Updated datasets 2024-08-06 UTC

opengeos · Aug 6, 2024 · cbef3f8 · cbef3f8
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diff --git a/aws_open_datasets.json b/aws_open_datasets.json
@@ -28214,7 +28214,7 @@
         "ARN": "arn:aws:s3:::tiger-training",
         "Region": "us-west-2",
         "Type": "S3 Bucket",
-        "Documentation": "https://tiger.grand-challenge.org/data/",
+        "Documentation": "https://tiger.grand-challenge.org/Data/",
         "Contact": "https://tiger.grand-challenge.org/contact/",
         "ManagedBy": "Radboud University Medical Center",
         "UpdateFrequency": "As required",

diff --git a/aws_open_datasets.tsv b/aws_open_datasets.tsv
@@ -1033,7 +1033,7 @@ Sup3rCC	Sup3rCC Generative Models	arn:aws:s3:::nrel-pds-sup3rcc/models/	us-west-
 Swiss Public Transport Stops	data files ESRI FGDB, CSV , MapInfo, Interlis	arn:aws:s3:::data.geo.admin.ch/ch.bav.haltestellen-oev/data.zip	eu-west-1	S3 Bucket	https://www.bav.admin.ch/bav/de/home/allgemeine-themen/fachthemen/geoinformation	[email protected]	Swiss Geoportal	annually	You may use this dataset for non-commercial purposes. You may use this dataset f	aws-pds, cities, geospatial, infrastructure, mapping, traffic, transportation	['[Browse Bucket](https://data.geo.admin.ch/index.html)']				
 Synthea Coherent Data Set	Synthetic data set that includes FHIR resources, DICOM images, genomic data, phy	arn:aws:s3:::synthea-open-data/coherent/	us-east-1	S3 Bucket	https://doi.org/10.3390/electronics11081199	[email protected]	[The MITRE Corporation](https://www.mitre.org)	Rarely	[Creative Commons Attribution 4.0 International License](https://creativecommons	aws-pds, health, bioinformatics, life sciences, medicine, csv, dicom, genomic, imaging					
 Synthea synthetic patient generator data in OMOP Common Data Model	Project data files	arn:aws:s3:::synthea-omop	us-east-1	S3 Bucket	https://github.com/synthetichealth/synthea/wiki	Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6	[Amazon Web Sevices](https://aws.amazon.com/)	Not updated	https://github.com/synthetichealth/synthea/blob/master/LICENSE	aws-pds, bioinformatics, health, life sciences, natural language processing, us					
-TIGER Training	Whole slide images with corresponding annotations including tumor, stroma and tu	arn:aws:s3:::tiger-training	us-west-2	S3 Bucket	https://tiger.grand-challenge.org/data/	https://tiger.grand-challenge.org/contact/	Radboud University Medical Center	As required	CC BY-NC 4.0	aws-pds, life sciences, cancer, computational pathology, grand-challenge.org, histopathology, deep learning, computer vision					
+TIGER Training	Whole slide images with corresponding annotations including tumor, stroma and tu	arn:aws:s3:::tiger-training	us-west-2	S3 Bucket	https://tiger.grand-challenge.org/Data/	https://tiger.grand-challenge.org/contact/	Radboud University Medical Center	As required	CC BY-NC 4.0	aws-pds, life sciences, cancer, computational pathology, grand-challenge.org, histopathology, deep learning, computer vision					
 TSBench	TSBench Evaluation Metrics and Probabilistic Forecasts	arn:aws:s3:::odp-tsbench	us-east-1	S3 Bucket	https://github.com/awslabs/gluon-ts/tree/master/src/gluonts/nursery/tsbench/READ	[email protected], [email protected]	[Amazon Web Services](https://aws.amazon.com)	Not expected to be updated	[CC BY](https://creativecommons.org/licenses/by/4.0/)	machine learning, deep learning, meta learning, benchmark, time series forecasting					
 Tabula Muris	https://githubcom/czbiohub/tabula-muris	arn:aws:s3:::czb-tabula-muris	us-east-1	S3 Bucket	https://github.com/czbiohub/tabula-muris/blob/master/tabula-muris-on-aws.md	If you have questions about the data, you can create an Issue at https://github.	[Chan Zuckerberg Biohub](https://www.czbiohub.org/)	This is the final version of the dataset, it will not be updated.	https://github.com/czbiohub/tabula-muris/blob/master/LICENSE	aws-pds, biology, encyclopedic, genomic, health, life sciences, medicine					
 Tabula Muris Senis	https://githubcom/czbiohub/tabula-muris-senis	arn:aws:s3:::czb-tabula-muris-senis	us-west-2	S3 Bucket	https://github.com/czbiohub/tabula-muris-senis/blob/master/tabula-muris-senis-on	If you have questions about the data, you can create an Issue at https://github.	[Chan Zuckerberg Biohub](https://www.czbiohub.org/)	This is the first version of the dataset and it will be updated after the manusc	https://github.com/czbiohub/tabula-muris-senis/blob/master/LICENSE	aws-pds, biology, encyclopedic, genomic, health, life sciences, medicine, single-cell transcriptomics					

diff --git a/datasets/broad-gnomad.yaml b/datasets/broad-gnomad.yaml
@@ -1,7 +1,7 @@
 Name: Genome Aggregation Database (gnomAD)
 Description: |
   The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use.
-  The v4 data set (GRCh38) spans 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, of [diverse ancestries](https://gnomad.broadinstitute.org/stats#diversity), sequenced sequenced as part of various disease-specific and population genetic studies.
+  The v4.1 data set (GRCh38) spans 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, of [diverse ancestries](https://gnomad.broadinstitute.org/stats#diversity), sequenced sequenced as part of various disease-specific and population genetic studies.
   The gnomAD Principal Investigators and team can be found [here](https://gnomad.broadinstitute.org/team), and the groups that have contributed data to the current release are listed [here](https://gnomad.broadinstitute.org/about).
   Sign up for the gnomAD mailing list [here](http://broad.io/gnomad_list).
 Documentation: https://gnomad.broadinstitute.org/about
@@ -39,8 +39,8 @@ DataAtWork:
       AuthorName: Hail Team
       AuthorURL: https://hail.is/
   Publications:
-    - Title: A genome-wide mutational constraint map quantified from variation in 76,156 human genomes. bioRxiv 2022.03.20.485034 (2022)
-      URL: https://doi.org/10.1101/2022.03.20.485034
+    - Title: A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024)
+      URL: https://doi.org/10.1038/s41586-023-06045-0
       AuthorName: Chen, S., Francioli, L. C., Goodrich, J. K., Collins, R. L., Wang, Q., Alföldi, J., Watts, N. A., Vittal, C., Gauthier, L. D., Poterba, T., Wilson, M. W., Tarasova, Y., Phu, W., Yohannes, M. T., Koenig, Z., Farjoun, Y., Banks, E., Donnelly, S., Gabriel, S., Gupta, N., Ferriera, S., Tolonen, C., Novod, S., Bergelson, L., Roazen, D., Ruano-Rubio, V., Covarrubias, M., Llanwarne, C., Petrillo, N., Wade, G., Jeandet, T., Munshi, R., Tibbetts, K., gnomAD Project Consortium, O’Donnell-Luria, A., Solomonson, M., Seed, C., Martin, A. R., Talkowski, M. E., Rehm, H. L., Daly, M. J., Tiao, G., Neale, B. M., MacArthur, D. G. & Karczewski, K. J.
     - Title: The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020)
       URL: https://doi.org/10.1038/s41586-020-2308-7

diff --git a/datasets/nrel-pds-dsgrid.yaml b/datasets/nrel-pds-dsgrid.yaml
@@ -47,10 +47,10 @@ DataAtWork:
     - Title: dsgrid Documentation
       URL: https://dsgrid.github.io/dsgrid/
       AuthorName: Elaine Hale
-    - Title: Python API for Accessing dsgrid Data for the [Electrification Futures Study (EFS)](https://data.openei.org/submissions/4130)
+    - Title: Python API for Accessing dsgrid Data for the Electrification Futures Study (EFS)
       URL: https://github.com/dsgrid/dsgrid-legacy-efs-api
       AuthorName: Elaine Hale
-    - Title: dsgrid Project Standard Scenarios for the [TEMPO Project](https://data.openei.org/submissions/5958)
+    - Title: dsgrid Project Standard Scenarios for the TEMPO Project
       URL: https://github.com/dsgrid/dsgrid-project-StandardScenarios/tree/main/tempo_project
       AuthorName: Elaine Hale
   Tools & Applications:

diff --git a/datasets/tiger.yaml b/datasets/tiger.yaml
@@ -1,7 +1,7 @@
 Name: TIGER Training
 Description: |
     "This dataset contains the training data for the [Tumor InfiltratinG lymphocytes in breast cancER or TIGER](https://tiger.grand-challenge.org) challenge. TIGER is the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in breast cancer histopathology slides. TILs are proving to be an important biomarker in cancer patients as they can play a part in killing tumor cells, particularly in some types of breast cancer. Identifying and measuring TILs can help to better target treatments, particularly immunotherapy, and may result in lower levels of other more aggressive treatments, including chemotherapy."
-Documentation: https://tiger.grand-challenge.org/data/
+Documentation: https://tiger.grand-challenge.org/Data/
 Contact: https://tiger.grand-challenge.org/contact/
 ManagedBy: Radboud University Medical Center
 UpdateFrequency: As required