100+ datasets found
  1. 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.

  2. 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.

  3. Raw Data - CSV Files

    • osf.io
    Updated Apr 27, 2020
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    Katelyn Conn (2020). Raw Data - CSV Files [Dataset]. https://osf.io/h5wbt
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    Dataset updated
    Apr 27, 2020
    Dataset provided by
    Center for Open Sciencehttps://cos.io/
    Authors
    Katelyn Conn
    Description

    Raw Data in .csv format for use with the R data wrangling scripts.

  4. 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

  5. B

    Residential School Locations Dataset (CSV Format)

    • borealisdata.ca
    • search.dataone.org
    Updated Jun 5, 2019
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    Rosa Orlandini (2019). Residential School Locations Dataset (CSV Format) [Dataset]. http://doi.org/10.5683/SP2/RIYEMU
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 5, 2019
    Dataset provided by
    Borealis
    Authors
    Rosa Orlandini
    License

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

    Time period covered
    Jan 1, 1863 - Jun 30, 1998
    Area covered
    Canada
    Description

    The Residential School Locations Dataset [IRS_Locations.csv] contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites.

  6. Datasets for Sentiment Analysis

    • zenodo.org
    csv
    Updated Dec 10, 2023
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    Julie R. Repository creator - Campos Arias; Julie R. Repository creator - Campos Arias (2023). Datasets for Sentiment Analysis [Dataset]. http://doi.org/10.5281/zenodo.10157504
    Explore at:
    csvAvailable download formats
    Dataset updated
    Dec 10, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Julie R. Repository creator - Campos Arias; Julie R. Repository creator - Campos Arias
    License

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

    Description

    This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

    Below are the datasets specified, along with the details of their references, authors, and download sources.

    ----------- STS-Gold Dataset ----------------

    The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

    Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

    File name: sts_gold_tweet.csv

    ----------- Amazon Sales Dataset ----------------

    This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

    Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

    Features:

    • product_id - Product ID
    • product_name - Name of the Product
    • category - Category of the Product
    • discounted_price - Discounted Price of the Product
    • actual_price - Actual Price of the Product
    • discount_percentage - Percentage of Discount for the Product
    • rating - Rating of the Product
    • rating_count - Number of people who voted for the Amazon rating
    • about_product - Description about the Product
    • user_id - ID of the user who wrote review for the Product
    • user_name - Name of the user who wrote review for the Product
    • review_id - ID of the user review
    • review_title - Short review
    • review_content - Long review
    • img_link - Image Link of the Product
    • product_link - Official Website Link of the Product

    License: CC BY-NC-SA 4.0

    File name: amazon.csv

    ----------- Rotten Tomatoes Reviews Dataset ----------------

    This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

    This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

    Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

    File name: data_rt.csv

    ----------- Preprocessed Dataset Sentiment Analysis ----------------

    Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
    Stemmed and lemmatized using nltk.
    Sentiment labels are generated using TextBlob polarity scores.

    The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

    DOI: 10.34740/kaggle/dsv/3877817

    Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

    This dataset was used in the experimental phase of my research.

    File name: EcoPreprocessed.csv

    ----------- Amazon Earphones Reviews ----------------

    This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

    This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

    The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

    License: U.S. Government Works

    Source: www.amazon.in

    File name (original): AllProductReviews.csv (contains 14337 reviews)

    File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

    ----------- Amazon Musical Instruments Reviews ----------------

    This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

    This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

    The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

    Source: http://jmcauley.ucsd.edu/data/amazon/

    File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

    File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

  7. 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

  8. H

    Dataset metadata of known Dataverse installations, August 2023

    • dataverse.harvard.edu
    • search.dataone.org
    Updated Aug 30, 2024
    + more versions
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    Julian Gautier (2024). Dataset metadata of known Dataverse installations, August 2023 [Dataset]. http://doi.org/10.7910/DVN/8FEGUV
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 30, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Julian Gautier
    License

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

    Description

    This dataset contains the metadata of the datasets published in 85 Dataverse installations and information about each installation's metadata blocks. It also includes the lists of pre-defined licenses or terms of use that dataset depositors can apply to the datasets they publish in the 58 installations that were running versions of the Dataverse software that include that feature. The data is useful for reporting on the quality of dataset and file-level metadata within and across Dataverse installations and improving understandings about how certain Dataverse features and metadata fields are used. Curators and other researchers can use this dataset to explore how well Dataverse software and the repositories using the software help depositors describe data. How the metadata was downloaded The dataset metadata and metadata block JSON files were downloaded from each installation between August 22 and August 28, 2023 using a Python script kept in a GitHub repo at https://github.com/jggautier/dataverse-scripts/blob/main/other_scripts/get_dataset_metadata_of_all_installations.py. In order to get the metadata from installations that require an installation account API token to use certain Dataverse software APIs, I created a CSV file with two columns: one column named "hostname" listing each installation URL in which I was able to create an account and another column named "apikey" listing my accounts' API tokens. The Python script expects the CSV file and the listed API tokens to get metadata and other information from installations that require API tokens. How the files are organized ├── csv_files_with_metadata_from_most_known_dataverse_installations │ ├── author(citation)_2023.08.22-2023.08.28.csv │ ├── contributor(citation)_2023.08.22-2023.08.28.csv │ ├── data_source(citation)_2023.08.22-2023.08.28.csv │ ├── ... │ └── topic_classification(citation)_2023.08.22-2023.08.28.csv ├── dataverse_json_metadata_from_each_known_dataverse_installation │ ├── Abacus_2023.08.27_12.59.59.zip │ ├── dataset_pids_Abacus_2023.08.27_12.59.59.csv │ ├── Dataverse_JSON_metadata_2023.08.27_12.59.59 │ ├── hdl_11272.1_AB2_0AQZNT_v1.0(latest_version).json │ ├── ... │ ├── metadatablocks_v5.6 │ ├── astrophysics_v5.6.json │ ├── biomedical_v5.6.json │ ├── citation_v5.6.json │ ├── ... │ ├── socialscience_v5.6.json │ ├── ACSS_Dataverse_2023.08.26_22.14.04.zip │ ├── ADA_Dataverse_2023.08.27_13.16.20.zip │ ├── Arca_Dados_2023.08.27_13.34.09.zip │ ├── ... │ └── World_Agroforestry_-_Research_Data_Repository_2023.08.27_19.24.15.zip └── dataverse_installations_summary_2023.08.28.csv └── dataset_pids_from_most_known_dataverse_installations_2023.08.csv └── license_options_for_each_dataverse_installation_2023.09.05.csv └── metadatablocks_from_most_known_dataverse_installations_2023.09.05.csv This dataset contains two directories and four CSV files not in a directory. One directory, "csv_files_with_metadata_from_most_known_dataverse_installations", contains 20 CSV files that list the values of many of the metadata fields in the citation metadata block and geospatial metadata block of datasets in the 85 Dataverse installations. For example, author(citation)_2023.08.22-2023.08.28.csv contains the "Author" metadata for the latest versions of all published, non-deaccessioned datasets in the 85 installations, where there's a row for author names, affiliations, identifier types and identifiers. The other directory, "dataverse_json_metadata_from_each_known_dataverse_installation", contains 85 zipped files, one for each of the 85 Dataverse installations whose dataset metadata I was able to download. Each zip file contains a CSV file and two sub-directories: The CSV file contains the persistent IDs and URLs of each published dataset in the Dataverse installation as well as a column to indicate if the Python script was able to download the Dataverse JSON metadata for each dataset. It also includes the alias/identifier and category of the Dataverse collection that the dataset is in. One sub-directory contains a JSON file for each of the installation's published, non-deaccessioned dataset versions. The JSON files contain the metadata in the "Dataverse JSON" metadata schema. The Dataverse JSON export of the latest version of each dataset includes "(latest_version)" in the file name. This should help those who are interested in the metadata of only the latest version of each dataset. The other sub-directory contains information about the metadata models (the "metadata blocks" in JSON files) that the installation was using when the dataset metadata was downloaded. I included them so that they can be used when extracting metadata from the dataset's Dataverse JSON exports. The dataverse_installations_summary_2023.08.28.csv file contains information about each installation, including its name, URL, Dataverse software version, and counts of dataset metadata...

  9. 1000 Empirical Time series

    • figshare.com
    • researchdata.edu.au
    png
    Updated May 30, 2023
    + more versions
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    Ben Fulcher (2023). 1000 Empirical Time series [Dataset]. http://doi.org/10.6084/m9.figshare.5436136.v10
    Explore at:
    pngAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Ben Fulcher
    License

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

    Description

    A diverse selection of 1000 empirical time series, along with results of an hctsa feature extraction, using v1.06 of hctsa and Matlab 2019b, computed on a server at The University of Sydney.The results of the computation are in the hctsa file, HCTSA_Empirical1000.mat for use in Matlab using v1.06 of hctsa.The same data is also provided in .csv format for the hctsa_datamatrix.csv (results of feature computation), with information about rows (time series) in hctsa_timeseries-info.csv, information about columns (features) in hctsa_features.csv (and corresponding hctsa code used to compute each feature in hctsa_masterfeatures.csv), and the data of individual time series (each line a time series, for time series described in hctsa_timeseries-info.csv) is in hctsa_timeseries-data.csv. These .csv files were produced by running >>OutputToCSV(HCTSA_Empirical1000.mat,true,true); in hctsa.The input file, INP_Empirical1000.mat, is for use with hctsa, and contains the time-series data and metadata for the 1000 time series. For example, massive feature extraction from these data on the user's machine, using hctsa, can proceed as>> TS_Init('INP_Empirical1000.mat');Some visualizations of the dataset are in CarpetPlot.png (first 1000 samples of all time series as a carpet (color) plot) and 150TS-250samples.png (conventional time-series plots of the first 250 samples of a sample of 150 time series from the dataset). More visualizations can be performed by the user using TS_PlotTimeSeries from the hctsa package.See links in references for more comprehensive documentation for performing methodological comparison using this dataset, and on how to download and use v1.06 of hctsa.

  10. c

    Netflix movies and tv shows sample dataset

    • crawlfeeds.com
    csv, zip
    Updated Apr 27, 2025
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    Crawl Feeds (2025). Netflix movies and tv shows sample dataset [Dataset]. https://crawlfeeds.com/datasets/netflix-movies-and-tv-shows-sample-dataset
    Explore at:
    zip, csvAvailable download formats
    Dataset updated
    Apr 27, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

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

    Description

    Netflix is a streaming service and production company. Crawl feeds team extracted more than 100 records from netflix for quality analysis purposes. Get in touch with crawl feeds team for complete dataset. Last extracted on 5 mar 2022

  11. 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.

  12. m

    Network traffic for machine learning classification

    • data.mendeley.com
    Updated Feb 12, 2020
    + more versions
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    Víctor Labayen Guembe (2020). Network traffic for machine learning classification [Dataset]. http://doi.org/10.17632/5pmnkshffm.1
    Explore at:
    Dataset updated
    Feb 12, 2020
    Authors
    Víctor Labayen Guembe
    License

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

    Description

    The dataset is a set of network traffic traces in pcap/csv format captured from a single user. The traffic is classified in 5 different activities (Video, Bulk, Idle, Web, and Interactive) and the label is shown in the filename. There is also a file (mapping.csv) with the mapping of the host's IP address, the csv/pcap filename and the activity label.

    Activities:

    Interactive: applications that perform real-time interactions in order to provide a suitable user experience, such as editing a file in google docs and remote CLI's sessions by SSH. Bulk data transfer: applications that perform a transfer of large data volume files over the network. Some examples are SCP/FTP applications and direct downloads of large files from web servers like Mediafire, Dropbox or the university repository among others. Web browsing: contains all the generated traffic while searching and consuming different web pages. Examples of those pages are several blogs and new sites and the moodle of the university. Vídeo playback: contains traffic from applications that consume video in streaming or pseudo-streaming. The most known server used are Twitch and Youtube but the university online classroom has also been used. Idle behaviour: is composed by the background traffic generated by the user computer when the user is idle. This traffic has been captured with every application closed and with some opened pages like google docs, YouTube and several web pages, but always without user interaction.

    The capture is performed in a network probe, attached to the router that forwards the user network traffic, using a SPAN port. The traffic is stored in pcap format with all the packet payload. In the csv file, every non TCP/UDP packet is filtered out, as well as every packet with no payload. The fields in the csv files are the following (one line per packet): Timestamp, protocol, payload size, IP address source and destination, UDP/TCP port source and destination. The fields are also included as a header in every csv file.

    The amount of data is stated as follows:

    Bulk : 19 traces, 3599 s of total duration, 8704 MBytes of pcap files Video : 23 traces, 4496 s, 1405 MBytes Web : 23 traces, 4203 s, 148 MBytes Interactive : 42 traces, 8934 s, 30.5 MBytes Idle : 52 traces, 6341 s, 0.69 MBytes

  13. m

    Download CSV DB

    • maclookup.app
    json
    Updated Jun 26, 2025
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    (2025). Download CSV DB [Dataset]. https://maclookup.app/downloads/csv-database
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 26, 2025
    Description

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

  14. US Real Estate

    • zenrows.com
    csv
    Updated Jun 27, 2021
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    ZenRows (2021). US Real Estate [Dataset]. https://www.zenrows.com/datasets/us-real-estate
    Explore at:
    csv(5,8MB)Available download formats
    Dataset updated
    Jun 27, 2021
    Dataset provided by
    ZenRows S.L.
    Authors
    ZenRows
    License

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

    Area covered
    United States
    Description

    High-quality, free real estate dataset from all around the United States, in CSV format. Over 10.000 records relevant to Real Estate investors, agents, and data scientists. We are working on complete datasets from a wide variety of countries. Don't hesitate to contact us for more information.

  15. Dataset of the paper: "How do Hugging Face Models Document Datasets, Bias,...

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Jan 16, 2024
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    Federica Pepe; Vittoria Nardone; Vittoria Nardone; Antonio Mastropaolo; Antonio Mastropaolo; Gerardo Canfora; Gerardo Canfora; Gabriele BAVOTA; Gabriele BAVOTA; Massimiliano Di Penta; Massimiliano Di Penta; Federica Pepe (2024). Dataset of the paper: "How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study" [Dataset]. http://doi.org/10.5281/zenodo.10058142
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    zipAvailable download formats
    Dataset updated
    Jan 16, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Federica Pepe; Vittoria Nardone; Vittoria Nardone; Antonio Mastropaolo; Antonio Mastropaolo; Gerardo Canfora; Gerardo Canfora; Gabriele BAVOTA; Gabriele BAVOTA; Massimiliano Di Penta; Massimiliano Di Penta; Federica Pepe
    License

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

    Description

    This replication package contains datasets and scripts related to the paper: "*How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study*"

    ## Root directory

    - `statistics.r`: R script used to compute the correlation between usage and downloads, and the RQ1/RQ2 inter-rater agreements

    - `modelsInfo.zip`: zip file containing all the downloaded model cards (in JSON format)

    - `script`: directory containing all the scripts used to collect and process data. For further details, see README file inside the script directory.

    ## Dataset

    - `Dataset/Dataset_HF-models-list.csv`: list of HF models analyzed

    - `Dataset/Dataset_github-prj-list.txt`: list of GitHub projects using the *transformers* library

    - `Dataset/Dataset_github-Prj_model-Used.csv`: contains usage pairs: project, model

    - `Dataset/Dataset_prj-num-models-reused.csv`: number of models used by each GitHub project

    - `Dataset/Dataset_model-download_num-prj_correlation.csv` contains, for each model used by GitHub projects: the name, the task, the number of reusing projects, and the number of downloads

    ## RQ1

    - `RQ1/RQ1_dataset-list.txt`: list of HF datasets

    - `RQ1/RQ1_datasetSample.csv`: sample set of models used for the manual analysis of datasets

    - `RQ1/RQ1_analyzeDatasetTags.py`: Python script to analyze model tags for the presence of datasets. it requires to unzip the `modelsInfo.zip` in a directory with the same name (`modelsInfo`) at the root of the replication package folder. Produces the output to stdout. To redirect in a file fo be analyzed by the `RQ2/countDataset.py` script

    - `RQ1/RQ1_countDataset.py`: given the output of `RQ2/analyzeDatasetTags.py` (passed as argument) produces, for each model, a list of Booleans indicating whether (i) the model only declares HF datasets, (ii) the model only declares external datasets, (iii) the model declares both, and (iv) the model is part of the sample for the manual analysis

    - `RQ1/RQ1_datasetTags.csv`: output of `RQ2/analyzeDatasetTags.py`

    - `RQ1/RQ1_dataset_usage_count.csv`: output of `RQ2/countDataset.py`

    ## RQ2

    - `RQ2/tableBias.pdf`: table detailing the number of occurrences of different types of bias by model Task

    - `RQ2/RQ2_bias_classification_sheet.csv`: results of the manual labeling

    - `RQ2/RQ2_isBiased.csv`: file to compute the inter-rater agreement of whether or not a model documents Bias

    - `RQ2/RQ2_biasAgrLabels.csv`: file to compute the inter-rater agreement related to bias categories

    - `RQ2/RQ2_final_bias_categories_with_levels.csv`: for each model in the sample, this file lists (i) the bias leaf category, (ii) the first-level category, and (iii) the intermediate category

    ## RQ3

    - `RQ3/RQ3_LicenseValidation.csv`: manual validation of a sample of licenses

    - `RQ3/RQ3_{NETWORK-RESTRICTIVE|RESTRICTIVE|WEAK-RESTRICTIVE|PERMISSIVE}-license-list.txt`: lists of licenses with different permissiveness

    - `RQ3/RQ3_prjs_license.csv`: for each project linked to models, among other fields it indicates the license tag and name

    - `RQ3/RQ3_models_license.csv`: for each model, indicates among other pieces of info, whether the model has a license, and if yes what kind of license

    - `RQ3/RQ3_model-prj-license_contingency_table.csv`: usage contingency table between projects' licenses (columns) and models' licenses (rows)

    - `RQ3/RQ3_models_prjs_licenses_with_type.csv`: pairs project-model, with their respective licenses and permissiveness level

    ## scripts

    Contains the scripts used to mine Hugging Face and GitHub. Details are in the enclosed README

  16. c

    Amazon India products dataset in CSV format

    • crawlfeeds.com
    csv, zip
    Updated Mar 27, 2025
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    Crawl Feeds (2025). Amazon India products dataset in CSV format [Dataset]. https://crawlfeeds.com/datasets/amazon-india-products-dataset-in-csv-format
    Explore at:
    csv, zipAvailable download formats
    Dataset updated
    Mar 27, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

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

    Area covered
    India
    Description

    Gain access to a structured dataset featuring thousands of products listed on Amazon India. This dataset is ideal for e-commerce analytics, competitor research, pricing strategies, and market trend analysis.

    Dataset Features:

    • Product Details: Name, Brand, Category, and Unique ID

    • Pricing Information: Current Price, Discounted Price, and Currency

    • Availability & Ratings: Stock Status, Customer Ratings, and Reviews

    • Seller Information: Seller Name and Fulfillment Details

    • Additional Attributes: Product Description, Specifications, and Images

    Dataset Specifications:

    • Format: CSV

    • Number of Records: 50,000+

    • Delivery Time: 3 Days

    • Price: $149.00

    • Availability: Immediate

    This dataset provides structured and actionable insights to support e-commerce businesses, pricing strategies, and product optimization. If you're looking for more datasets for e-commerce analysis, explore our E-commerce datasets for a broader selection.

  17. f

    Full oral and gene database (csv format)

    • figshare.com
    application/gzip
    Updated May 22, 2019
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    Braden Tierney (2019). Full oral and gene database (csv format) [Dataset]. http://doi.org/10.6084/m9.figshare.8001362.v1
    Explore at:
    application/gzipAvailable download formats
    Dataset updated
    May 22, 2019
    Dataset provided by
    figshare
    Authors
    Braden Tierney
    License

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

    Description

    This is our complete database in csv format (with gene names, ID's, annotations, lengths, cluster sizes, and taxonomic classifications) that can be queried on our website. The difference is that it does not have the sequences – those can be downloaded in other files on figshare. This file, as well as those, can be parsed and linked by the gene identifier.We recommend downloading this database and parsing it yourself if you attempt to run a query that is too large for our servers to handle.

  18. Podcast PR Contacts - Self-Service CSV Batch Export

    • datarade.ai
    .csv, .xls
    Updated May 27, 2025
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    Listen Notes (2025). Podcast PR Contacts - Self-Service CSV Batch Export [Dataset]. https://datarade.ai/data-products/podcast-pr-contacts-self-service-csv-batch-export-listen-notes
    Explore at:
    .csv, .xlsAvailable download formats
    Dataset updated
    May 27, 2025
    Dataset authored and provided by
    Listen Notes
    Area covered
    Costa Rica, Bulgaria, Algeria, Kuwait, French Polynesia, Dominican Republic, Israel, Gibraltar, Congo, Benin
    Description

    == Quick starts ==

    Batch export podcast metadata to CSV files:

    1) Export by search keyword: https://www.listennotes.com/podcast-datasets/keyword/

    2) Export by category: https://www.listennotes.com/podcast-datasets/category/

    == Quick facts ==

    The most up-to-date and comprehensive podcast database available All languages & All countries Includes over 3,500,000 podcasts Features 35+ data fields , such as basic metadata, global rank, RSS feed (with audio URLs), Spotify links, and more Delivered in CSV format

    == Data Attributes ==

    See the full list of data attributes on this page: https://www.listennotes.com/podcast-datasets/fields/?filter=podcast_only

    How to access podcast audio files: Our dataset includes RSS feed URLs for all podcasts. You can retrieve audio for over 170 million episodes directly from these feeds. With access to the raw audio, you’ll have high-quality podcast speech data ideal for AI training and related applications.

    == Custom Offers ==

    We can provide custom datasets based on your needs, such as language-specific data, daily/weekly/monthly update frequency, or one-time purchases.

    We also provide a RESTful API at PodcastAPI.com

    Contact us: hello@listennotes.com

    == Need Help? ==

    If you have any questions about our products, feel free to reach out hello@listennotes.com

    == About Listen Notes, Inc. ==

    Since 2017, Listen Notes, Inc. has provided the leading podcast search engine and podcast database.

  19. i

    Sample Dataset for Testing

    • ieee-dataport.org
    Updated Apr 28, 2025
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    Alex Outman (2025). Sample Dataset for Testing [Dataset]. https://ieee-dataport.org/documents/sample-dataset-testing
    Explore at:
    Dataset updated
    Apr 28, 2025
    Authors
    Alex Outman
    License

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

    Description

    10

  20. MOT testing data for Great Britain

    • s3.amazonaws.com
    • gov.uk
    Updated Mar 24, 2022
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    Driver and Vehicle Standards Agency (2022). MOT testing data for Great Britain [Dataset]. https://s3.amazonaws.com/thegovernmentsays-files/content/179/1797262.html
    Explore at:
    Dataset updated
    Mar 24, 2022
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Driver and Vehicle Standards Agency
    Area covered
    United Kingdom, Great Britain
    Description

    About this data set

    This data set comes from data held by the Driver and Vehicle Standards Agency (DVSA).

    It is not classed as an ‘official statistic’. This means it’s not subject to scrutiny and assessment by the UK Statistics Authority.

    MOT test results by class

    The MOT test checks that your vehicle meets road safety and environmental standards. Different types of vehicles (for example, cars and motorcycles) fall into different ‘classes’.

    This data table shows the number of initial tests. It does not include abandoned tests, aborted tests, or retests.

    The initial fail rate is the rate for vehicles as they were brought for the MOT. The final fail rate excludes vehicles that pass the test after rectification of minor defects at the time of the test.

    This data table is updated every 3 months.

    https://www.gov.uk/assets/whitehall/pub-cover-spreadsheet-471052e0d03e940bbc62528a05ac204a884b553e4943e63c8bffa6b8baef8967.png">

    Initial failures by defect category

    These tables give data for the following classes of vehicles:

    • class 1 and 2 vehicles - motorcycles
    • class 3 and 4 vehicles - cars and light vans up to 3,000kg
    • class 5 vehicles - private passenger vehicles with more than 12 seats
    • class 7 vehicles - goods vehicles between 3,000kg and 3,500kg gross vehicle weight

    All figures are for vehicles as they were brought in for the MOT.

    A failed test usually has multiple failure items.

    The percentage of tests is worked out as the number of tests with one or more failure items in the defect as a percentage of total tests.

    The percentage of defects is worked out as the total defects in the category as a percentage of total defects for all categories.

    The average defects per initial test failure is worked out as the total failure items as a percentage of total tests failed plus tests that passed after rectification of a minor defect at the time of the test.

    These data tables are updated every 3 months.

    https://www.gov.uk/assets/whitehall/pub-cover-spreadsheet-471052e0d03e940bbc62528a05ac204a884b553e4943e63c8bffa6b8baef8967.png">

    https://www.gov.uk/assets/whitehall/pub-cover-spreadsheet-471052e0d03e940bbc62528a05ac204a884b553e4943e63c8bffa6b8baef8967.png">

    MOT class 3 and 4 vehicles: initial failures by defect category</h3

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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
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GitTables 1M - CSV files

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.

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