Merge branch 'main' into raen/update/spherex-with-qr2

Author: cstner

datasets/askap.yaml

@@ -23,13 +23,13 @@ Tags:
 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
+    ARN: arn:aws:s3:::askap-odp/racs-low1/
     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
+  - 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:

datasets/rcm-ceos-ard.yaml

@@ -42,7 +42,7 @@ 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
@@ -66,3 +66,7 @@ DataAtWork:
       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/
+    - 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

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

datasets/sentinel-products-ca-mirror.yaml

@@ -35,5 +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)'