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‎datasets/allen-hmba-releases.yaml‎

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Name: Human and Mammalian Brain Atlas
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Description:
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Human and Mammalian Brain Atlas (HMBA) is a major atlas of the BRAIN Initiative Cell Atlas Network (BICAN) that proposes to establish a comprehensive,
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highly granular cell atlas in complete adult human, macaque, and marmoset brains that links brain structure, function and cellular architecture.
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Release artifacts have been made available in this OpenData bucket to enable utilization along with their paper publications by the neuroscience community.
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Documentation: https://portal.brain-map.org/explore/hmba
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Contact: [email protected]
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ManagedBy: "[Allen Institute](http://www.alleninstitute.org/)"
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UpdateFrequency: Never
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Tags:
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- aws-pds
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- biology
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- gene expression
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- neurobiology
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- life sciences
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- single-cell transcriptomics
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- Mus musculus
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- Homo sapiens
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- non-human primate
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License: http://www.alleninstitute.org/legal/terms-use/
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Citation:
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Resources:
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- Description: Project data files in a public bucket
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ARN: arn:aws:s3:::allen-hmba-releases
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Region: us-west-2
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Type: S3 Bucket
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DataAtWork:
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Tutorials:
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- Title: Human-Mammalian Brain - Basal Ganglia - Data
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URL: https://alleninstitute.github.io/abc_atlas_access/descriptions/HMBA-BG_dataset.html
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AuthorName: Allen Institute for Brain Science
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AuthorURL: www.alleninstitute.org
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- Title: Human-Mammalian Brain - CCF Book
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URL: https://alleninstitute.github.io/CCF-MAP/
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AuthorName: Allen Institute for Brain Science
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AuthorURL: www.alleninstitute.org
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Tools & Applications:
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- Title: HMBA Basal Ganglia resources in Brain Knowledge Platform's Data Catalog
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URL: https://knowledge.brain-map.org/data/POZ2HCPBT60DSDJ8UA7
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AuthorName: Allen Institute for Brain Science
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AuthorURL: www.alleninstitute.org
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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/braidyn-bc_cued-lever-pull.yaml‎

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Name: "BraiDyn-BC: Cued lever-pull task dataset"
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Description: |
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The BraiDyn-BC (Brain Dynamics underlying emergence of Behavioral Change) Database offers an extensive, multimodal dataset that links
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wide-field calcium imaging of the mouse neocortex to comprehensive behavioral measurements during a behavioral task.
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As one of the contents in this database, we newly provide a dataset that includes 15 sessions spanning two weeks of motor skill learning,
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in which 25 mice were trained to pull a lever to obtain water rewards.
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Simultaneous high-speed videography captures body, facial, and eye movements, and environmental parameters are monitored.
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The dataset also features resting-state cortical activity and sensory-evoked responses, enhancing its utility for both learning-related and
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sensory-driven neural dynamics studies.
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Data are formatted in accordance with the Neurodata Without Borders (NWB) standard, ensuring compatibility with existing analysis tools and
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adherence to the FAIR principles.
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This resource enables in-depth investigations into the neural mechanisms underlying behavior and learning.
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The platform encourages collaborative research, supporting the exploration of rapid within-session learning effects, long-term behavioral adaptations, and neural circuit dynamics.
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Documentation: https://doi.org/10.1101/2025.02.03.631599
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Contact: "Ken Nakae ([email protected])"
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ManagedBy: "[BraiDyn-BC Database Project](https://boatneck-weeder-7b7.notion.site/BraiDyn-BC-Database-303cf08c89f94d81bb2eaed4c3c50345)"
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UpdateFrequency: NA
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Tags:
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- Mus musculus
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- neuroscience
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- calcium imaging
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- video
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- imaging
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- life sciences
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- aws-pds
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License: Creative Commons Attribution 4.0 International (CC-BY 4.0)
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Resources:
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- Description: BraiDyn-BC - Cued lever-pull task dataset
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ARN: arn:aws:s3:::braidyn-bc-buckets
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Region: ap-northeast-1
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Type: S3 Bucket
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DataAtWork:
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Tutorials:
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- Title: Detailed usage tutorials on Google Colab
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URL: https://drive.google.com/drive/folders/1QciTJd3tXkEGhz6782czB2dEO3fafm8M
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AuthorName: Keisuke Sehara
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AuthorURL: https://orcid.org/0000-0003-4368-8143
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Tools & Applications:
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- Title: A set of libraries used for generating the dataset
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URL: https://github.com/BraiDyn-BC/bdbc-data-pipeline
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AuthorName: Keisuke Sehara, Ryo Aoki, Shoya Sugimoto
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Publications:
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- Title: A multimodal dataset linking wide-field calcium imaging to behavior changes in mice during an operant lever-pull task
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URL: https://doi.org/10.1101/2025.02.03.631599
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AuthorName: Kondo M, Sehara K, Harukuni R, Aoki R, Sugimoto S, Tanaka YR, Matsuzaki M, Nakae K
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AuthorURL:
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ADXCategories:
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- Healthcare & Life Sciences Data

‎datasets/epa-hourly-prognostic-meteorology.yaml‎

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Name: >-
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EPA Hourly Prognostic Meteorological Data
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Description: >-
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The data are hourly outputs from the Weather Research and Forecasting (WRF) model
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generated by the EPA's Office of Air Quality Planning and Standards, Air Quality
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Assessment Division, Air Quality Modeling Group. These data were generated at a 12-km
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resolution over the Continental United States (12US), beginning for the year 2021 and
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continuing annually through 2023. These files are intended for use in a broad range of
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air quality applications, but specifically may be used in dispersion modeling applications
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that would benefit from the use of the Mesoscale Model Interface (MMIF) tool
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(https://www.epa.gov/scram/air-quality-dispersion-modeling-related-model-support-programs#mmif)
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which translates prognostic meteorological data into formats suitable for use with AERMOD,
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CALPUFF, or SCICHEM. The individual files are less than 1GB in size, which allows for
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the use of the MMIF tool in a Windows environment. These data are anticipated to be updated
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annually so the 3 most-recent years are available for use. Additionally, model-observation
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paired files are included to aid in the performance evaluation that is necessary for use
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of these data in regulatory applications per Appendix W to 40 CFR Part 51.
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Documentation: >-
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2022 WRF Modeling TSD:
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https://bit.ly/2022WRF
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Contact: [email protected]
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ManagedBy: U.S. Environmental Protection Agency (https://www.epa.gov)
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UpdateFrequency: Annually
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Tags:
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- aws-pds
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- environmental
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- air quality
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- regulatory
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- weather
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- meteorological
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License: >-
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These datasets are products of the U.S. Government and are intended for public
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access and use. Unless otherwise specified, all data produced by the U.S EPA
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is, by default, in the public domain and are not subject to domestic copyright
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protection under 17 U.S.C. § 105. More details on the U.S. Public Domain
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license are available here: http://www.usa.gov/publicdomain/label/1.0/
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Citation: >-
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WRF Modeling:
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US EPA, 2024, "Meteorological Model Performance for Annual 2022 Simulation
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WRF v4.4.2"
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Resources:
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- Description: >-
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The WRF output are stored as uncompressed netcdf/hdf5 formatted files in
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directories corresponding to the specific years of interest. The model-obs
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paired files are stored as comma-delimited files in the year-specific
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directories.
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ARN: 'arn:aws:s3:::epa-hourly-prognostic-meteorology'
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Region: us-east-1
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Type: S3 Bucket
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Explore:
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- '[Browse Bucket](https://epa-hourly-prognostic-meteorology.s3.amazonaws.com/index.html)'
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- Description: Notification for the EPA Hourly Prognostic Meteorological Data bucket
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ARN: 'arn:aws:sns:us-east-1:127085394039:epa-hourly-prognostic-meteorology-object_created'
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Region: us-east-1
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Type: SNS Topic

‎datasets/kreppref.yaml‎

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Name: Reference Indexes for krepp
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Description: krepp is an alignment-free method for estimating distances and phylogenetic placement of individual reads to many thousands of reference genomes in a scalable manner using k-mers. This dataset includes k-mer-based indexes consisting of ultra-large reference genome sets that can be efficiently analyzed using krepp.
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Documentation: https://github.com/bo1929/krepp/wiki/Available-reference-indexes
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Contact: https://github.com/bo1929/krepp/issues
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ManagedBy: Mirarab Lab at UC San Diego
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UpdateFrequency: Quarterly or as new data becomes available
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Tags:
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- bioinformatics
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- metagenomics
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- microbiome
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- reference index
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- aws-pds
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- life sciences
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License: GPL-3.0 license. Use of the data should be cited in the usual way, following https://github.com/bo1929/krepp/tree/master?tab=readme-ov-file#citation.
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Resources:
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- Description: This dataset contains genomic indexes for various reference datasets in binary format. Using krepp, you can perform distance estimation and phylogenetic placement with respect to these indexes.
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ARN: arn:aws:s3:::kreppref
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Region: us-west-1
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Type: S3 Bucket
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DataAtWork:
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Tutorials:
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- Title: Tutorial for using krepp indexes for metagenomic sequence analysis.
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URL: https://github.com/bo1929/krepp/wiki/Tutorial
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AuthorName: Ali Osman Berk Sapci
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AuthorURL: https://bo1929.github.io/
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Publications:
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- Title: A k-mer-based maximum likelihood method for estimating distances of reads to genomes enables genome-wide phylogenetic placement.
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URL: https://www.biorxiv.org/content/10.1101/2025.01.20.633730v2
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AuthorName: Sapci et al. (2024)

‎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/nrel-pds-ncdb.yaml‎

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The NCDB seeks to maintain the inherent relationship between the various parameters
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that are needed to model solar, wind, hydrology and load and provide data for multiple
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important climate scenarios.
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Documentation: https://nsrdb.nrel.gov/
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Contact: [email protected]
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ManagedBy: '[National Renewable Energy Laboratory](https://www.nrel.gov/)'
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Explore:
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- '[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fncdb%2F)'
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DataAtWork:
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Tutorials:
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Tools & Applications:
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- Title: NCDB Website
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URL: https://climate.nrel.gov
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AuthorName: NREL NCDB Team
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- Title: HSDS Examples
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URL: https://github.com/NREL/hsds-examples
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AuthorName: Caleb Phillips, Caroline Draxl, John Readey, Jordan Perr-Sauer, Michael Rossol
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- Title: NCDB HSDS Examples
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URL: https://github.com/NREL/hsds-examples/blob/master/notebooks/10_NCDB_introduction.ipynb
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AuthorName: Reid Olson
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Publications:
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- Title: Regridding uncertainty for statistical downscaling of solar radiation
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URL: https://ascmo.copernicus.org/articles/9/103/2023/

‎datasets/ont_basemod_data.yaml‎

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Name: ONT Methylation Benchmarking Datasets
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Description: ONT Methylation Benchmarking Datasets are generated to benchmark existing methylation-calling tools on the Oxford Nanopore sequencing platform using their recent R10.4.1 flowcell chemistry. It spans a diverse range of species, including bacteria (E. coli, H. pylori J99, H. pylori 26695, A. variabilis, T. denticola), plants (Rice, Arabidopsis), and mammals (mouse, human).In addition, the dataset includes EMSeq data for E. coli, plant, and mouse samples, which can serve as ground truth for methylation studies. It also provides unmethylated whole-genome amplified (WGA) DNA for H. pylori 26695 and a dam- dcm- double mutant (DM) of E. coli that lacks canonical 5mC and 6mA methylation. These variants, together with their wild-type counterparts, offer value for both training and benchmarking DNA methylation calling models.
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Documentation: https://github.com/SowpatiLab/ont-basemod-benchmark-data/blob/main/documentation.md
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Contact: "[email protected]; [email protected]"
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ManagedBy: "[CSIR-Centre for Cellular and Molecular Biology](https://www.ccmb.res.in/)"
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UpdateFrequency: Datasets will be updated periodically as additional data is generated.
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Tags:
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- aws-pds
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- life sciences
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- genomic
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- long read sequencing
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- bioinformatics
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- epigenomics
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- benchmark
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- bam
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License: "[MIT License](https://opensource.org/license/mit)"
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Citation: "Please cite Kulkarni et al. Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation. bioRxiv (2024). doi: https://doi.org/10.1101/2024.11.09.622763 when referencing the ONT methylation benchmarking datasets in publications."
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Resources:
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- Description: ONT Methylation Benchmarking Datasets
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ARN: arn:aws:s3:::ont-basemod-benchmark-data
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Region: ap-south-1
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Type: S3 Bucket
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Explore:
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- '[Browse Bucket](https://ont-basemod-benchmark-data.s3.amazonaws.com/index.html)'
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- Description: Notifications for object created
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ARN: arn:aws:sns:ap-south-1:767415906609:ont-basemod-benchmark-data-object_created
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Region: ap-south-1
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Type: SNS Topic
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DataAtWork:
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Tutorials:
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- Title: Methylation calling using ONT methylation benchmarking dataset
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URL: https://github.com/SowpatiLab/ont-basemod-benchmark-data/blob/main/tutorial.md
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AuthorName: Onkar Kulkarni
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Publications:
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- Title: Comprehensive benchmarking of tools for nanopore-based detection of DNA methylation
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URL: https://www.biorxiv.org/content/10.1101/2024.11.09.622763v1
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AuthorName: Kulkarni 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

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