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‎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/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:

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