100+ datasets found
  1. m

    Download CSV DB

    • maclookup.app
    json
    Updated Jul 10, 2025
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    (2025). Download CSV DB [Dataset]. https://maclookup.app/downloads/csv-database
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 10, 2025
    Description

    Free, daily updated MAC prefix and vendor CSV database. Download now for accurate device identification.

  2. GitTables 1M - CSV files

    • zenodo.org
    zip
    Updated Jun 6, 2022
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    Madelon Hulsebos; Çağatay Demiralp; Paul Groth; Madelon Hulsebos; Çağatay Demiralp; Paul Groth (2022). GitTables 1M - CSV files [Dataset]. http://doi.org/10.5281/zenodo.6515973
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 6, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Madelon Hulsebos; Çağatay Demiralp; Paul Groth; Madelon Hulsebos; Çağatay Demiralp; Paul Groth
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This dataset contains >800K CSV files behind the GitTables 1M corpus.

    For more information about the GitTables corpus, visit:

    - our website for GitTables, or

    - the main GitTables download page on Zenodo.

  3. EPA FRS Facilities Combined File CSV Download for the State of Wyoming

    • catalog.data.gov
    Updated Nov 29, 2020
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    U.S. EPA Office of Environmental Information (OEI) - Office of Information Collection (OIC) (2020). EPA FRS Facilities Combined File CSV Download for the State of Wyoming [Dataset]. https://catalog.data.gov/dataset/epa-frs-facilities-combined-file-csv-download-for-the-state-of-wyoming
    Explore at:
    Dataset updated
    Nov 29, 2020
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Area covered
    Wyoming
    Description

    The Facility Registry System (FRS) identifies facilities, sites, or places subject to environmental regulation or of environmental interest to EPA programs or delegated states. Using vigorous verification and data management procedures, FRS integrates facility data from program national systems, state master facility records, tribal partners, and other federal agencies and provides the Agency with a centrally managed, single source of comprehensive and authoritative information on facilities.

  4. CSV file used in statistical analyses

    • data.csiro.au
    • researchdata.edu.au
    • +1more
    Updated Oct 13, 2014
    + more versions
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    CSIRO (2014). CSV file used in statistical analyses [Dataset]. http://doi.org/10.4225/08/543B4B4CA92E6
    Explore at:
    Dataset updated
    Oct 13, 2014
    Dataset authored and provided by
    CSIROhttp://www.csiro.au/
    License

    https://research.csiro.au/dap/licences/csiro-data-licence/https://research.csiro.au/dap/licences/csiro-data-licence/

    Time period covered
    Mar 14, 2008 - Jun 9, 2009
    Dataset funded by
    CSIROhttp://www.csiro.au/
    Description

    A csv file containing the tidal frequencies used for statistical analyses in the paper "Estimating Freshwater Flows From Tidally-Affected Hydrographic Data" by Dan Pagendam and Don Percival.

  5. c

    BevMo Alcoholic Beverage Records Extracted - Download Comprehensive CSV...

    • crawlfeeds.com
    csv, zip
    Updated Sep 7, 2024
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    Crawl Feeds (2024). BevMo Alcoholic Beverage Records Extracted - Download Comprehensive CSV Dataset Now [Dataset]. https://crawlfeeds.com/datasets/bevmo-alcoholic-beverage-records-extracted-download-comprehensive-csv-dataset-now
    Explore at:
    csv, zipAvailable download formats
    Dataset updated
    Sep 7, 2024
    Dataset authored and provided by
    Crawl Feeds
    License

    https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy

    Description

    We are excited to announce that we have successfully extracted a comprehensive set of alcoholic beverage records from BevMo and compiled them into a CSV file.

    This meticulously organized dataset includes key information such as product URLs, IDs, names, SKUs, GTIN14 barcodes, detailed product descriptions, availability status, pricing, currency, images, breadcrumbs, and more.

    Our dataset provides an invaluable resource for anyone looking to analyze or utilize detailed BevMo product information.

    Download the dataset today and gain access to a wealth of information from one of the leading beverage retailers.

    Perfect for market analysis, e-commerce insights, and competitive research.

  6. a

    Alaska DCCED CBPL Active Business License CSV File Download

    • hub.arcgis.com
    • rural-utility-business-advisory-hub-site-1-dcced.hub.arcgis.com
    • +2more
    Updated Nov 16, 2021
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    Dept. of Commerce, Community, & Economic Development (2021). Alaska DCCED CBPL Active Business License CSV File Download [Dataset]. https://hub.arcgis.com/documents/6070036058764b96a0d37d147088e70c
    Explore at:
    Dataset updated
    Nov 16, 2021
    Dataset authored and provided by
    Dept. of Commerce, Community, & Economic Development
    Area covered
    Alaska
    Description

    Alaska DCCED Division of Corporations, Business and Professional Licensing courtesy CSV Download Link Location

  7. f

    Event Logs CSV

    • figshare.com
    rar
    Updated Dec 9, 2019
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    Dina Bayomie (2019). Event Logs CSV [Dataset]. http://doi.org/10.6084/m9.figshare.11342063.v1
    Explore at:
    rarAvailable download formats
    Dataset updated
    Dec 9, 2019
    Dataset provided by
    figshare
    Authors
    Dina Bayomie
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The event logs in CSV format. The dataset contains both correlated and uncorrelated logs

  8. e

    ATOM Download Service for the RÚIAN data of feature hierarchy by the area of...

    • data.europa.eu
    wfs
    Updated Aug 29, 2020
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    (2020). ATOM Download Service for the RÚIAN data of feature hierarchy by the area of the country - CSV format [Dataset]. https://data.europa.eu/data/datasets/cz-00025712-cuzk_atom-md_ruian-csv-hie-st
    Explore at:
    wfsAvailable download formats
    Dataset updated
    Aug 29, 2020
    Description

    Download Service provides pre-defined data on relationship between selected territorial elements and units of territorial registration using the ATOM technology. The service is publicly available and free-of-charge (data covers the whole territory of the Czech Republic) and enables downloading of predefined data file containing data for the whole Czech Republic. Files are created during the first day of each month with data valid to the last day of previous month. The whole dataset (7 files) is compressed (ZIP) for downloading.

  9. f

    Export Excel fieldbook to csv-file

    • figshare.com
    mp4
    Updated Jul 6, 2016
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    Wouter Marra (2016). Export Excel fieldbook to csv-file [Dataset]. http://doi.org/10.6084/m9.figshare.3472199.v1
    Explore at:
    mp4Available download formats
    Dataset updated
    Jul 6, 2016
    Dataset provided by
    figshare
    Authors
    Wouter Marra
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Screencast on how to export field observations with gps coordinates in Excel to a .csv file.

  10. Customer Dataset csv

    • kaggle.com
    Updated Mar 22, 2023
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    Moses Moncy (2023). Customer Dataset csv [Dataset]. https://www.kaggle.com/datasets/mosesmoncy/customer-dataset-csv
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 22, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Moses Moncy
    Description

    Dataset

    This dataset was created by Moses Moncy

    Contents

  11. q

    Dataset in CSV format

    • data.researchdatafinder.qut.edu.au
    Updated Jun 18, 2019
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    (2019). Dataset in CSV format [Dataset]. https://data.researchdatafinder.qut.edu.au/dataset/occupational-head-dose/resource/3502742e-7a1e-4995-8da3-1f40f8362920
    Explore at:
    Dataset updated
    Jun 18, 2019
    License

    http://researchdatafinder.qut.edu.au/display/n6106http://researchdatafinder.qut.edu.au/display/n6106

    Description

    QUT Research Data Respository Dataset Resource available for download

  12. EPA FRS Facilities Single File CSV Download for the State of Arkansas

    • catalog.data.gov
    Updated Nov 29, 2020
    + more versions
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    U.S. EPA Office of Environmental Information (OEI) - Office of Information Collection (OIC) (2020). EPA FRS Facilities Single File CSV Download for the State of Arkansas [Dataset]. https://catalog.data.gov/dataset/epa-frs-facilities-single-file-csv-download-for-the-state-of-arkansas
    Explore at:
    Dataset updated
    Nov 29, 2020
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Area covered
    Arkansas
    Description

    The Facility Registry System (FRS) identifies facilities, sites, or places subject to environmental regulation or of environmental interest to EPA programs or delegated states. Using vigorous verification and data management procedures, FRS integrates facility data from program national systems, state master facility records, tribal partners, and other federal agencies and provides the Agency with a centrally managed, single source of comprehensive and authoritative information on facilities.

  13. The Canada Trademarks Dataset

    • zenodo.org
    pdf, zip
    Updated Jul 19, 2024
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    Jeremy Sheff; Jeremy Sheff (2024). The Canada Trademarks Dataset [Dataset]. http://doi.org/10.5281/zenodo.4999655
    Explore at:
    zip, pdfAvailable download formats
    Dataset updated
    Jul 19, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Jeremy Sheff; Jeremy Sheff
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Canada
    Description

    The Canada Trademarks Dataset

    18 Journal of Empirical Legal Studies 908 (2021), prepublication draft available at https://papers.ssrn.com/abstract=3782655, published version available at https://onlinelibrary.wiley.com/share/author/CHG3HC6GTFMMRU8UJFRR?target=10.1111/jels.12303

    Dataset Selection and Arrangement (c) 2021 Jeremy Sheff

    Python and Stata Scripts (c) 2021 Jeremy Sheff

    Contains data licensed by Her Majesty the Queen in right of Canada, as represented by the Minister of Industry, the minister responsible for the administration of the Canadian Intellectual Property Office.

    This individual-application-level dataset includes records of all applications for registered trademarks in Canada since approximately 1980, and of many preserved applications and registrations dating back to the beginning of Canada’s trademark registry in 1865, totaling over 1.6 million application records. It includes comprehensive bibliographic and lifecycle data; trademark characteristics; goods and services claims; identification of applicants, attorneys, and other interested parties (including address data); detailed prosecution history event data; and data on application, registration, and use claims in countries other than Canada. The dataset has been constructed from public records made available by the Canadian Intellectual Property Office. Both the dataset and the code used to build and analyze it are presented for public use on open-access terms.

    Scripts are licensed for reuse subject to the Creative Commons Attribution License 4.0 (CC-BY-4.0), https://creativecommons.org/licenses/by/4.0/. Data files are licensed for reuse subject to the Creative Commons Attribution License 4.0 (CC-BY-4.0), https://creativecommons.org/licenses/by/4.0/, and also subject to additional conditions imposed by the Canadian Intellectual Property Office (CIPO) as described below.

    Terms of Use:

    As per the terms of use of CIPO's government data, all users are required to include the above-quoted attribution to CIPO in any reproductions of this dataset. They are further required to cease using any record within the datasets that has been modified by CIPO and for which CIPO has issued a notice on its website in accordance with its Terms and Conditions, and to use the datasets in compliance with applicable laws. These requirements are in addition to the terms of the CC-BY-4.0 license, which require attribution to the author (among other terms). For further information on CIPO’s terms and conditions, see https://www.ic.gc.ca/eic/site/cipointernet-internetopic.nsf/eng/wr01935.html. For further information on the CC-BY-4.0 license, see https://creativecommons.org/licenses/by/4.0/.

    The following attribution statement, if included by users of this dataset, is satisfactory to the author, but the author makes no representations as to whether it may be satisfactory to CIPO:

    The Canada Trademarks Dataset is (c) 2021 by Jeremy Sheff and licensed under a CC-BY-4.0 license, subject to additional terms imposed by the Canadian Intellectual Property Office. It contains data licensed by Her Majesty the Queen in right of Canada, as represented by the Minister of Industry, the minister responsible for the administration of the Canadian Intellectual Property Office. For further information, see https://creativecommons.org/licenses/by/4.0/ and https://www.ic.gc.ca/eic/site/cipointernet-internetopic.nsf/eng/wr01935.html.

    Details of Repository Contents:

    This repository includes a number of .zip archives which expand into folders containing either scripts for construction and analysis of the dataset or data files comprising the dataset itself. These folders are as follows:

    • /csv: contains the .csv versions of the data files
    • /do: contains Stata do-files used to convert the .csv files to .dta format and perform the statistical analyses set forth in the paper reporting this dataset
    • /dta: contains the .dta versions of the data files
    • /py: contains the python scripts used to download CIPO’s historical trademarks data via SFTP and generate the .csv data files

    If users wish to construct rather than download the datafiles, the first script that they should run is /py/sftp_secure.py. This script will prompt the user to enter their IP Horizons SFTP credentials; these can be obtained by registering with CIPO at https://ised-isde.survey-sondage.ca/f/s.aspx?s=59f3b3a4-2fb5-49a4-b064-645a5e3a752d&lang=EN&ds=SFTP. The script will also prompt the user to identify a target directory for the data downloads. Because the data archives are quite large, users are advised to create a target directory in advance and ensure they have at least 70GB of available storage on the media in which the directory is located.

    The sftp_secure.py script will generate a new subfolder in the user’s target directory called /XML_raw. Users should note the full path of this directory, which they will be prompted to provide when running the remaining python scripts. Each of the remaining scripts, the filenames of which begin with “iterparse”, corresponds to one of the data files in the dataset, as indicated in the script’s filename. After running one of these scripts, the user’s target directory should include a /csv subdirectory containing the data file corresponding to the script; after running all the iterparse scripts the user’s /csv directory should be identical to the /csv directory in this repository. Users are invited to modify these scripts as they see fit, subject to the terms of the licenses set forth above.

    With respect to the Stata do-files, only one of them is relevant to construction of the dataset itself. This is /do/CA_TM_csv_cleanup.do, which converts the .csv versions of the data files to .dta format, and uses Stata’s labeling functionality to reduce the size of the resulting files while preserving information. The other do-files generate the analyses and graphics presented in the paper describing the dataset (Jeremy N. Sheff, The Canada Trademarks Dataset, 18 J. Empirical Leg. Studies (forthcoming 2021)), available at https://papers.ssrn.com/abstract=3782655). These do-files are also licensed for reuse subject to the terms of the CC-BY-4.0 license, and users are invited to adapt the scripts to their needs.

    The python and Stata scripts included in this repository are separately maintained and updated on Github at https://github.com/jnsheff/CanadaTM.

    This repository also includes a copy of the current version of CIPO's data dictionary for its historical XML trademarks archive as of the date of construction of this dataset.

  14. UK House Price Index: data downloads March 2025

    • gov.uk
    Updated May 21, 2025
    + more versions
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    HM Land Registry (2025). UK House Price Index: data downloads March 2025 [Dataset]. https://www.gov.uk/government/statistical-data-sets/uk-house-price-index-data-downloads-march-2025
    Explore at:
    Dataset updated
    May 21, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    HM Land Registry
    Area covered
    United Kingdom
    Description

    The UK House Price Index is a National Statistic.

    Create your report

    Download the full UK House Price Index data below, or use our tool to https://landregistry.data.gov.uk/app/ukhpi?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=tool&utm_term=9.30_21_05_25" class="govuk-link">create your own bespoke reports.

    Download the data

    Datasets are available as CSV files. Find out about republishing and making use of the data.

    Full file

    This file includes a derived back series for the new UK HPI. Under the UK HPI, data is available from 1995 for England and Wales, 2004 for Scotland and 2005 for Northern Ireland. A longer back series has been derived by using the historic path of the Office for National Statistics HPI to construct a series back to 1968.

    Download the full UK HPI background file:

    Individual attributes files

    If you are interested in a specific attribute, we have separated them into these CSV files:

  15. Reference count CSV dataset of all bibliographic resources in OpenCitations...

    • figshare.com
    zip
    Updated Dec 11, 2023
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    OpenCitations ​ (2023). Reference count CSV dataset of all bibliographic resources in OpenCitations Index [Dataset]. http://doi.org/10.6084/m9.figshare.24747498.v1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Dec 11, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    OpenCitations ​
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    A CSV dataset containing the number of references of each bibliographic entity identified by an OMID in the OpenCitations Index (https://opencitations.net/index).The dataset is based on the last release of the OpenCitations Index (https://opencitations.net/download) – November 2023. The size of the zipped archive is 0.35 GB, while the size of the unzipped CSV file is 1.7 GB.The CSV dataset contains the reference count of 71,805,806 bibliographic entities. The first column (omid) lists the entities, while the second column (references) indicates the corresponding number of incoming citations.

  16. c

    Walmart Dataset

    • crawlfeeds.com
    csv, zip
    Updated Apr 26, 2025
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    Crawl Feeds (2025). Walmart Dataset [Dataset]. https://crawlfeeds.com/datasets/walmart-dataset
    Explore at:
    csv, zipAvailable download formats
    Dataset updated
    Apr 26, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

    https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy

    Description

    Walmart products sample dataset having 1000+ records in CSV format. Download monthly dataset for walmart data and it having around 100K+ records.

    Get 50% discount for all datasets. Link

  17. d

    Data Downloads GeoJSON GeoJSON Download CSV CSV Download Shapefile ZIP...

    • catalogue.data.wa.gov.au
    Updated May 18, 2020
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    (2020). Data Downloads GeoJSON GeoJSON Download CSV CSV Download Shapefile ZIP Download KML KML Download CSV CSV Download GeoJSON GeoJSON Download KML KML Download Shapefile ZIP Download ArcGIS Hub Dataset HTML Download Shapefile ZIP Download CSV CSV Download KML KML Download CSV CSV Download KML KML Download GeoJSON GeoJSON Download Shapefile ZIP Download CSV CSV Download KML KML Download GeoJSON GeoJSON Download Shapefile ZIP Download CSV CSV Download KML KML Download GeoJSON GeoJSON Download Shapefile ZIP Download CSV CSV Download KML KML Download GeoJSON GeoJSON Download Shapefile ZIP Download CSV CSV Download KML KML Download GeoJSON GeoJSON Download Shapefile ZIP Download CSV CSV Download KML KML Download GeoJSON GeoJSON Download Shapefile ZIP Download CSV CSV Download KML KML Download GeoJSON GeoJSON Download Shapefile ZIP Download CSV CSV Download KML KML Download GeoJSON GeoJSON Download Shapefile ZIP Download [Dataset]. https://catalogue.data.wa.gov.au/dataset/perth-cps2-devwa-area
    Explore at:
    Dataset updated
    May 18, 2020
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains spatial boundaries for the DevWA Area relating to the City of Perth Planning Scheme No.2.Please see https://perth.wa.gov.au/develop/planning-framework/planning-schemes and https://perth.wa.gov.au/develop/planning-framework/planning-policies-and-precinct-plans for more information regarding the City of Perth Planning Schemes. Show full description

  18. Data from: Ecosystem-Level Determinants of Sustained Activity in Open-Source...

    • zenodo.org
    application/gzip, bin +2
    Updated Aug 2, 2024
    + more versions
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    Marat Valiev; Marat Valiev; Bogdan Vasilescu; James Herbsleb; Bogdan Vasilescu; James Herbsleb (2024). Ecosystem-Level Determinants of Sustained Activity in Open-Source Projects: A Case Study of the PyPI Ecosystem [Dataset]. http://doi.org/10.5281/zenodo.1419788
    Explore at:
    bin, application/gzip, zip, text/x-pythonAvailable download formats
    Dataset updated
    Aug 2, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Marat Valiev; Marat Valiev; Bogdan Vasilescu; James Herbsleb; Bogdan Vasilescu; James Herbsleb
    License

    https://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.htmlhttps://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.html

    Description
    Replication pack, FSE2018 submission #164:
    ------------------------------------------
    
    **Working title:** Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: 
    A Case Study of the PyPI Ecosystem
    
    **Note:** link to data artifacts is already included in the paper. 
    Link to the code will be included in the Camera Ready version as well.
    
    
    Content description
    ===================
    
    - **ghd-0.1.0.zip** - the code archive. This code produces the dataset files 
     described below
    - **settings.py** - settings template for the code archive.
    - **dataset_minimal_Jan_2018.zip** - the minimally sufficient version of the dataset.
     This dataset only includes stats aggregated by the ecosystem (PyPI)
    - **dataset_full_Jan_2018.tgz** - full version of the dataset, including project-level
     statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages
     themselves, which take around 2TB.
    - **build_model.r, helpers.r** - R files to process the survival data 
      (`survival_data.csv` in **dataset_minimal_Jan_2018.zip**, 
      `common.cache/survival_data.pypi_2008_2017-12_6.csv` in 
      **dataset_full_Jan_2018.tgz**)
    - **Interview protocol.pdf** - approximate protocol used for semistructured interviews.
    - LICENSE - text of GPL v3, under which this dataset is published
    - INSTALL.md - replication guide (~2 pages)
    Replication guide
    =================
    
    Step 0 - prerequisites
    ----------------------
    
    - Unix-compatible OS (Linux or OS X)
    - Python interpreter (2.7 was used; Python 3 compatibility is highly likely)
    - R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible)
    
    Depending on detalization level (see Step 2 for more details):
    - up to 2Tb of disk space (see Step 2 detalization levels)
    - at least 16Gb of RAM (64 preferable)
    - few hours to few month of processing time
    
    Step 1 - software
    ----------------
    
    - unpack **ghd-0.1.0.zip**, or clone from gitlab:
    
       git clone https://gitlab.com/user2589/ghd.git
       git checkout 0.1.0
     
     `cd` into the extracted folder. 
     All commands below assume it as a current directory.
      
    - copy `settings.py` into the extracted folder. Edit the file:
      * set `DATASET_PATH` to some newly created folder path
      * add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` 
    - install docker. For Ubuntu Linux, the command is 
      `sudo apt-get install docker-compose`
    - install libarchive and headers: `sudo apt-get install libarchive-dev`
    - (optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools`
     Without this dependency, you might get an error on the next step, 
     but it's safe to ignore.
    - install Python libraries: `pip install --user -r requirements.txt` . 
    - disable all APIs except GitHub (Bitbucket and Gitlab support were
     not yet implemented when this study was in progress): edit
     `scraper/init.py`, comment out everything except GitHub support
     in `PROVIDERS`.
    
    Step 2 - obtaining the dataset
    -----------------------------
    
    The ultimate goal of this step is to get output of the Python function 
    `common.utils.survival_data()` and save it into a CSV file:
    
      # copy and paste into a Python console
      from common import utils
      survival_data = utils.survival_data('pypi', '2008', smoothing=6)
      survival_data.to_csv('survival_data.csv')
    
    Since full replication will take several months, here are some ways to speedup
    the process:
    
    ####Option 2.a, difficulty level: easiest
    
    Just use the precomputed data. Step 1 is not necessary under this scenario.
    
    - extract **dataset_minimal_Jan_2018.zip**
    - get `survival_data.csv`, go to the next step
    
    ####Option 2.b, difficulty level: easy
    
    Use precomputed longitudinal feature values to build the final table.
    The whole process will take 15..30 minutes.
    
    - create a folder `
  19. EPA FRS Facilities Single File CSV Download for the State of Arizona

    • catalog.data.gov
    Updated Nov 29, 2020
    + more versions
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    U.S. EPA Office of Environmental Information (OEI) - Office of Information Collection (OIC) (2020). EPA FRS Facilities Single File CSV Download for the State of Arizona [Dataset]. https://catalog.data.gov/dataset/epa-frs-facilities-single-file-csv-download-for-the-state-of-arizona
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    Dataset updated
    Nov 29, 2020
    Dataset provided by
    United States Environmental Protection Agencyhttp://www.epa.gov/
    Area covered
    Arizona
    Description

    The Facility Registry System (FRS) identifies facilities, sites, or places subject to environmental regulation or of environmental interest to EPA programs or delegated states. Using vigorous verification and data management procedures, FRS integrates facility data from program national systems, state master facility records, tribal partners, and other federal agencies and provides the Agency with a centrally managed, single source of comprehensive and authoritative information on facilities.

  20. Level Crossing Warning Bell (LCWB) Dataset

    • zenodo.org
    • data.niaid.nih.gov
    Updated May 20, 2023
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    Lorenzo De Donato; Lorenzo De Donato; Valeria Vittorini; Valeria Vittorini; Francesco Flammini; Francesco Flammini; Stefano Marrone; Stefano Marrone (2023). Level Crossing Warning Bell (LCWB) Dataset [Dataset]. http://doi.org/10.5281/zenodo.7945412
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    Dataset updated
    May 20, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Lorenzo De Donato; Lorenzo De Donato; Valeria Vittorini; Valeria Vittorini; Francesco Flammini; Francesco Flammini; Stefano Marrone; Stefano Marrone
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Acknowledgement
    These data are a product of a research activity conducted in the context of the RAILS (Roadmaps for AI integration in the raiL Sector) project which has received funding from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 881782 Rails. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the Shift2Rail JU members other than the Union.

    Disclaimers
    The information and views set out in this document are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this document. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein.

    This "dataset" has been created for scientific purposes only - and WITHOUT ANY COMMERCIAL purposes - to study the potentials of Deep Learning and Transfer Learning approaches. We are NOT re-distributing any video or audio; our files just contain pointers and indications needed to reproduce our study. The authors DO NOT ASSUME any responsibility for the use that other researchers or users will make of these data.

    General Info
    The CSV files contained in this folder (and subfolders) compose the Level Crossing (LC) Warning Bell (WB) Dataset.

    When using any of these data, please mention:

    De Donato, L., Marrone, S., Flammini, F., Sansone, C., Vittorini, V., Nardone, R., Mazzariello, C., and Bernaudine, F., "Intelligent Detection of Warning Bells at Level Crossings through Deep Transfer Learning for Smarter Railway Maintenance", Engineering Applications of Artificial Intelligence, Elsevier, 2023

    Content of the folder
    This folder contains the following subfolders and files.

    "Data Files" contains all the CSV files related to the data composing the LCWB Dataset:

    • WB_data.csv (WB_labels.csv): representing data of the "Warning Bell (WB)" class;
    • NA_data.csv (NA_labels.csv): representing data of the "No Alarm (NA)" class;
    • GE_data.csv (GE_labels.csv): representing data of the "GEneric alarm (GE)" class.

    "LCWB Dataset" contains all the JSON files that show how the aforementioned data have been distributed among training, validation, and test sets:

    • IT_Distribution.json and UK_distribution.json respectively show how Italian (IT) WBs and British (UK) WBs have been distributed;
    • The same goes for NA_Distribution.json and GE_Distribution.json, which show the distribution of NA and GE data respectively;
    • DatasetDistribution.json simply incorporates the content of the aforementioned JSON files in a unique file that can be exploited to obtain exactly the same dataset we adopted in our analyses.

    "Additional Files" contains some CSV files related to data we adopted to further test the deep neural network leveraged in the aforementioned manuscript:

    • FR_DE_data.csv (FR_DE_labels.csv): representing data that have been used to test the generalisation performances of the network we exploited on LC WBs related to countries that were not considered in the training phase.
    • Noises_data.csv (Noises_labels.csv): representing the noises that were considered to study the behaviour of the network in case of noisy data.

    CSV Files Structure
    Each "XX_labels.csv" file contains, for each entry, the following information:

    • The identifier ("index") of the sub-class (which is not relevant in our case);
    • The code-name ("mid") of the class, which is used in the "XX_data.csv" file to indicate the sub-class of a specific audio;
    • The extended name of the class ("display_name").

    Worth mentioning, sub-classes do not have a specific purpose in our task. They have been kept to maintain as much as possible the structure of the "class_labels_indices.csv" file provided by AudioSet. The same applies to the "XX_data.csv" files, which have roughly the same structures of "Evaluation", "Balanced train", and "Unbalanced train" AudioSet CSV files.

    Indeed, each "XX_data.csv" file contains, for each entry, the following information:

    • ID: the identifier of the entry;
    • YTID: the YouTube identifier of the video;
    • start_seconds and end_seconds: which delimit the portion of audio (extracted from YTID) which is of interest for this task;
    • positive_labels: the label(s) associated with the audio.


    Credits
    The structure of the CSV files contained in this dataset, as well as part of their content, was inspired by the CSV files composing the AudioSet dataset which is made available by Google Inc. under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, while its ontology is available under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

    Particularly, from AudioSet, we retrieved:

    • The structure of the CSV files as discussed above.
    • Data contained in GE_data.csv (which is a minimal portion of data made available by AudioSet) as well as the related 19 classes (in GE_labels.csv) which we selected among the hundreds of classes included in the AudioSet ontology.

    Pointers contained in "XX_data.csv" files other than GE_data.csv have been retrieved manually from scratch. Then, the related "XX_labels.csv" files have been created consequently.

    More about downloading the AudioSet dataset can be found here.

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(2025). Download CSV DB [Dataset]. https://maclookup.app/downloads/csv-database

Download CSV DB

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jsonAvailable download formats
Dataset updated
Jul 10, 2025
Description

Free, daily updated MAC prefix and vendor CSV database. Download now for accurate device identification.

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