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datasets/askap.yaml

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License: CC-BY-4.0. Attribution required for refereed scientific papers.
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Resources:
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- Description: The Rapid ASKAP Continuum Survey (RACS) Public Data Releases
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ARN: arn:aws:s3:::askap/racs
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ARN: arn:aws:s3:::askap-odp/racs-low1/
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Region: ap-southeast-2
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Type: S3 Bucket
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RequesterPays: False
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- Description: Notifications for new Rapid ASKAP Continuum Survey (RACS) data
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ARN: arn:aws:sns:ap-southeast-2:336305517014:racs-low1-object_created
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Region: sp-southeast-2
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- Description: Notifications for new ASKAP data
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ARN: arn:aws:sns:ap-southeast-2:336305517014:askap-odp-object_created
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Region: ap-southeast-2
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Type: SNS Topic
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DataAtWork:
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Tutorials:

datasets/huj-herbarium.yaml

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Name: National Herbarium of Israel
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Description:
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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.
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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.
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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.
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Documentation: https://bit.ly/HUJVirtualHerbarium
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ManagedBy: National Natural History Collections, The Hebrew University of Jerusalem
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UpdateFrequency: Monthly
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Tags:
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- biology
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- life sciences
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- biodiversity
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- environmental
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- climate
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- digital preservation
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- imaging
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- image processing
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- aws-pds
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License: CC-BY-SA 4.0
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Citation: Vascular plants - Herbarium of The National Natural History Collections was accessed on DATE from https://registry.opendata.aws/huj-herbarium.
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Resources:
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- Description: HUJ Herbarium Collection Images
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ARN: arn:aws:s3:::hujinnhc/specify_assets/
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Region: il-central-1
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Type: S3 Bucket
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Explore:
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DataAtWork:
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Tutorials:
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- Title: How to use AWS S3 bucket to explore our public images dataset
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URL: https://bit.ly/HUJimages
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NotebookURL:
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AuthorName: Eyal Ben-Hur
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AuthorURL:
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DeprecatedNotice:
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ADXCategories:
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- Healthcare & Life Sciences Data
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datasets/noaa-nws-naqfc-pds.yaml

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Type: S3 Bucket
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Explore:
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- '[Browse Bucket](https://noaa-nws-naqfc-pds.s3.amazonaws.com/index.html)'
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- Description: New data notifications for NAQFC, only Lambda and SQS protocols allowed
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ARN: arn:aws:sns:us-east-1:709902155096:NewNWSAirQualityObject
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Region: us-east-1
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Type: SNS Topic
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DataAtWork:
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Tutorials:
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Tools & Applications:

datasets/radiant.yaml

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Name: RADIANT Public Data
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Description: >
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The Real-time Analysis and Discovery in Integrated And Networked Technologies (RADIANT)
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initiative seeks to develop an extensible, federated framework for rapid exchange of
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multimodal clinical and research data on behalf of accelerated discovery and patient impact.
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Coordination and implementation of initial RADIANT deployments will leverage a network of
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more than 35 partnered health care systems and participating patient families within the
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Children’s Brain Tumor Network (CBTN) and the Pediatric Neuro-Oncology Consortium (PNOC).
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This data set is composed of public multi-modal data provisioned by RADIANT. The initial
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bolus of data is from CBTN and consists of clinical data extracted/abstracted from
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electronic medical records, omic data such as genomics, transcriptomics and proteomics and
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radiology and pathology imaging data. Data are collected or generated as part of consent-based,
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IRB-approved observational or interventional studies with the goal of making it available
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globally to researchers across a broad number of disciplines.
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Documentation: https://cbtn.org/research-resources
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ManagedBy: "[The Center for Data-Driven Discovery in Biomedicine (D3b) at the Children's Hospital of Philadelphia](https://d3b.center/)"
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UpdateFrequency: |
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Data is updated on a regular basis by the RADIANT teams to make data available as
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rapidly as possible.
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Tags:
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- aws-pds
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- life sciences
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- cancer
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- genetic
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- genomic
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- transcriptomics
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- medical imaging
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- radiology
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- Homo sapiens
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- pediatric
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- whole genome sequencing
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License: "NIH Genomic Data Sharing Policy: https://grants.nih.gov/grants/guide/notice-files/not-od-14-124.html"
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Resources:
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- Description: "Children's Brain Tumor Network"
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ARN: arn:aws:s3:::opendata-chop-study-us-east-1-prd-sd-bhjxbdqk
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Region: us-east-1
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Type: S3 Bucket
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ControlledAccess: https://cbtn.org/
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DataAtWork:
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Tools & Applications:
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- Title: RADIANT Source Code
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URL: https://github.com/radiant-network
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AuthorName: RADIANT Team
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AuthorURL: https://github.com/radiant-network
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- Title: CAVATICA
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URL: http://cavatica.org
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AuthorName: Seven Bridges Genomics
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AuthorURL: http://www.sevenbridges.com
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- Title: PedcBioPortal
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URL: https://pedcbioportal.kidsfirstdrc.org
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AuthorName: cBioPortal
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AuthorURL: https://www.cbioportal.org/
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- Title: Flywheel (CHOP D3b)
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URL: https://chop.flywheel.io
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AuthorName: Flywheel
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AuthorURL: https://flywheel.io/
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Publications:
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- Title: "The children's brain tumor network (CBTN) - Accelerating research in pediatric central nervous system tumors through collaboration and open science."
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URL: https://pubmed.ncbi.nlm.nih.gov/36335802/
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AuthorName: Jena V Lilly, Jo Lynne Rokita, Jennifer L Mason, et al.
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- Title: "The landscape of primary mismatch repair deficient gliomas in children, adolescents, and young adults: a multi-cohort study"
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URL: https://pubmed.ncbi.nlm.nih.gov/39701117/
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AuthorName: Logine Negm, Jiil Chung, Liana Nobre, et al.
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- Title: "Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma"
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URL: https://pubmed.ncbi.nlm.nih.gov/39747214/
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AuthorName: Anahita Gathi Kazerooni, Adam Kraya, Komal S Rathi, Meen Chul Kim, et al.
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- Title: "Multi-scale signaling and tumor evolution in high-grade gliomas"
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URL: https://pubmed.ncbi.nlm.nih.gov/38981438/
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AuthorName: Jingxian Liu, Song Cao, Kathleen J Imback, et al.
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- Title: "Germline analysis of an international cohort of pediatric diffuse midline glioma patients"
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URL: https://pubmed.ncbi.nlm.nih.gov/40072012/
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AuthorName: Marion K Mateos, Pamela Ajuyah, Noemi Fuentes-Bolanos, et al.
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- Title: "A road map for the treatment of pediatric diffuse midline glioma"
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URL: https://pubmed.ncbi.nlm.nih.gov/38039965/
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AuthorName: Carl Koschmann, Wajd N Al-Holou, Marta M Alonso, et al.
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- Title: "Use of External Control Cohorts in Pediatric Brain Tumor Clinical Trials"
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URL: https://pubmed.ncbi.nlm.nih.gov/38394473/
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AuthorName: Ashley S Margol, Annette M Molinaro, Arzu Onar-Thomas, et al.
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- Title: "OpenPBTA: The Open Pediatric Brain Tumor Atlas"
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URL: https://pubmed.ncbi.nlm.nih.gov/37492101/
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AuthorName: Joshua A Shapiro, Krutika S Gaonkar, Stephanie J Spielman, et al.
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- Title: "Generation and multi-dimensional profiling of a childhood cancer cell line atlas defines new therapeutic opportunities"
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URL: https://pubmed.ncbi.nlm.nih.gov/37001527/
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AuthorName: Claire Xin Sun, Paul Daniel, Gabrielle Bradshaw et al.

datasets/rcm-ceos-ard.yaml

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Region: ca-central-1
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Type: S3 Bucket
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Explore:
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- '[EODMS STAC for RCM CEOS ARD](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/collections/rcm-ard/items/)'
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- '[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)'
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DataAtWork:
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Tutorials:
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- 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
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URL: https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM
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AuthorName: European Space Agency (ESA)
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AuthorURL: https://www.esa.int/
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- Title: RCM CEOS ARD Dataset on GEO.ca | Ensemble de données RCM CEOS ARD sur GEO.ca
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URL: https://app.geo.ca/en-ca/map-browser/record/eodms-rcm-ard
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AuthorName: Canada Centre for Remote Sensing | Centre canadien de télédétection
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AuthorURL: https://natural-resources.canada.ca/science-data/science-research/research-centres/canada-centre-remote-sensing

datasets/roa.yaml

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Name: Rain over Africa
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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.
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Documentation: https://github.com/SEE-GEO/roa
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Contact: https://github.com/SEE-GEO/roa
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ManagedBy: "[Geoscience and Remote Sensing at Chalmers University of Technology](https://www.chalmers.se/en/departments/see/research/geo)"
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UpdateFrequency: At most, yearly
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Tags:
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- aws-pds
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- agriculture
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- analysis ready data
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- atmosphere
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- aws-pds
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- climate
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- deep learning
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- earth observation
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- geophysics
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- geoscience
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- hydrology
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- machine learning
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- precipitation
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- satellite imagery
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- weather
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- zarr
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License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)"
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Citation: "Please refer to https://github.com/SEE-GEO/roa#5-how-to-cite for instructions on how to cite the RoA data."
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Resources:
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- Description: RoA expected rain rate and quantiles at levels 5%, 16%, 25%, 50%, 75%, 84%, and 95% in Zarr format
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ARN: arn:aws:s3:::rainoverafrica
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Region: us-west-2
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Type: S3 Bucket
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- Description: Notifications for new Rain over Africa data
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ARN: arn:aws:sns:us-west-2:261854712492:rainoverafrica-object_created
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Region: us-west-2
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Type: SNS Topic
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DataAtWork:
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Tutorials:
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- Title: Reading RoA data
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URL: https://github.com/SEE-GEO/roa?tab=readme-ov-file#22-reading-roa-data
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AuthorName: Adrià Amell
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Services:
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- Amazon S3
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- Title: How to use the data
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URL: https://github.com/SEE-GEO/roa?tab=readme-ov-file#3-how-to-use-the-data
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AuthorName: Adrià Amell
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Services:
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- Amazon S3
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Publications:
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- Title: Probabilistic near real-time retrievals of Rain over Africa using deep learning
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URL: https://doi.org/10.1029/2025JD044595
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AuthorName: Adrià Amell, Lilian Hee, Simon Pfreundschuh, and Patrick Eriksson
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DeprecatedNotice:
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ADXCategories:
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- Environmental Data

datasets/sentinel-products-ca-mirror.yaml

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Region: ca-central-1
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Type: S3 Bucket
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Explore:
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- '[EODMS STAC for Sentinel products](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/)'
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- '[STAC for Sentinel products](https://radiantearth.github.io/stac-browser/#/external/www.eodms-sgdot.nrcan-rncan.gc.ca/stac/collections/sentinel-1)'
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