Merge branch 'main' into main

berylrab · web-flow · commit 5109d9b92086 · 2025-08-06T10:21:18.000-04:00
diff --git a/datasets/aws-public-blockchain.yaml b/datasets/aws-public-blockchain.yaml
@@ -3,25 +3,19 @@ Description: >
   <p>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.</p>
 
   <h4>Datasets</h4>
-  <table width="100%">
-  <thead>
-    <tr><th>Blockchain dataset</th><th>Maintained by</th><th>Path</th></tr>
-  </thead>
-  <tbody>
-    <tr><td>Bitcoin</td>     <td>AWS</td>    <td><code>s3://aws-public-blockchain/v1.0/btc/</code></td></tr>
-    <tr><td>Ethereum</td>    <td>AWS</td>    <td><code>s3://aws-public-blockchain/v1.0/eth/</code></td></tr>
-    <tr><td>Arbitrum</td>    <td>SonarX</td> <td><code>s3://aws-public-blockchain/v1.1/sonarx/arbitrum/</code></td></tr>
-    <tr><td>Aptos</td>       <td>SonarX</td> <td><code>s3://aws-public-blockchain/v1.1/sonarx/aptos/</code></td></tr>
-    <tr><td>Base</td>        <td>SonarX</td> <td><code>s3://aws-public-blockchain/v1.1/sonarx/base/</code></td></tr>
-    <tr><td>Provenance</td>  <td>SonarX</td> <td><code>s3://aws-public-blockchain/v1.1/sonarx/provenance/</code></td></tr>
-    <tr><td>XRP Ledger</td>  <td>SonarX</td> <td><code>s3://aws-public-blockchain/v1.1/sonarx/xrp/</code></td></tr>
-    <tr><td>Stellar (<a href="https://developers.stellar.org/docs/learn/fundamentals/data-format/xdr" rel="noopener noreferrer">XDR files</a>)</td>  <td>Stellar</td> <td><code>s3://aws-public-blockchain/v1.1/stellar/</code></td></tr>
-    <tr><td>The Open Network (TON)</td>  <td>TON</td> <td><code>s3://aws-public-blockchain/v1.1/ton/</code></td></tr>
-  </tbody>
-  </table>
-  </br>
+  <b>Blockchain dataset - Maintained by - Path:</b><br>
+  - Bitcoin                 - AWS     - <code>s3://aws-public-blockchain/v1.0/btc/</code><br>
+  - Ethereum                - AWS     - <code>s3://aws-public-blockchain/v1.0/eth/</code><br>
+  - Arbitrum                - SonarX  - <code>s3://aws-public-blockchain/v1.1/sonarx/arbitrum/</code><br>
+  - Aptos                   - SonarX  - <code>s3://aws-public-blockchain/v1.1/sonarx/aptos/</code><br>
+  - Base                    - SonarX  - <code>s3://aws-public-blockchain/v1.1/sonarx/base/</code><br>
+  - Provenance              - SonarX  - <code>s3://aws-public-blockchain/v1.1/sonarx/provenance/</code><br>
+  - XRP Ledger              - SonarX  - <code>s3://aws-public-blockchain/v1.1/sonarx/xrp/</code><br>
+  - Stellar(<a href="https://developers.stellar.org/docs/learn/fundamentals/data-format/xdr" rel="noopener noreferrer">XDR files</a>)              - Stellar - <code>s3://aws-public-blockchain/v1.1/stellar/</code><br>
+  - The Open Network (TON)  - TON - <code>s3://aws-public-blockchain/v1.1/ton/</code><br>
+  </br> 
 
-  <h3>Become a Data Provider</h3>
+  <h4>Become a Data Provider</h4>
   <p>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 <a href="mailto:aws-public-blockchain@amazon.com">aws-public-blockchain@amazon.com</a>.</p>
   
 
diff --git a/datasets/gaia-dr3.yaml 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
diff --git a/datasets/noaa-historicalcharts.yaml b/datasets/noaa-historicalcharts.yaml
@@ -2,7 +2,7 @@ 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.<br/><br/>
+  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.<br/><br/>
   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.
@@ -25,3 +25,4 @@ Resources:
     Type: S3 Bucket
     Explore:
     - '[Browse Bucket](https://noaa-nos-historicalcharts-pds.s3.amazonaws.com/index.html)'
+ 
diff --git a/datasets/noaa-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).<br/>RINEX data can be found at: *rinex/`YYYY`/`DDD`/`ssss`/*
       - **Station logs**: 
diff --git a/datasets/ome-zarr-open-scivis.yaml b/datasets/ome-zarr-open-scivis.yaml
@@ -0,0 +1,48 @@
+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 <matt@mmmccormick.com>"
+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
+License: CC-BY-4.0 unless otherwise specified
+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/proteingym.yaml 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/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/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/tags.yaml b/tags.yaml
@@ -202,7 +202,9 @@
 - hazard indicator
 - Hawkes Process
 - hdf5
+- hdf
 - health
+- heliophysics
 - high-throughput imaging
 - hiring
 - hispanic
@@ -418,6 +420,7 @@
 - temporal point process
 - tertiary analysis
 - text analysis
+- tiff
 - tiles
 - time series forecasting
 - trading
