of over 4,000 CT brain scans annotated by a cohort of over 40 volunteer radiologists from RSNA and the American Society of Neuroradiology to show the presence and location of intracranial aneurysms. It also includes a set of about 200 imaging studies that are annotated with AI-generated segmentations highlighting abnormalities. The imaging data was provided by 18 institutions. Initially compiled in 2025 for the RSNA Intracranial Aneurysm Detection AI Challenge hosted on Kaggle competition platform (https://www.kaggle.com/competitions/rsna-intracranial-aneurysm-detection), it represents the largest publicly available collection of its kind. Additional information on the dataset and how to make use of it is provided in a forthcoming Data Resource Publication listed below, as well as on the Kaggle competition website, which also provides access to models developed during the competition." +Documentation: https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Intracranial-Aneurysm-Detection-Dataset +Contact: informatics@rsna.org +ManagedBy: 'Radiological Society of North America (https://www.rsna.org/)' +UpdateFrequency: The dataset may be updated with additional or corrected data on a need-to-update basis. +Tags: + - aws-pds + - radiology + - medical imaging + - medical image computing + - machine learning + - computer vision + - csv + - labeled + - life sciences +License: "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. If you share or re-distribute the data in any form, include a citation to the “Radiological Society of North America Intracranial Aneurysm Detection (RSNA-ICA) Dataset, July 2025” [https://doi.org/10.1148/dataset.ica.2025]." +Resources: + - Description: Zip archive containing DCM and CSV files + ARN: arn:aws:s3:::intracranial-aneurysm-detection + Region: us-west-2 + Type: S3 Bucket + ControlledAccess: https://mira.rsna.org/dataset/7 +DataAtWork: + Publications: + - Title: The RSNA Intercranial Aneurysm Detection Dataset + URL: https://pubs.rsna.org/doi/full/10.1148/ryai.2021200254 + AuthorName: Authors, Various + diff --git a/datasets/rsna-intracranial-hemorrhage-detection.yaml b/datasets/rsna-intracranial-hemorrhage-detection.yaml index e37a5ddc9..6d4b75d31 100644 --- a/datasets/rsna-intracranial-hemorrhage-detection.yaml +++ b/datasets/rsna-intracranial-hemorrhage-detection.yaml @@ -22,7 +22,7 @@ Resources: ARN: arn:aws:s3:::intracranial-hemorrhage Region: us-west-2 Type: S3 Bucket - ControlledAccess: https://mira.rsna.org/dataset/2 + ControlledAccess: https://mira.rsna.org/dataset/1 DataAtWork: Publications: - Title: "Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge" diff --git a/datasets/rsna-lumbar-spine-degenerative-classification-dataset.yaml b/datasets/rsna-lumbar-spine-degenerative-classification-dataset.yaml new file mode 100644 index 000000000..7bdafb478 --- /dev/null +++ b/datasets/rsna-lumbar-spine-degenerative-classification-dataset.yaml @@ -0,0 +1,28 @@ +Name: RSNA Lumbar Spine Degenerative Classification Dataset (RSNA-LSDD) +Description: "The Radiological Society of North America Lumbar Spine Degenerative Classification dataset (RSNA-LSDD) is a collection of over 2,600 magnetic resonance imaging (MR) scans of the lumbar spine annotated by a cohort of about 60 volunteer radiologists recruited by the RSNA, the American Society for Spine Radiology and the American Society of Neuroradiology to identify the location and severity of five degenerative conditions across the five intervertebral disc levels (L1/L2, L2/L3, L3/L4, L4/L5, and L5/S1). The imaging data, comprising over 8,500 image series (Sagittal “T2”, Axial T2 and Sagittal T1), was provided by twelve institutions from across the globe. Initially compiled in 2024 for the RSNA Lumbar Spine Degenerative Classification AI Challenge hosted on Kaggle competition platform (https://www.kaggle.com/competitions/rsna-2024-lumbar-spine-degenerative-classification), it represents the largest publicly available collection of its kind. Additional information on the dataset and how to make use of it is provided in the Data Resource Publication listed below, as well as on the Kaggle competition website, which also provides access to models developed during the competition." +Documentation: https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Lumbar-Spine-Degenerative-Classification-Dataset +Contact: informatics@rsna.org +ManagedBy: 'Radiological Society of North America (https://www.rsna.org/)' +UpdateFrequency: The dataset may be updated with additional or corrected data on a need-to-update basis. +Tags: + - aws-pds + - radiology + - medical imaging + - medical image computing + - machine learning + - computer vision + - csv + - labeled + - life sciences +License: "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. If you share or re-distribute the data in any form, include a citation to the “Radiological Society of North America Screening Mammography Breast Cancer Detection (RSNA-SMBC) Dataset, November 2022” [https://doi.org/10.1148/dataset.smbc.2024]." +Resources: + - Description: Zip archive containing DCM and CSV files + ARN: arn:aws:s3:::lumbar-spine-degenerative-classification + Region: us-west-2 + Type: S3 Bucket + ControlledAccess: https://mira.rsna.org/dataset/6 +DataAtWork: + Publications: + - Title: The RSNA Lumbar Spine Degenerative Classification Dataset + URL: https://pubs.rsna.org/doi/full/10.1148/ryai.2021200254 + AuthorName: Authors, Various diff --git a/datasets/rsna-pulmonary-embolism-detection.yaml b/datasets/rsna-pulmonary-embolism-detection.yaml index ca6a28ce3..184c9c8d4 100644 --- a/datasets/rsna-pulmonary-embolism-detection.yaml +++ b/datasets/rsna-pulmonary-embolism-detection.yaml @@ -22,7 +22,7 @@ Resources: ARN: arn:aws:s3:::pulmonary-embolism-detection Region: us-west-2 Type: S3 Bucket - ControlledAccess: https://mira.rsna.org/dataset/1 + ControlledAccess: https://mira.rsna.org/dataset/2 DataAtWork: Publications: - Title: The RSNA Pulmonary Embolism CT Dataset diff --git a/datasets/rsna-ratic.yaml b/datasets/rsna-ratic.yaml new file mode 100644 index 000000000..ce59c398c --- /dev/null +++ b/datasets/rsna-ratic.yaml @@ -0,0 +1,30 @@ +Name: RSNA Abdominal Traumatic Injury CT (RATIC) +Description: "Blunt force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are key to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically by physical exam, patient symptoms, or laboratory tests. Prompt diagnosis of abdominal trauma using medical imaging is thus critical to patient care. AI tools that assist and expedite diagnosis of abdominal trauma have the potential to substantially improve patient care and health outcomes in the emergency setting. To create the ground truth dataset, RSNA collected imaging data sourced from 23 sites in 14 countries on six continents, including more than 4,000 CT exams with various abdominal injuries and a roughly equal number of cases without injury." +Documentation: https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Abdominal-Traumatic-Injury-CT +Contact: informatics@rsna.org +ManagedBy: 'Radiological Society of North America (https://www.rsna.org/)' +UpdateFrequency: The dataset may be updated with additional or corrected data on a need-to-update basis. +Tags: + - aws-pds + - radiology + - medical imaging + - medical image computing + - machine learning + - computer vision + - csv + - labeled + - computed tomography + - x-ray tomography + - life sciences +License: "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. If you share or re-distribute the data in any form, include a citation to the “Brain CT Hemorrhage Dataset, Copyright RSNA, 2019” as follows: Flanders AF, et al. The RSNA Brain CT Hemorrhage Dataset [10.1148/ryai.2020190211]. Radiology: Artificial Intelligence 2020;2:3." +Resources: + - Description: Zip archive containing DCM and CSV files + ARN: arn:aws:s3:::abdominal-trauma-detection + Region: us-west-2 + Type: S3 Bucket + ControlledAccess: https://mira.rsna.org/dataset/5 +DataAtWork: + Publications: + - Title: The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset + AuthorName: Rudie, Jeffrey D. + URL: https://doi.org/10.48550/arXiv.2405.19595 diff --git a/datasets/s1-orbits.yaml b/datasets/s1-orbits.yaml index ce619203a..3b968d4ff 100644 --- a/datasets/s1-orbits.yaml +++ b/datasets/s1-orbits.yaml @@ -14,6 +14,10 @@ Contact: https://asf.alaska.edu/asf/contact-us/ ManagedBy: "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)" UpdateFrequency: > Updated as new data becomes available on the [Copernicus Data Space Ecosystem](https://documentation.dataspace.copernicus.eu/Data/ComplementaryData/Additional.html#sentinel-1-orbits). Typically AUX_POEORB files are published daily and AUX_RESORB files are published every other hour. +Collabs: + ASDI: + Tags: + - disaster response Tags: - auxiliary data - disaster response diff --git a/datasets/salk-aging-mouse-brain-epigeneti.yaml b/datasets/salk-aging-mouse-brain-epigeneti.yaml new file mode 100644 index 000000000..efb5ef760 --- /dev/null +++ b/datasets/salk-aging-mouse-brain-epigeneti.yaml @@ -0,0 +1,33 @@ +Name: Aging Mouse Brain Epigenetic +Description: "Aging is a major risk factor for neurodegenerative diseases, yet underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types." +Contact: ecker@salk.edu +Documentation: https://doi.org/10.1101/2025.04.21.648266 +ManagedBy: "[Salk Institute](http://www.salk.edu)" +UpdateFrequency: Never +Tags: + - life sciences + - genetic + - genomic + - whole genome sequencing + - whole exome sequencing + - transcriptomics + - fastq + - bam + - cram + - aws-pds +License: "[NCBI Policy](https://www.ncbi.nlm.nih.gov/home/about/policies/) and [NIH Genomic Data Sharing Policy ](https://osp.od.nih.gov/scientific-sharing/genomic-data-sharing/)" +Resources: + - Description: Aging mouse brain epigenomics dataset + ARN: arn:aws:s3:::salk-aging-mouse-brain-epigenetics + Region: us-west-2 + Type: S3 Bucket + - Description: Dataset update notifications + ARN: arn:aws:sns:us-west-2:855738613743:salk-aging-mouse-brain-epigenetics-updates + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Publications: + - Title: Cell-type-specific transposable element demethylation and TAD remodeling in the aging mouse brain + URL: https://doi.org/10.1101/2025.04.21.648266 + AuthorName: Zeng, Q., Wei, T., Klein, A., Bartlett, A., Liu, H., Nery, J.R., Castanon, R., Osteen, J., Johnson, N.D., Wang, W., Ding, W., Chen, H., Altshul, J., Kenworthy, M., Valadon, C., Owens, W., Wu, Z., Amaral, M.L., Song, Báez-Becerra, T.a.t.i.a.n.a., Cho, S., Chen, C., Willier, J., Cao, S., Rink, J., Lee, J., Barcoma, A., Arzavala, J., Emerson, N., Lu, Y.R., Ren, B., Behrens, M.a.r.g.a.r.i.t.a., Ecker, J.R. + AuthorURL: https://www.salk.edu/scientist/joseph-ecker/ diff --git a/datasets/satellogic-earthview.yaml b/datasets/satellogic-earthview.yaml index 8ea995f6a..253901501 100644 --- a/datasets/satellogic-earthview.yaml +++ b/datasets/satellogic-earthview.yaml @@ -4,6 +4,10 @@ Documentation: https://satellogic-earthview.s3.us-west-2.amazonaws.com/index.htm Contact: https://www.satellogic.com/ ManagedBy: "[Satellogic](https://www.satellogic.com)" UpdateFrequency: New data will be made available periodically, with annual updates expected in the future covering the same or other new regions. +Collabs: + ASDI: + Tags: + - satellite imagery Tags: - aws-pds - satellite imagery diff --git a/datasets/seefar.yaml b/datasets/seefar.yaml index 5407ba92a..626e9880d 100644 --- a/datasets/seefar.yaml +++ b/datasets/seefar.yaml @@ -4,6 +4,10 @@ Documentation: https://coastalcarbon.ai/seefar Contact: James Lowman ManagedBy: Coastal Carbon UpdateFrequency: Yearly +Collabs: + ASDI: + Tags: + - climate Tags: - geospatial - earth observation diff --git a/datasets/sentinel-2-l2a-cogs.yaml b/datasets/sentinel-2-l2a-cogs.yaml index b9f117e0c..8065946a3 100644 --- a/datasets/sentinel-2-l2a-cogs.yaml +++ b/datasets/sentinel-2-l2a-cogs.yaml @@ -110,6 +110,12 @@ DataAtWork: AuthorName: Louise Liddell Services: - Amazon SageMaker Studio Lab + - Title: Monitoring of methane (CH4) emission point sources on AWS + URL: https://github.com/aws-samples/aws-opendata-samples/blob/main/notebooks/aws-methane-emissions-monitor/monitor_methane_ch4_emission_point_sources.ipynb + AuthorName: Janosch Woschitz, Karsten Schroer + Services: + - Amazon SageMaker + - Amazon S3 Publications: - Title: STAC and Sentinel-2 COGs (ESIP Summer Meeting 2020) URL: https://docs.google.com/presentation/d/14NsKFZ3UF2Swwx_9L7sPMX9ccFUK1ruQyZXWK9Cz4L4/edit?usp=sharing diff --git a/datasets/sentinel-2.yaml b/datasets/sentinel-2.yaml index dd4f528e0..5743b956b 100644 --- a/datasets/sentinel-2.yaml +++ b/datasets/sentinel-2.yaml @@ -163,6 +163,10 @@ DataAtWork: URL: https://map.onesoil.ai/ AuthorName: OneSoil AuthorURL: https://onesoil.ai/ + - Title: Pera Portal + URL: https://portal.geopera.com/ + AuthorName: Geopera + AuthorURL: https://geopera.com/ Publications: - Title: Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring URL: https://ieeexplore.ieee.org/abstract/document/9165649 @@ -170,4 +174,3 @@ DataAtWork: - Title: "Coral-spawn slicks: Reflectance spectra and detection using optical satellite data" URL: https://www.sciencedirect.com/science/article/pii/S0034425720304284 AuthorName: Hiroya Yamano, Asahi Sakuma, Saki Harii - diff --git a/datasets/sentinel-products-ca-mirror.yaml b/datasets/sentinel-products-ca-mirror.yaml index 8cc477f10..a14da8007 100644 --- a/datasets/sentinel-products-ca-mirror.yaml +++ b/datasets/sentinel-products-ca-mirror.yaml @@ -14,6 +14,10 @@ UpdateFrequency: "Sentinel-1 is an NRT dataset retrieved from ESA within 90 minu
Sentinel-1 est un ensemble de données NRT récupéré de l'ESA dans les 90 minutes suivant la liaison descendante du satellite. Sentinel-2 et Sentinel-3 non NRT sont également récupérés le plus rapidement possible en fonction de la couverture du Canada et de la disponibilité à la source." +Collabs: + ASDI: + Tags: + - satellite imagery Tags: - aws-pds - agriculture @@ -31,5 +35,5 @@ Resources: Region: ca-central-1 Type: S3 Bucket Explore: - - '[EODMS STAC for Sentinel products](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/)' + - '[STAC for Sentinel products](https://radiantearth.github.io/stac-browser/#/external/www.eodms-sgdot.nrcan-rncan.gc.ca/stac/collections/sentinel-1)' diff --git a/datasets/smaht.yaml b/datasets/smaht.yaml new file mode 100644 index 000000000..e80aa7f83 --- /dev/null +++ b/datasets/smaht.yaml @@ -0,0 +1,90 @@ +Name: Somatic Mosaicism across Human Tissues (SMaHT) +Description: | + The Somatic Mosaicism across Human Tissues (SMaHT) project is an NIH Common + Fund consortium (2023-) aimed to comprehensively characterize somatic variation + ("mosaicism") in normal human tissues. While most genetic studies have relied + on blood-derived DNA, SMaHT captures the full spectrum of DNA variation across + cell types, tissues, and organs from phenotypically normal individuals to + better understand the role of somatic mosaicism in human development, aging, + and disease progression. + + Researchers in the consortium develop and apply experimental and computational + methods, paired with the state-of-the-art sequencing technologies, to accurately + detect even rare mutations (frequency < 1%) in subpopulations of cells. In + addition to generating the production data across ~20 tissue types from 150 + post-mortem donors, SMaHT also produces datasets from cell line and tissue + homogenate samples, to benchmark and develop new technologies and computational + tools for mosaic variant detection. + + The resulting data include high-coverage whole-genome and transcriptome data + using both short-read and long-read sequencing technologies from multiple platforms + (e.g., Illumina, PacBio, Oxford Nanopore Technologies, Ultima Genomics). SMaHT will + also generate comprehensive genome-wide catalogs of somatic variants. We anticipate + that this resource will be valuable not only for researchers studying somatic + mosaicism, but also for the broader scientific community interested in large-scale + WGS data from normal human tissues. More about the SMaHT project: + program announcement, https://commonfund.nih.gov/smaht, and https://smaht.org/. + More about the data portal: https://data.smaht.org/ and types of data generated: + https://data.smaht.org/about/consortium/data +Documentation: https://data.smaht.org/docs +Contact: smhelp@hms-dbmi.atlassian.net +ManagedBy: SMaHT Data Analysis Center (DAC) +UpdateFrequency: Bi-annually +Tags: + - biology + - bioinformatics + - genetic + - genomic + - imaging + - life sciences + - whole genome sequencing + - bam + - aws-pds +License: NIH Genomic Data Sharing Policy - https://gdc.cancer.gov/access-data/data-access-policies +Citation: The SMaHT datasets were generated as part of the NIH Common Fund consortium initiative, + Somatic Mosaicism across Human Tissues (SMaHT). The SMaHT datasets are submitted under dbGaP + studies (http://www.ncbi.nlm.nih.gov/gap), with the study accession numbers, phs004193 for the + SMaHT Benchmarking data and phs004194 for the SMaHT Production data. The datasets were provided + by the SMaHT Data Analysis Center (DAC) [1UM1DA058230] on behalf of the SMaHT network. More + information about the SMaHT Network is available online at https://smaht.org/, about the SMaHT + Data Portal at https://data.smaht.org/ , and types of data generated by the Network at + https://data.smaht.org/about/consortium/data +Resources: + - Description: | + SMaHT Open-Access Data - Publicly available data files without restriction, including + aligned reads from WGS and RNA-Seq, as well as variants identified from cell line + samples that are commercially available without restriction. Somatic (non-inherited) + variants from donor tissue samples are also open-access data. + ARN: arn:aws:s3:::smaht-open-data-public + Region: us-east-1 + Type: S3 Bucket + - Description: | + SMaHT Controlled Access Data - Controlled-access data files, including aligned reads + from WGS and RNA-Seq, as well as germline (inherited) from donor tissue samples. + Access to these data is managed through dbGaP. + ARN: arn:aws:s3:::smaht-open-data-protected + Region: us-east-1 + Type: S3 Bucket + ControlledAccess: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs004194 + - Description: > + Amazon SNS topic that publishes notifications when public access data is added for this dataset. + ARN: arn:aws:sns:us-east-1:874962955096:smaht-open-data-public-object_created + Region: us-east-1 + Type: SNS Topic + - Description: | + Amazon SNS topic that publishes notifications when new controlled access data is added for this dataset. + ARN: arn:aws:sns:us-east-1:874962955096:smaht-open-data-protected-object_created + Region: us-east-1 + Type: SNS Topic +DataAtWork: + Tools & Applications: + - Title: Somatic Mosaicism across Human Tissues Data Portal + URL: https://data.smaht.org/ + AuthorName: SMaHT Data Analysis Center (DAC) + Publications: + - Title: The Somatic Mosaicism across Human Tissues Network + URL: https://www.nature.com/articles/s41586-025-09096-7 + AuthorName: Coorens T, Oh J, Choi Y, Lim N, Zhao B, Voshall A et al. +ADXCategories: + - Healthcare & Life Sciences Data + diff --git a/datasets/snpeff.yaml b/datasets/snpeff.yaml new file mode 100644 index 000000000..74e1a78ef --- /dev/null +++ b/datasets/snpeff.yaml @@ -0,0 +1,59 @@ +Name: SnpEff & SnpSift Genomic Variant Annotation Databases +Description: "SnpEff is a variant annotation and effect prediction tool that annotates and predicts the effects of genetic variants on genes and proteins (such as amino acid changes). It supports over 38,000 genomes and provides comprehensive genomic databases for variant annotation. The databases include reference genomes, gene annotations, protein sequences, and regulatory elements from trusted sources like ENSEMBL, RefSeq, and UCSC. SnpSift complements SnpEff by providing tools to annotate genomic variants using databases, filter large genomic datasets, and manipulate annotated variants. Together, these tools provide a complete solution for genomic variant analysis, supporting research in human genetics, cancer genomics, pharmacogenomics, and model organism studies." +Contact: Pablo Cingolani +Documentation: https://pcingola.github.io/SnpEff/ +ManagedBy: "[Pablo Cingolani](http://www.linkedin.com/in/pablocingolani)" +Resources: + - Description: SnpEff databases for genomic variant annotation + ARN: arn:aws:s3:::snpeff-public + Region: us-east-2 + Type: S3 Bucket +UpdateFrequency: Monthly +Tags: + - life sciences + - genomic + - variant annotation + - bioinformatics + - genetic + - genome + - cancer + - protein + - vcf + - whole genome sequencing + - whole exome sequencing + - transcriptomics + - structural variation + - aws-pds +License: "[MIT License](https://opensource.org/licenses/MIT)" +DataAtWork: + Tutorials: + - Title: SnpEff Documentation + URL: https://pcingola.github.io/SnpEff/ + AuthorName: Pablo Cingolani + AuthorURL: http://www.linkedin.com/in/pablocingolani + - Title: SnpEff Introduction and Quick Start + URL: https://pcingola.github.io/SnpEff/snpeff/introduction/ + AuthorName: SnpEff Project + AuthorURL: https://pcingola.github.io/SnpEff/ + - Title: Building Custom SnpEff Databases + URL: https://pcingola.github.io/SnpEff/snpeff/build_db/ + AuthorName: SnpEff Project + AuthorURL: https://pcingola.github.io/SnpEff/ + Tools & Applications: + - Title: SnpEff + URL: https://github.com/pcingola/SnpEff + AuthorName: Pablo Cingolani + AuthorURL: http://www.linkedin.com/in/pablocingolani + - Title: SnpSift + URL: https://github.com/pcingola/SnpSift + AuthorName: Pablo Cingolani + AuthorURL: http://www.linkedin.com/in/pablocingolani + Publications: + - Title: "A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3" + URL: https://www.ncbi.nlm.nih.gov/pubmed/22728672 + AuthorName: Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM + AuthorURL: https://www.ncbi.nlm.nih.gov/pubmed/?term=Cingolani%20P%5BAuthor%5D&cauthor=true&cauthor_uid=22728672 + - Title: "Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift" + URL: https://www.frontiersin.org/articles/10.3389/fgene.2012.00035/full + AuthorName: Cingolani P, Patel VM, Coon M, Nguyen T, Land SJ, Ruden DM, Lu X + AuthorURL: https://www.frontiersin.org/people/u/4691 diff --git a/datasets/sofar-spotter-archive.yaml b/datasets/sofar-spotter-archive.yaml index a904b1757..2102c5622 100644 --- a/datasets/sofar-spotter-archive.yaml +++ b/datasets/sofar-spotter-archive.yaml @@ -5,6 +5,10 @@ Documentation: "[Spotter Technical Reference Manual](https://content.sofarocean. Contact: opendata@sofarocean.com ManagedBy: "[Sofar Ocean](https://www.sofarocean.com/company/contact-us)" UpdateFrequency: As available +Collabs: + ASDI: + Tags: + - oceans Tags: - aws-pds - climate diff --git a/datasets/software-heritage.yaml b/datasets/software-heritage.yaml index 2fa120d41..27c17f867 100644 --- a/datasets/software-heritage.yaml +++ b/datasets/software-heritage.yaml @@ -14,7 +14,7 @@ Description: | information is also included, providing timestamps about when and where all archived source code artifacts have been observed in the wild. Author and committer information is anonymized. -Documentation: https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html +Documentation: https://docs.softwareheritage.org/devel/swh-export/graph/athena.html Contact: aws@softwareheritage.org ManagedBy: Software Heritage UpdateFrequency: Data is updated yearly @@ -48,11 +48,11 @@ Resources: DataAtWork: Tutorials: - Title: Using the Software Heritage Graph Dataset - URL: https://docs.softwareheritage.org/devel/swh-dataset/graph/index.html + URL: https://docs.softwareheritage.org/devel/swh-export/graph/ AuthorName: The Software Heritage team Tools & Applications: - Title: The SWH-Graph module - URL: https://docs.softwareheritage.org/devel/swh-graph/index.html + URL: https://docs.softwareheritage.org/devel/swh-graph/ AuthorName: The Software Heritage team Publications: - Title: The Software Heritage Graph Dataset diff --git a/datasets/sparc.yaml b/datasets/sparc.yaml index ecd10cdb9..fb3b83c4f 100644 --- a/datasets/sparc.yaml +++ b/datasets/sparc.yaml @@ -10,7 +10,7 @@ Description: | of anatomical and functional connectivity of the nervous system. Documentation: https://docs.sparc.science Contact: joostw@seas.upenn.edu, support@sparc.science -ManagedBy: "[The SPARC Data Resource Center](https://sparc.science/about)" +ManagedBy: "[The SPARC Data and Resource Center](https://sparc.science/about)" UpdateFrequency: Continually adding new datasets and releasing versions of datasets Tags: - bioinformatics @@ -32,13 +32,13 @@ DataAtWork: Tutorials: - Title: Downloading large scale SPARC datasets URL: https://docs.sparc.science/recipes - AuthorName: "The SPARC Data Resource Center" + AuthorName: "The SPARC Data and Resource Center" - Title: Download public data, scaffolds and run computations URL: https://docs.sparc.science/docs/getting-started-with-the-sparc-python-client - AuthorName: "The SPARC Data Resource Center" + AuthorName: "The SPARC Data and Resource Center" - Title: Using sparc.client for data movement in SPARC URL: https://docs.sparc.science/docs/tutorial-using-sparcclient-for-data-movement-in-sparc - AuthorName: "The SPARC Data Resource Center" + AuthorName: "The SPARC Data and Resource Center" Tools & Applications: - Title: The SPARC Portal URL: https://sparc.science diff --git a/datasets/speedtest-global-performance.yaml b/datasets/speedtest-global-performance.yaml index 68dab2050..ead22a350 100644 --- a/datasets/speedtest-global-performance.yaml +++ b/datasets/speedtest-global-performance.yaml @@ -5,6 +5,10 @@ Documentation: "[Performance Maps Overview](https://github.com/teamookla/ookla-o Contact: opendata@ookla.com ManagedBy: "[Ookla](https://www.ookla.com/ookla-for-good)" UpdateFrequency: Quarterly +Collabs: + ASDI: + Tags: + - infrastructure Tags: - analytics - aws-pds diff --git a/datasets/spherex-qr.yaml b/datasets/spherex-qr.yaml new file mode 100644 index 000000000..66e695c0d --- /dev/null +++ b/datasets/spherex-qr.yaml @@ -0,0 +1,94 @@ +Name: 'SPHEREx Quick Release (QR): An All-Sky Spectral Survey' +Description: 'The Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) is a NASA Astrophysics Medium-class Explorer (MIDEX) mission launched in March 2025. During its planned two-year mission, SPHEREx will perform the first ever all-sky spectral survey in the optical to near-infrared (0.75-5 microns). SPHEREx data will be used to probe inflation and the early universe, trace the history of galactic light production, and investigate the origin of planetary systems and biogenic ices, in addition to contributing to many other astrophysics research topics. IRSA began releasing SPHEREx QR2 data on a weekly basis in October 2025. QR2 features substantially improved calibrations and supersedes QR1.' +Documentation: https://irsa.ipac.caltech.edu/Missions/spherex.html +Contact: https://irsa.ipac.caltech.edu/docs/help_desk.html +ManagedBy: "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech" +UpdateFrequency: SPHEREx QR is updated weekly. The data may also be presented in new ways as the products become available. +Tags: + - aws-pds + - astronomy + - imaging + - object detection + - satellite imagery + - survey +License: https://irsa.ipac.caltech.edu/data_use_terms.html +Citation: 'If you use SPHEREx data from the IRSA archive, please cite the appropriate Digital Object Identifier: [10.26131/IRSA629](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA629) for QR1 or [10.26131/IRSA652](https://www.ipac.caltech.edu/doi/irsa/10.26131/IRSA652) for QR2, include the following acknowledgement: "This publication makes use of data products from the Spectro-Photometer for the History of the Universe, Epoch of Reionization and Ices Explorer (SPHEREx), which is a joint project of the Jet Propulsion Laboratory and the California Institute of Technology, and is funded by the National Aeronautics and Space Administration.", and follow the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html).' +Resources: + - Description: 'QR2 Spectral Images: Calibrated Spectral Images plus per-pixel status and processing flags, variance map, zodiacal model, exposure-averaged PSF, and wavelength WCS. Multi-extension FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/level2 + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Absolute Gain Matrix: Pixel-to-pixel gain variations within a single spectral channel and relative gain differences across channels. FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/abs_gain_matrix + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Exposure-Averaged PSF: Wavelength-dependent point spread function (PSF) estimates on a fine positional grid across each detector. FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/average_psf + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Dark Current: Per pixel dark current. FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/dark + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Dichroic: Map of pixels affected by flux attenuation due to the dichroic filter. FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/dichroic + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Gain Factors: Gain factors for each detector. YAML format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/gain_factors + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Non-functional Pixel Map: Map of permanently non-functioning pixels. FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/nonfunc + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Non-Linearity Correction: Corrections applied to compensate for detector non-linearity due to gain degradation. FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/nonlinear_pars + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Readout Noise: Per-detector read noise maps. FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/readnoise_pars + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Solid Angle Pixel Map: Per-detector measure of the solid angle per pixel in units of squared arcsec. FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/solid_angle_pixel_map + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'QR2 Spectral WCS Map: Detailed World Coordinate System (WCS) map. FITS format.' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr2/spectral_wcs + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'SPHEREx Quick Release 1 (QR1): IRSA began releasing SPHEREx QR1 data on a weekly basis in July 2025. QR1 is superseded by QR2 and only available through January 2026. The data products and their organization in the bucket are described in the [SPHEREx Archive at IRSA User Guide](https://caltech-ipac.github.io/spherex-archive-documentation/).' + ARN: arn:aws:s3:::nasa-irsa-spherex/qr + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False +DataAtWork: + Tutorials: + - Title: Notebook Tutorials + URL: https://irsa.ipac.caltech.edu/docs/notebooks/#accessing-spherex-data + AuthorName: Caltech/IPAC-IRSA + AuthorURL: https://irsa.ipac.caltech.edu diff --git a/datasets/ssl4eo-multi-product-data.yaml b/datasets/ssl4eo-multi-product-data.yaml index 66218702f..4e9325cb0 100644 --- a/datasets/ssl4eo-multi-product-data.yaml +++ b/datasets/ssl4eo-multi-product-data.yaml @@ -4,6 +4,10 @@ Documentation: https://github.com/sunny1401/ssl4eo_multi_satellite_products Contact: https://github.com/sunny1401/ssl4eo_multi_satellite_products ManagedBy: Sankranti Joshi UpdateFrequency: Not updated +Collabs: + ASDI: + Tags: + - satellite imagery Tags: - satellite imagery License: https://creativecommons.org/licenses/by-nc-sa/4.0/ diff --git a/datasets/st-open-data.yaml b/datasets/st-open-data.yaml new file mode 100644 index 000000000..1ba885ac1 --- /dev/null +++ b/datasets/st-open-data.yaml @@ -0,0 +1,38 @@ +Name: SpaceEye-T VVHR EO Open Data +Description: | + SpaceEye-T satellite collects the highest resolution optical imagery among the commercial satellites, 25 cm resolution. The Open Data features various satellite images around the world for end users to experience the power of VVHR optical data. +Documentation: https://www.si-imaging.com/page/72?sca=SpaceEye-T +Contact: https://www.si-imaging.com +UpdateFrequency: The dataset is frequently updated. The frequent updates include the time-series data for regular monitoring, and the data for disaster management. SI Imaging wants to provide the user expierence on what is possible with VVHR optical satellite data. If you have a suggestion for a new location, feedback on the dataset, or any questions, contact us. +Tags: + - aws-pds + - satellite imagery + - earth observation + - disaster response + - geospatial + - image processing +License: | + Creative Commons Attribution 4.0 International (CC BY 4.0). + For more information, See the document "ST-1 Product Terms of Use" at [our Terms of Use webpage](https://si-imaging.com/page/73) +Citation: | + When publicly post of ST products Open Data unedited, "SpaceEye-T © [Year] Satrec Initiative (Licensed under CC BY 4.0)". + When publicly post derivative data created by using ST products Open Data, "SpaceEye-T-derived data © [Year] Satrec Initiative (Originally licensed under CC BY 4.0)". +ManagedBy: "[SI Imaging Services](https://www.si-imaging.com/)" +Resources: + - Description: SpaceEye-T Imagery Collection + ARN: arn:aws:s3:::st-vvhr-opendata + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Bucket](http://st-vvhr-opendata.s3-website.us-west-2.amazonaws.com/)' + - Description: Notifications for new SpaceEye-T VVHR EO Open data + ARN: arn:aws:sns:us-west-2:348881531141:st-vvhr-opendata-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Publications: + - Title: "SpaceEye-T Data Manual" + URL: https://www.si-imaging.com/page/72?sca=SpaceEye-T + AuthorName: SI-Imaging Services +ADXCategories: + - Resources Data diff --git a/datasets/stdpopsim_kern.yaml b/datasets/stdpopsim_kern.yaml index 3edfbc3a9..4fe4f841a 100644 --- a/datasets/stdpopsim_kern.yaml +++ b/datasets/stdpopsim_kern.yaml @@ -1,27 +1,31 @@ -Name: stdpopsim species resources -Description: Contains all resources (genome specifications, recombination maps, etc.) required for - species specific simulation with the stdpopsim package. These resources are originally from a - variety of other consortium and published work but are consolidated here for ease of access and - use. If you are interested in adding a new species to the stdpopsim resource please raise an - issue on the stdpopsim GitHub page to have the necessary files added here. -Documentation: https://stdpopsim.readthedocs.io/en/latest/catalog.html -Contact: https://github.com/popsim-consortium/stdpopsim/issues -ManagedBy: Andrew Kern & Jerome Kelleher -UpdateFrequency: Data will be added as new species, genome assemblies, and genetic map data for already included species become available. -Tags: - - aws-pds - - genetic maps - - life sciences - - population genetics - - recombination maps - - simulations -License: Please see the individual datasets compiled here for licensing details and make sure to cite the original sources of any elements of this data that you use. -Resources: - - Description: https://stdpopsim.readthedocs.io/en/latest/ - ARN: arn:aws:s3:::stdpopsim - Region: us-west-2 - Type: S3 Bucket -DataAtWork: - Tutorials: - Tools & Applications: - Publications: +Name: stdpopsim species resources +Description: Contains all resources (genome specifications, recombination maps, etc.) required for + species specific simulation with the stdpopsim package. These resources are originally from a + variety of other consortium and published work but are consolidated here for ease of access and + use. If you are interested in adding a new species to the stdpopsim resource please raise an + issue on the stdpopsim GitHub page to have the necessary files added here. +Documentation: https://stdpopsim.readthedocs.io/en/latest/catalog.html +Contact: https://github.com/popsim-consortium/stdpopsim/issues +ManagedBy: Andrew Kern & Jerome Kelleher +UpdateFrequency: Data will be added as new species, genome assemblies, and genetic map data for already included species become available. +Collabs: + ASDI: + Tags: + - biodiversity +Tags: + - aws-pds + - genetic maps + - life sciences + - population genetics + - recombination maps + - simulations +License: Please see the individual datasets compiled here for licensing details and make sure to cite the original sources of any elements of this data that you use. +Resources: + - Description: https://stdpopsim.readthedocs.io/en/latest/ + ARN: arn:aws:s3:::stdpopsim + Region: us-west-2 + Type: S3 Bucket +DataAtWork: + Tutorials: + Tools & Applications: + Publications: diff --git a/datasets/surface-pm2-5-v6gl02.yaml b/datasets/surface-pm2-5-v6gl.yaml similarity index 87% rename from datasets/surface-pm2-5-v6gl02.yaml rename to datasets/surface-pm2-5-v6gl.yaml index c74ab92e8..eecf20d65 100644 --- a/datasets/surface-pm2-5-v6gl02.yaml +++ b/datasets/surface-pm2-5-v6gl.yaml @@ -1,10 +1,15 @@ Name: SatPM2.5 Description: Fine particulate matter (PM2.5) concentrations are estimated using information from satellite-, simulation- and monitor-based sources. Aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS, and VIIRS) and their respective retrievals (Dark Target, Deep Blue, MAIAC) is combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations to produce geophysical estimates that explain most of the variance in ground-based PM2.5 measurements. A subsequent statistical fusion incorporates additional information from ground-based PM2.5 measurements. -Documentation: https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V6.GL.02.03 -Contact: randall.martin@wustl.edu +Documentation: https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V6.GL.02.04 +Contact: support@satpm.org ManagedBy: "https://sites.wustl.edu/acag/" UpdateFrequency: Yearly +Collabs: + ASDI: + Tags: + - climate Tags: + - aws-pds - atmosphere - netcdf - environmental @@ -12,15 +17,15 @@ Tags: - health License: Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/) Resources: - - Description: Satellite-Derived Fine Particulate Matter (PM2.5) concentrations from the Atmospheric Composition Analysis Group and Washington University in St. Louis, version GL06.02.03 - ARN: arn:aws:s3:::v6.gl.02.03 + - Description: Satellite-Derived Fine Particulate Matter (PM2.5) concentrations from the Atmospheric Composition Analysis Group and Washington University in St. Louis, version GL06.02.04 + ARN: arn:aws:s3:::v6.gl.02.04 Region: us-west-2 Type: S3 Bucket Explore: - - '[Browse Bucket](https://s3.us-west-2.amazonaws.com/v6.gl.02.03/index.html)' + - '[Browse Bucket](https://s3.us-west-2.amazonaws.com/v6.gl.02.04/index.html)' DataAtWork: Tutorials: - - Title: Importing and Plotting the V6.GL.02.03 dataset into Matlab + - Title: Importing and Plotting the V6.GL.02.04 dataset into Matlab URL: https://sites.wustl.edu/acag/importing-and-plotting-the-v6-gl-02-02-dataset-into-matlab/ AuthorName: Aaron van Donkelaar AuthorURL: https://sites.wustl.edu/acag/people/aaron-van-donkelaar/ diff --git a/datasets/surya-bench.yaml b/datasets/surya-bench.yaml new file mode 100644 index 000000000..37f1d6bdc --- /dev/null +++ b/datasets/surya-bench.yaml @@ -0,0 +1,30 @@ +Name: Surya Bench +Description: | + This dataset provides machine learning (ML)-ready solar data curated from NASA’s Solar Dynamics Observatory (SDO), covering observations from May 13, 2010, to July 31, 2024. It includes Level-1.5 processed data from: + - Atmospheric Imaging Assembly (AIA): + - Helioseismic and Magnetic Imager (HMI): + The dataset is designed to facilitate large-scale ML applications in heliophysics, such as solar activity forecasting, unsupervised representation learning, and scientific foundation model development. +Documentation: https://huggingface.co/datasets/nasa-impact/Surya-bench +Contact: sujit.roy@nasa.gov +ManagedBy: NASA IMPACT +UpdateFrequency: This is the final version of the Dataset. +Tags: + - machine learning + - solar + - aws-pds + - heliophysics +License: | + Creative Commons Attribution 4.0 International. +Citation: > + Roy, S., Singh, T., Freitag, M., Schmude, J., Lal, R., Hegde, D., Ranjan, S., Lin, A., Gaur, V., Vos, E.E. and Ghosal, R., 2024. AI Foundation Model for Heliophysics: Applications, Design, and Implementation. arXiv preprint arXiv:2410.10841. +Resources: + - Description: Surya Bench + ARN: arn:aws:s3:::nasa-surya-bench + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Bucket](http://nasa-surya-bench.s3.amazonaws.com/index.html)' + - Description: Notifications for Surya bench data + ARN: arn:aws:sns:us-west-2:614929158106:nasa-surya-bench-object_created + Region: us-west-2 + Type: SNS Topic diff --git a/datasets/targetepigenomics.yaml b/datasets/targetepigenomics.yaml index d34f37d6d..e08f8f81b 100644 --- a/datasets/targetepigenomics.yaml +++ b/datasets/targetepigenomics.yaml @@ -5,6 +5,10 @@ Contact: targetdcc16@gmail.com ManagedBy: TaRGET II Data Coordination Center (TaRGET-DCC) Documentation: https://data.targetepigenomics.org/ UpdateFrequency: "TaRGET-DCC offers monthly data releases, although this dataset may not be updated at every release." +Collabs: + ASDI: + Tags: + - climate Tags: - biology - bioinformatics diff --git a/datasets/terrain-tiles.yaml b/datasets/terrain-tiles.yaml index 4ce3b3fc5..cc21053e5 100644 --- a/datasets/terrain-tiles.yaml +++ b/datasets/terrain-tiles.yaml @@ -89,10 +89,14 @@ DataAtWork: URL: https://app.shadowmap.org/ AuthorName: Shadowmap Technologies GmbH AuthorURL: https://shadowmap.org + - Title: "Arnis: Generate any location from the real world in Minecraft with a high level of detail" + URL: https://github.com/louis-e/arnis + AuthorName: Louis Erbkamm + AuthorURL: https://louisdev.de/ Publications: - Title: "Landscape transformations produce favorable roosting conditions for turkey vultures and black vultures" URL: https://www.nature.com/articles/s41598-021-94045-3 AuthorName: Jacob E. Hill, Kenneth F. Kellner, Bryan M. Kluever, Michael L. Avery, John S. Humphrey, Eric A. Tillman, Travis L. DeVault & Jerrold L. Belant - Title: Interactive Visualization of 3D Terrain Data Stored in the Cloud URL: https://ieeexplore.ieee.org/abstract/document/9298063 - AuthorName: Gregory Larrick, Yun Tian, Uri Rogers, Halim Acosta, and Fangyang Shen + AuthorName: Gregory Larrick, Yun Tian, Uri Rogers, Halim Acosta, and Fangyang Shen diff --git a/datasets/tglc.yaml b/datasets/tglc.yaml index 4e423c713..2e4f095b2 100644 --- a/datasets/tglc.yaml +++ b/datasets/tglc.yaml @@ -1,5 +1,5 @@ -Name: TESS-GAIA Light Curve (TESS) +Name: TESS-GAIA Light Curve (TGLC) Description: | TESS-Gaia Light Curve (TGLC) is a PSF-based TESS full-frame image (FFI) light curve product. Using Gaia DR3 as priors, the team forward models the FFIs with the effective point spread function to remove contamination from nearby stars. The resulting light curves show a photometric precision closely tracking the pre-launch prediction of the noise level: TGLC's photometric precision consistently reaches ≲2% at 16th TESS magnitude even in crowded fields, demonstrating excellent decontamination and deblending power. Documentation: https://archive.stsci.edu/hlsp/tglc @@ -13,12 +13,12 @@ Tags: License: All HLSPs hosted at MAST are subject to a [CC By 4.0 license](https://creativecommons.org/licenses/by/4.0/). Resources: - Description: TGLC Files - ARN: arn:aws:s3:::stpubdata/hlsp/tglc + ARN: arn:aws:s3:::stpubdata/mast/hlsp/tglc Region: us-east-1 Type: S3 Bucket RequesterPays: False - Description: Notifications for new data - ARN: arn:aws:sns:us-east-1:879230861493:stpubdata/hlsp/tglc + ARN: arn:aws:sns:us-east-1:879230861493:stpubdata/mast/hlsp/tglc Region: us-east-1 Type: SNS Topic DataAtWork: diff --git a/datasets/ucsf-rmac.yaml b/datasets/ucsf-rmac.yaml new file mode 100644 index 000000000..e8b59c393 --- /dev/null +++ b/datasets/ucsf-rmac.yaml @@ -0,0 +1,45 @@ +Name: UCSF Renal Mass CT Dataset +Description: This dataset provides a set of 831 3D Multiphase CT exams of renal masses, registered across phases with annotations identifying the masses +Documentation: https://github.com/LarsonLab/UCSF-RMaC +Contact: "[Peder Larson](peder.larson@ucsf.edu)]" +ManagedBy: "[UCSF Larson Advanced Imaging Lab](https://larsonlab.github.io/)" +UpdateFrequency: ad hoc +Tags: + - aws-pds + - cancer + - life sciences + - computed tomography + - medicine + - medical imaging + - radiology +License: https://creativecommons.org/licenses/by/4.0/ +Citation: +Resources: + - Description: Renal Mass CT Data on S3 + ARN: arn:aws:s3:::ucsf-rmac-dataset + Region: us-west-2 + Type: S3 Bucket + - Description: Notifications for new Renal Mass CT data + ARN: arn:aws:sns:us-west-1:905542596225:ucsf-dmi-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: Label Exploration Tutorial + URL: https://github.com/LarsonLab/UCSF-RMaC/blob/main/tutorials/labelexploration.ipynb + NotebookURL: + AuthorName: Sule Sahin + AuthorURL: https://github.com/sule-sahin + - Title: Mask Overlays + URL: https://github.com/LarsonLab/UCSF-RMaC/blob/main/tutorials/maskoverlays.ipynb + NotebookURL: + AuthorName: Sule Sahin + AuthorURL: https://github.com/sule-sahin + Tools & Applications: + - Title: UCSF Renal Mass CT Dataset + URL: https://github.com/LarsonLab/UCSF-RMaC + AuthorName: Peder Larson + AuthorURL: https://scholar.google.com/citations?user=LrQ7YekAAAAJ&hl=en +DeprecatedNotice: +ADXCategories: + - Healthcare & Life Sciences Data diff --git a/datasets/uniprot.yaml b/datasets/uniprot.yaml index a02c3421c..cd24f0a9c 100644 --- a/datasets/uniprot.yaml +++ b/datasets/uniprot.yaml @@ -18,6 +18,10 @@ Tags: - SPARQL License: http://creativecommons.org/licenses/by/4.0/ Resources: + - Description: UniProt 2025_04 + ARN: arn:aws:s3:::aws-open-data-uniprot-rdf/2025-04/ + Region: eu-west-3 + Type: S3 Bucket - Description: UniProt 2025_03 ARN: arn:aws:s3:::aws-open-data-uniprot-rdf/2025-03/ Region: eu-west-3 diff --git a/datasets/usgs_aqr.yaml b/datasets/usgs_aqr.yaml index 39b7a75e1..74182e289 100644 --- a/datasets/usgs_aqr.yaml +++ b/datasets/usgs_aqr.yaml @@ -1,38 +1,42 @@ -Name: Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States -Description: "Aquatic reflectance produced with the dark spectrum fitting (DSF) algorithm as implemented in the Atmospheric Correction for OLI “lite” (ACOLITE) software (version 20221114.0). Aquatic reflectance is defined here as unitless water-leaving radiance reflectance and represents the ratio of water-leaving radiance (units of watts per square meter per steradian per nanometer) to downwelling irradiance (units of watts per square meter per nanometer) multiplied by pi." -Documentation: https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed -Contact: tvking@usgs.gov -ManagedBy: "[United States Geological Survey](https://www.usgs.gov)" -UpdateFrequency: New scenes are added daily. -Tags: - - aws-pds - - earth observation - - satellite imagery - - geospatial - - natural resource - - cog - - water -License: "Contains modified Copernicus Sentinel data, which is available under the Creative Commons CC BY-SA 3.0 IGO license. Please reference King et al., 2024 (doi 10.5066/P904243C) when referring to the aquatic reflectance, and include the statement 'Contains modified Copernicus Sentinel data [Year]' to acknowledge the data originator." -Citation: "King, T.V., Meyer, M.F., Hundt, S.A., Ball, G.P., Hafen, K.C., Avouris, D.M., Ducar, S.D., Wakefield, B.F., Stengel, V.S., and Vanhellemont, Q., 2024, Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States; U.S. Geological Survey Data Release, doi 10.5066/P904243C." -Resources: - - Description: Scenes and metadata - ARN: arn:aws:s3:::usgs-wma-sentinel-2-aqr-acolite-dsf/version_01 - Region: us-west-2 - Type: S3 Bucket - - Description: New scene notification - ARN: arn:aws:sns:us-west-2:242201296900:usgs-wma-sentinel-2-aqr-acolite-dsf-object_created - Region: us-west-2 - Type: SNS Topic -DataAtWork: - Tutorials: - - Title: "tutorial.zip" - URL: https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed - AuthorName: S.D. Ducar - Tools & Applications: - - Title: GLOBUS Access Point - URL: https://app.globus.org/file-manager?origin_id=8fd8727f-c464-4e86-a5ed-c6db72848c02&origin_path=%2F - AuthorName: T.V. King, et al. - Publications: - - Title: Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States - URL: https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed +Name: Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States +Description: "Aquatic reflectance produced with the dark spectrum fitting (DSF) algorithm as implemented in the Atmospheric Correction for OLI “lite” (ACOLITE) software (version 20221114.0). Aquatic reflectance is defined here as unitless water-leaving radiance reflectance and represents the ratio of water-leaving radiance (units of watts per square meter per steradian per nanometer) to downwelling irradiance (units of watts per square meter per nanometer) multiplied by pi." +Documentation: https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed +Contact: tvking@usgs.gov +ManagedBy: "[United States Geological Survey](https://www.usgs.gov)" +UpdateFrequency: New scenes are added daily. +Collabs: + ASDI: + Tags: + - oceans +Tags: + - aws-pds + - earth observation + - satellite imagery + - geospatial + - natural resource + - cog + - water +License: "Contains modified Copernicus Sentinel data, which is available under the Creative Commons CC BY-SA 3.0 IGO license. Please reference King et al., 2024 (doi 10.5066/P904243C) when referring to the aquatic reflectance, and include the statement 'Contains modified Copernicus Sentinel data [Year]' to acknowledge the data originator." +Citation: "King, T.V., Meyer, M.F., Hundt, S.A., Ball, G.P., Hafen, K.C., Avouris, D.M., Ducar, S.D., Wakefield, B.F., Stengel, V.S., and Vanhellemont, Q., 2024, Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States; U.S. Geological Survey Data Release, doi 10.5066/P904243C." +Resources: + - Description: Scenes and metadata + ARN: arn:aws:s3:::usgs-wma-sentinel-2-aqr-acolite-dsf/version_01 + Region: us-west-2 + Type: S3 Bucket + - Description: New scene notification + ARN: arn:aws:sns:us-west-2:242201296900:usgs-wma-sentinel-2-aqr-acolite-dsf-object_created + Region: us-west-2 + Type: SNS Topic +DataAtWork: + Tutorials: + - Title: "tutorial.zip" + URL: https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed + AuthorName: S.D. Ducar + Tools & Applications: + - Title: GLOBUS Access Point + URL: https://app.globus.org/file-manager?origin_id=8fd8727f-c464-4e86-a5ed-c6db72848c02&origin_path=%2F + AuthorName: T.V. King, et al. + Publications: + - Title: Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States + URL: https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed AuthorName: T.V. King, et al. \ No newline at end of file diff --git a/datasets/venus-l2a-cogs.yaml b/datasets/venus-l2a-cogs.yaml index ab3a4736d..2a00c448f 100644 --- a/datasets/venus-l2a-cogs.yaml +++ b/datasets/venus-l2a-cogs.yaml @@ -13,6 +13,10 @@ Documentation: https://github.com/earthdaily/venus-on-aws/ Contact: Klaus Bachhuber - klaus.bachhuber@earthdaily.com ManagedBy: "[EarthDaily Analytics](https://earthdaily.com/)" UpdateFrequency: New Venus data are added regularly +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - agriculture diff --git a/datasets/vt-opendata.yaml b/datasets/vt-opendata.yaml index 2c06c0664..d9991d4e9 100644 --- a/datasets/vt-opendata.yaml +++ b/datasets/vt-opendata.yaml @@ -4,6 +4,10 @@ Documentation: https://vcgi.vermont.gov/data-and-programs/ Contact: If you have specific questions please contact - vcgi@vermont.gov ManagedBy: "[Vermont Center for Geographic Information](https://vcgi.vermont.gov)" UpdateFrequency: Vermont acquires statewide imagery approximately once every other year. Lidar is acquired approximately once every 5-8 years. High-resolution landcover is generated once every other year. +Collabs: + ASDI: + Tags: + - satellite imagery Tags: - earth observation - aerial imagery diff --git a/datasets/wbg-cckp.yaml b/datasets/wbg-cckp.yaml index 69deae2ea..78698a791 100644 --- a/datasets/wbg-cckp.yaml +++ b/datasets/wbg-cckp.yaml @@ -4,6 +4,10 @@ Documentation: https://worldbank.github.io/climateknowledgeportal Contact: C. MacKenzie Dove cdove@worldbank.org; askclimate@worldbank.org ManagedBy: "[World Bank Group](https://www.worldbank.org/en/home)" UpdateFrequency: Semi-annually +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - climate diff --git a/datasets/whiffle-wins50.yaml b/datasets/whiffle-wins50.yaml index b0ef3611f..82590b172 100644 --- a/datasets/whiffle-wins50.yaml +++ b/datasets/whiffle-wins50.yaml @@ -4,6 +4,10 @@ Documentation: https://gitlab.com/whiffle-public/whiffle-open-data Contact: support@whiffle.nl ManagedBy: "[Whiffle](http://www.whiffle.nl/)" UpdateFrequency: No updates planned. +Collabs: + ASDI: + Tags: + - energy Tags: - aws-pds - weather diff --git a/datasets/wis2-global-cache.yaml b/datasets/wis2-global-cache.yaml index 481518c19..99e23ea97 100644 --- a/datasets/wis2-global-cache.yaml +++ b/datasets/wis2-global-cache.yaml @@ -4,6 +4,10 @@ Documentation: https://www.metoffice.gov.uk/services/data/external-data-channels Contact: gisc-exeter@metoffice.gov.uk ManagedBy: "[Met Office](https://www.metoffice.gov.uk/)" UpdateFrequency: New data added as soon as available from origin WIS2 Nodes. +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - atmosphere @@ -19,11 +23,15 @@ Tags: License: There are no restrictions on the use of this data. Attribution of original source is requested. Resources: - Description: Core data as defined in the [WMO Unified Data Policy (Resolution 1 (Cg-19))](https://library.wmo.int/idurl/4/58009) and the [initial Catalogue of Core Data](http://library.wmo.int/doc_num.php?explnum_id=11001#page=139). Data covers a global extent. Data is provided in WMO approved formats - GRIB, BUFR and NetCDF. Users should subscribe to receive notification messages about newly available data. Please refer to the documentation. - ARN: arn:aws:s3:::wis2-global-cache - Region: eu-west-2 + ARN: arn:aws:s3:::wis2globalcache + Region: us-east-1 Type: S3 Bucket Explore: - - '[Browse Bucket](https://wis2-global-cache.s3.amazonaws.com/index.html)' + - '[Browse Bucket](https://wis2globalcache.s3.us-east-1.amazonaws.com/index.html)' + - Description: Notifications for new wis2globalcache data + ARN: arn:aws:sns:us-east-1:211648629506:wis2globalcache-object_created + Region: us-east-1 + Type: SNS Topic DataAtWork: Tutorials: - Title: WIS 2.0 video for 19th World Meterological Congress @@ -38,3 +46,4 @@ DataAtWork: AuthorName: World Meteorological Organisation ADXCategories: - Environmental Data + diff --git a/datasets/wise-allsky.yaml b/datasets/wise-allsky.yaml index a4d34a732..4daf1f965 100644 --- a/datasets/wise-allsky.yaml +++ b/datasets/wise-allsky.yaml @@ -4,6 +4,10 @@ Documentation: https://irsa.ipac.caltech.edu/Missions/wise.html Contact: https://irsa.ipac.caltech.edu/docs/help_desk.html ManagedBy: "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech" UpdateFrequency: The All-Sky Data Release has been finalized and will not be updated. +Collabs: + ASDI: + Tags: + - climate Tags: - aws-pds - astronomy diff --git a/datasets/wpto-pds-us-wave.yaml b/datasets/wpto-pds-us-wave.yaml index e72bfa821..591f1382a 100644 --- a/datasets/wpto-pds-us-wave.yaml +++ b/datasets/wpto-pds-us-wave.yaml @@ -81,6 +81,18 @@ Resources: Type: S3 Bucket Explore: - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FGulf_of_Mexico_and_Puerto_Rico%2F)' + - Description: Updated version of 42 Year Wave Hindcast (1979-2020) for Alaska at 3-hour temporal resolution and down to 200m spatial resolution in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format. Updates resolve issues with NaNs. + ARN: arn:aws:s3:::wpto-pds-us-wave/v1.0.1/Alaska/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FAlaska%2F)' + - Description: Updated version of 42 Year Wave Hindcast (1979-2020) for the West Coast of the United States at 3-hour temporal resolution and down to 200m spatial resolution in [HDF5](https://portal.hdfgroup.org/display/HDF5/HDF5) format. Updates resolve issues with NaNs. + ARN: arn:aws:s3:::wpto-pds-us-wave/v1.0.1/West_Coast/ + Region: us-west-2 + Type: S3 Bucket + Explore: + - '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FWest_Coast%2F)' DataAtWork: Tutorials: Tools & Applications: diff --git a/datasets/ztf.yaml b/datasets/ztf.yaml new file mode 100644 index 000000000..5b7cc2546 --- /dev/null +++ b/datasets/ztf.yaml @@ -0,0 +1,37 @@ +Name: 'Zwicky Transient Facility (ZTF)' +Description: 'The Zwicky Transient Facility (ZTF) is a time-domain astronomy survey that uses the Palomar 48 inch Schmidt telescope and a custom-built wide-field camera to image the night sky in three photometric filters (g, r, and i). It is a fully-automated survey aimed at a systematic exploration of optical transient phenomena. It completes a scan of the observable northern sky approximately every three nights.' +Documentation: https://irsa.ipac.caltech.edu/Missions/ztf.html +Contact: https://irsa.ipac.caltech.edu/docs/help_desk.html +ManagedBy: "NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Caltech" +UpdateFrequency: ZTF datasets may be updated approximately twice per year. The data may also be presented in new ways as the products become available. +Tags: + - astronomy + - aws-pds + - object detection + - parquet + - survey +License: https://irsa.ipac.caltech.edu/data_use_terms.html +Citation: "If you use the Objects Table, please cite the Digital Object Identifier (DOI): [10.26131/IRSA597](https://www.ipac.caltech.edu/doi/10.26131/IRSA597). If you use the Lightcurves, please cite the Digital Object Identifier (DOI): [10.26131/IRSA598](https://www.ipac.caltech.edu/doi/10.26131/IRSA598). In addition, please follow the [ZTF acknowledgement guidelines](https://irsa.ipac.caltech.edu/data/ZTF/docs/releases/ztf_release_notes_latest) and the [IRSA acknowledgement guidelines](https://irsa.ipac.caltech.edu/ack.html)." +Resources: + - Description: 'Objects Table is a catalog of PSF-fit photometry detections extracted from ZTF reference images. The reference images were generated by stacking single exposures acquired from all science programs in the survey, resulting in photometry up to 2.5 magnitudes deeper than single-exposure detections. Objects Table contains both point-like and extended objects. The survey covers ~25,000 square degrees of the northern hemisphere. This version of the catalog is in Apache Parquet format and partitioned following the Hierarchical Adaptive Tiling Scheme ([HATS](https://hats.readthedocs.io/)).' + ARN: arn:aws:s3:::ipac-irsa-ztf/contributed/dr23/objects/hats + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False + - Description: 'Lightcurves is a catalog of PSF-fit photometry detections extracted from single-exposure images at the locations of Objects Table detections. An object ID identifies related data in both catalogs. Photometry is in the native ZTF photometric system and the epoch-dependent zeropoints have already been applied. Note that Lightcurves detections may be missing, for example, in cases where the Objects Table detection is fainter or approximately equal to the single-exposure sensitivity limits. This version of the catalog is in Apache Parquet format and partitioned following the Hierarchical Adaptive Tiling Scheme ([HATS](https://hats.readthedocs.io/)).' + ARN: arn:aws:s3:::ipac-irsa-ztf/contributed/dr23/lc/hats + Region: us-east-1 + Type: S3 Bucket + RequesterPays: False + AccountRequired: False +DataAtWork: + Tutorials: + - Title: IRSA Notebook Tutorials + URL: https://irsa.ipac.caltech.edu/docs/notebooks/ + AuthorName: Caltech/IPAC-IRSA + AuthorURL: https://irsa.ipac.caltech.edu + - Title: Multi-Wavelength Light Curves Tutorial + URL: https://nasa-fornax.github.io/fornax-demo-notebooks/light_curves/light_curve_generator.html + AuthorName: Fornax Initiative + AuthorURL: https://pcos.gsfc.nasa.gov/Fornax/ diff --git a/tags.yaml b/tags.yaml index 2e4f2b763..4b0bf6f67 100644 --- a/tags.yaml +++ b/tags.yaml @@ -51,6 +51,7 @@ - broadcast ephemeris - Caenorhabditis elegans - calcium imaging +- canada - cancer - carbon - cell biology @@ -89,6 +90,7 @@ - conversation data - copper - copyright monitoring +- coral reef - coronavirus - cover song identification - COVID-19 @@ -110,6 +112,7 @@ - deafrica - decennial census - deep learning +- dem - demographic and housing characteristics file - demographics - demography @@ -128,6 +131,9 @@ - drilling - drifters - Drosophila melanogaster +- drug discovery +- dsm +- dtm - earth observation - earthquakes - economics @@ -202,7 +208,9 @@ - hazard indicator - Hawkes Process - hdf5 +- hdf - health +- heliophysics - high-throughput imaging - hiring - hispanic @@ -392,6 +400,8 @@ - space biology - space weather - SPARQL +- spatial omics +- spatial transcriptomics - speaker identification - speech processing - speech recognition @@ -416,6 +426,7 @@ - temporal point process - tertiary analysis - text analysis +- tiff - tiles - time series forecasting - trading @@ -452,3 +463,8 @@ - x-ray tomography - xml - zarr +- wind speeds +- cloud amount +- visibility +- ERA5 +- MPAS