Merge branch 'main' into main

Author: berylrab

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: [email protected]
+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/racs
+    Region: ap-southeast-2
+    Type: S3 Bucket
+    RequesterPays: False
+  - Description: Notifications for new Rapid ASKAP Continuum Survey (RACS) data
+    ARN: arn:aws:sns:ap-southeast-2:336305517014:racs-low1-object_created
+    Region: sp-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. 

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

datasets/mosaic.yaml

@@ -1,6 +1,6 @@
 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://github.com/blahner/mosaic-preprocessing
+Documentation: https://blahner.github.io/MOSAICfmri/
 Contact: [email protected]
 ManagedBy: Massachusetts Institute of Technology, Georgia Tech
 UpdateFrequency: New data is uploaded as researchers preprocess their fMRI data according to MOSAIC format and submit.
@@ -23,6 +23,20 @@ Resources:
     - '[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

datasets/noaa-nexrad.yaml

@@ -52,14 +52,6 @@ Resources:
     ARN: arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel3Object
     Region: us-east-1
     Type: SNS Topic
-  - Description: "*OLD NEXRAD Level II archive bucket* which is now <b>Deprecated</b>. It is recommended to move to the new bucket: unidata-nexrad-level2 and SNS topic: arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2Archive"
-    ARN: arn:aws:s3:::noaa-nexrad-level2
-    Region: us-east-1
-    Type: S3 Bucket
-  - Description: "Notifications for the *OLD Level II archival bucket*  which is now <b>Deprecated</b>. It is recommended to move to the new bucket: unidata-nexrad-level2 and SNS topic: arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2Archive"
-    ARN: arn:aws:sns:us-east-1:811054952067:NewNEXRADLevel2Archive
-    Region: us-east-1
-    Type: SNS Topic
 DataAtWork:
   Tutorials:
     - Title: Using Python to Access NCEI Archived NEXRAD Level 2 Data (Jupyter notebook)

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: [email protected]

datasets/st-open-data.yaml

@@ -11,7 +11,12 @@ Tags:
   - disaster response
   - geospatial
   - image processing
-License: Creative Commons Attribution-NonCommercial 4.0 International
+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

datasets/surface-pm2-5-v6gl.yaml

@@ -1,14 +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.02.04
-Contact: [email protected]
+Contact: [email protected]
 ManagedBy: "https://sites.wustl.edu/acag/"
 UpdateFrequency: Yearly
 Collabs:
   ASDI:
     Tags:
       - climate
 Tags:
+  - aws-pds
   - atmosphere
   - netcdf
   - environmental