Free, daily updated MAC prefix and vendor CSV database. Download now for accurate device identification.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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 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.
https://research.csiro.au/dap/licences/csiro-data-licence/https://research.csiro.au/dap/licences/csiro-data-licence/
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.
https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy
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.
Alaska DCCED Division of Corporations, Business and Professional Licensing courtesy CSV Download Link Location
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The event logs in CSV format. The dataset contains both correlated and uncorrelated logs
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Screencast on how to export field observations with gps coordinates in Excel to a .csv file.
This dataset was created by Moses Moncy
http://researchdatafinder.qut.edu.au/display/n6106http://researchdatafinder.qut.edu.au/display/n6106
QUT Research Data Respository Dataset Resource available for download
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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:
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.
The UK House Price Index is a National Statistic.
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.
Datasets are available as CSV files. Find out about republishing and making use of the data.
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:
If you are interested in a specific attribute, we have separated them into these CSV files:
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-prices-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average_price&utm_term=9.30_21_05_25" class="govuk-link">Average price (CSV, 7MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-prices-Property-Type-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average_price_property_price&utm_term=9.30_21_05_25" class="govuk-link">Average price by property type (CSV, 15.3KB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Sales-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=sales&utm_term=9.30_21_05_25" class="govuk-link">Sales (CSV, 5.2KB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Cash-mortgage-sales-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=cash_mortgage-sales&utm_term=9.30_21_05_25" class="govuk-link">Cash mortgage sales (CSV, 4.9KB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/First-Time-Buyer-Former-Owner-Occupied-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=FTNFOO&utm_term=9.30_21_05_25" class="govuk-link">First time buyer and former owner occupier (CSV, 4.5KB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/New-and-Old-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=new_build&utm_term=9.30_21_05_25" class="govuk-link">New build and existing resold property (CSV, 11.0KB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Indices-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=index&utm_term=9.30_21_05_25" class="govuk-link">Index (CSV, 5.5KB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Indices-seasonally-adjusted-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=index_season_adjusted&utm_term=9.30_21_05_25" class="govuk-link">Index seasonally adjusted (CSV, 195KB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-price-seasonally-adjusted-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average-price_season_adjusted&utm_term=9.30_21_05_25" class="govuk-link">Average price seasonally adjusted (CSV, 205KB)
<a rel="external" href="https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Repossession-2025-03.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=repossession&utm_term=9.30_21_05
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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.
https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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
https://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.htmlhttps://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.html
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 `
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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:
"LCWB Dataset" contains all the JSON files that show how the aforementioned data have been distributed among training, validation, and test sets:
"Additional Files" contains some CSV files related to data we adopted to further test the deep neural network leveraged in the aforementioned manuscript:
CSV Files Structure
Each "XX_labels.csv" file contains, for each entry, the following information:
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:
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:
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.
Free, daily updated MAC prefix and vendor CSV database. Download now for accurate device identification.