We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

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.

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.

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

The code of our machine learning approach is also included. There is a README.txt file with the documentation of how to use the code.

https://i.imgur.com/PcSDv8A.png" alt="Imgur">

Overview

The dataset provided here is a rich compilation of various data files gathered to support diverse analytical challenges and education in data science. It is especially curated to provide researchers, data enthusiasts, and students with real-world data across different domains, including biostatistics, travel, real estate, sports, media viewership, and more.

Files

Below is a brief overview of what each CSV file contains: - Addresses: Practical examples of string manipulation and address data formatting in CSV. - Air Travel: Historical dataset suitable for analyzing trends in air travel over a period of three years. - Biostats: A dataset of office workers' biometrics, ideal for introductory statistics and biology. - Cities: Geographic and administrative data for urban analysis or socio-demographic studies. - Car Crashes in Catalonia: Weekly traffic accident data from Catalonia, providing a base for public policy research. - De Niro's Film Ratings: Analyze trends in film ratings over time with this entertainment-focused dataset. - Ford Escort Sales: Pre-owned vehicle sales data, perfect for regression analysis or price prediction models. - Old Faithful Geyser: Geological data for pattern recognition and prediction in natural phenomena. - Freshman Year Weights and BMIs: Dataset depicting weight and BMI changes for health and lifestyle studies. - Grades: Education performance data which can be correlated with demographics or study patterns. - Home Sales: A dataset reflecting the housing market dynamics, useful for economic analysis or real estate appraisal. - Hooke's Law Demonstration: Physics data illustrating the classic principle of elasticity in springs. - Hurricanes and Storm Data: Climate data on hurricane and storm frequency for environmental risk assessments. - Height and Weight Measurements: Public health research dataset on anthropometric data. - Lead Shot Specs: Detailed engineering data for material sciences and manufacturing studies. - Alphabet Letter Frequency: Text analysis dataset for frequency distribution studies in large text samples. - MLB Player Statistics: Comprehensive athletic data set for analysis of performance metrics in sports. - MLB Teams' Seasonal Performance: A dataset combining financial and sports performance data from the 2012 MLB season. - TV News Viewership: Media consumption data which can be used to analyze viewing patterns and trends. - Historical Nile Flood Data: A unique environmental dataset for historical trend analysis in flood levels. - Oscar Winner Ages: A dataset to explore age trends among Oscar-winning actors and actresses. - Snakes and Ladders Statistics: Data from the game outcomes useful in studying probability and game theory. - Tallahassee Cab Fares: Price modeling data from the real-world pricing of taxi services. - Taxable Goods Data: A snapshot of economic data concerning taxation impact on prices. - Tree Measurements: Ecological and environmental science data related to tree growth and forest management. - Real Estate Prices from Zillow: Market analysis dataset for those interested in housing price determinants.

Format

The enclosed data respect the comma-separated values (CSV) file format standards, ensuring compatibility with most data processing libraries in Python, R, and other languages. The datasets are ready for import into Jupyter notebooks, RStudio, or any other integrated development environment (IDE) used for data science.

Quality Assurance

The data is pre-checked for common issues such as missing values, duplicate records, and inconsistent entries, offering a clean and reliable dataset for various analytical exercises. With initial header lines in some CSV files, users can easily identify dataset fields and start their analysis without additional data cleaning for headers.

Acknowledgements

The dataset adheres to the GNU LGPL license, making it freely available for modification and distribution, provided that the original source is cited. This opens up possibilities for educators to integrate real-world data into curricula, researchers to validate models against diverse datasets, and practitioners to refine their analytical skills with hands-on data.

This dataset has been compiled from https://people.sc.fsu.edu/~jburkardt/data/csv/csv.html, with gratitude to the authors and maintainers for their dedication to providing open data resources for educational and research purposes. https://i.imgur.com/HOtyghv.png" alt="Imgur">

Complete dataset of “Film Circulation on the International Film Festival Network and the Impact on Global Film Culture”

A peer-reviewed data paper for this dataset is in review to be published in NECSUS_European Journal of Media Studies - an open access journal aiming at enhancing data transparency and reusability, and will be available from https://necsus-ejms.org/ and https://mediarep.org

Please cite this when using the dataset.

Detailed description of the dataset:

1 Film Dataset: Festival Programs

The Film Dataset consists a data scheme image file, a codebook and two dataset tables in csv format.

The codebook (csv file “1_codebook_film-dataset_festival-program”) offers a detailed description of all variables within the Film Dataset. Along with the definition of variables it lists explanations for the units of measurement, data sources, coding and information on missing data.

The csv file “1_film-dataset_festival-program_long” comprises a dataset of all films and the festivals, festival sections, and the year of the festival edition that they were sampled from. The dataset is structured in the long format, i.e. the same film can appear in several rows when it appeared in more than one sample festival. However, films are identifiable via their unique ID.

The csv file “1_film-dataset_festival-program_wide” consists of the dataset listing only unique films (n=9,348). The dataset is in the wide format, i.e. each row corresponds to a unique film, identifiable via its unique ID. For easy analysis, and since the overlap is only six percent, in this dataset the variable sample festival (fest) corresponds to the first sample festival where the film appeared. For instance, if a film was first shown at Berlinale (in February) and then at Frameline (in June of the same year), the sample festival will list “Berlinale”. This file includes information on unique and IMDb IDs, the film title, production year, length, categorization in length, production countries, regional attribution, director names, genre attribution, the festival, festival section and festival edition the film was sampled from, and information whether there is festival run information available through the IMDb data.

2 Survey Dataset

The Survey Dataset consists of a data scheme image file, a codebook and two dataset tables in csv format.

The codebook “2_codebook_survey-dataset” includes coding information for both survey datasets. It lists the definition of the variables or survey questions (corresponding to Samoilova/Loist 2019), units of measurement, data source, variable type, range and coding, and information on missing data.

The csv file “2_survey-dataset_long-festivals_shared-consent” consists of a subset (n=161) of the original survey dataset (n=454), where respondents provided festival run data for films (n=206) and gave consent to share their data for research purposes. This dataset consists of the festival data in a long format, so that each row corresponds to the festival appearance of a film.

The csv file “2_survey-dataset_wide-no-festivals_shared-consent” consists of a subset (n=372) of the original dataset (n=454) of survey responses corresponding to sample films. It includes data only for those films for which respondents provided consent to share their data for research purposes. This dataset is shown in wide format of the survey data, i.e. information for each response corresponding to a film is listed in one row. This includes data on film IDs, film title, survey questions regarding completeness and availability of provided information, information on number of festival screenings, screening fees, budgets, marketing costs, market screenings, and distribution. As the file name suggests, no data on festival screenings is included in the wide format dataset.

3 IMDb & Scripts

The IMDb dataset consists of a data scheme image file, one codebook and eight datasets, all in csv format. It also includes the R scripts that we used for scraping and matching.

The codebook “3_codebook_imdb-dataset” includes information for all IMDb datasets. This includes ID information and their data source, coding and value ranges, and information on missing data.

The csv file “3_imdb-dataset_aka-titles_long” contains film title data in different languages scraped from IMDb in a long format, i.e. each row corresponds to a title in a given language.

The csv file “3_imdb-dataset_awards_long” contains film award data in a long format, i.e. each row corresponds to an award of a given film.

The csv file “3_imdb-dataset_companies_long” contains data on production and distribution companies of films. The dataset is in a long format, so that each row corresponds to a particular company of a particular film.

The csv file “3_imdb-dataset_crew_long” contains data on names and roles of crew members in a long format, i.e. each row corresponds to each crew member. The file also contains binary gender assigned to directors based on their first names using the GenderizeR application.

The csv file “3_imdb-dataset_festival-runs_long” contains festival run data scraped from IMDb in a long format, i.e. each row corresponds to the festival appearance of a given film. The dataset does not include each film screening, but the first screening of a film at a festival within a given year. The data includes festival runs up to 2019.

The csv file “3_imdb-dataset_general-info_wide” contains general information about films such as genre as defined by IMDb, languages in which a film was shown, ratings, and budget. The dataset is in wide format, so that each row corresponds to a unique film.

The csv file “3_imdb-dataset_release-info_long” contains data about non-festival release (e.g., theatrical, digital, tv, dvd/blueray). The dataset is in a long format, so that each row corresponds to a particular release of a particular film.

The csv file “3_imdb-dataset_websites_long” contains data on available websites (official websites, miscellaneous, photos, video clips). The dataset is in a long format, so that each row corresponds to a website of a particular film.

The dataset includes 8 text files containing the script for webscraping. They were written using the R-3.6.3 version for Windows.

The R script “r_1_unite_data” demonstrates the structure of the dataset, that we use in the following steps to identify, scrape, and match the film data.

The R script “r_2_scrape_matches” reads in the dataset with the film characteristics described in the “r_1_unite_data” and uses various R packages to create a search URL for each film from the core dataset on the IMDb website. The script attempts to match each film from the core dataset to IMDb records by first conducting an advanced search based on the movie title and year, and then potentially using an alternative title and a basic search if no matches are found in the advanced search. The script scrapes the title, release year, directors, running time, genre, and IMDb film URL from the first page of the suggested records from the IMDb website. The script then defines a loop that matches (including matching scores) each film in the core dataset with suggested films on the IMDb search page. Matching was done using data on directors, production year (+/- one year), and title, a fuzzy matching approach with two methods: “cosine” and “osa.” where the cosine similarity is used to match titles with a high degree of similarity, and the OSA algorithm is used to match titles that may have typos or minor variations.

The script “r_3_matching” creates a dataset with the matches for a manual check. Each pair of films (original film from the core dataset and the suggested match from the IMDb website was categorized in the following five categories: a) 100% match: perfect match on title, year, and director; b) likely good match; c) maybe match; d) unlikely match; and e) no match). The script also checks for possible doubles in the dataset and identifies them for a manual check.

The script “r_4_scraping_functions” creates a function for scraping the data from the identified matches (based on the scripts described above and manually checked). These functions are used for scraping the data in the next script.

The script “r_5a_extracting_info_sample” uses the function defined in the “r_4_scraping_functions”, in order to scrape the IMDb data for the identified matches. This script does that for the first 100 films, to check, if everything works. Scraping for the entire dataset took a few hours. Therefore, a test with a subsample of 100 films is advisable.

The script “r_5b_extracting_info_all” extracts the data for the entire dataset of the identified matches.

The script “r_5c_extracting_info_skipped” checks the films with missing data (where data was not scraped) and tried to extract data one more time to make sure that the errors were not caused by disruptions in the internet connection or other technical issues.

The script “r_check_logs” is used for troubleshooting and tracking the progress of all of the R scripts used. It gives information on the amount of missing values and errors.

4 Festival Library Dataset

The Festival Library Dataset consists of a data scheme image file, one codebook and one dataset, all in csv format.

The codebook (csv file “4_codebook_festival-library_dataset”) offers a detailed description of all variables within the Library Dataset. It lists the definition of variables, such as location and festival name, and festival categories,

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema 

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
  _id: 

This is the accompanying dataset to the following paper https://www.nature.com/articles/s41597-023-01975-w

Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes from the same data sources in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time.

If you use Caravan in your research, it would be appreciated to not only cite Caravan itself, but also the source datasets, to pay respect to the amount of work that was put into the creation of these datasets and that made Caravan possible in the first place.

All current development and additional community extensions can be found at https://github.com/kratzert/Caravan

IMPORTANT: Due to size limitations for individual repositories, the netCDF version and the CSV version of Caravan (since Version 1.6) are split into two different repositories. You can find the netCDF version at https://zenodo.org/records/14673536

Channel Log:

• 23 May 2022: Version 0.2 - Resolved a bug when renaming the LamaH gauge ids from the LamaH ids to the official gauge ids provided as "govnr" in the LamaH dataset attribute files.
• 24 May 2022: Version 0.3 - Fixed gaps in forcing data in some "camels" (US) basins.
• 15 June 2022: Version 0.4 - Fixed replacing negative CAMELS US values with NaN (-999 in CAMELS indicates missing observation).
• 1 December 2022: Version 0.4 - Added 4298 basins in the US, Canada and Mexico (part of HYSETS), now totalling to 6830 basins. Fixed a bug in the computation of catchment attributes that are defined as pour point properties, where sometimes the wrong HydroATLAS polygon was picked. Restructured the attribute files and added some more meta data (station name and country).
• 16 January 2023: Version 1.0 - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. For the most up to date version, please check https://github.com/kratzert/Caravan
• 10 May 2023: Version 1.1 - No data change, just update data description.
• 17 May 2023: Version 1.2 - Updated a handful of attribute values that were affected by a bug in their derivation. See https://github.com/kratzert/Caravan/issues/22 for details.
• 16 April 2024: Version 1.4 - Added 9130 gauges from the original source dataset that were initially not included because of the area thresholds (i.e. basins smaller than 100sqkm or larger than 2000sqkm). Also extended the forcing period for all gauges (including the original ones) to 1950-2023. Added two different download options that include timeseries data only as either csv files (Caravan-csv.tar.xz) or netcdf files (Caravan-nc.tar.xz). Including the large basins also required an update in the earth engine code
• 16 Jan 2025: Version 1.5 - Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND").
• 27 May 2025: Version 1.6
  
  • Updated the CAMELS-AUS data to source from CAMELS-AUS v2. This means more basins (561 compared to 222) and more recent streamflow data (2022 compared to 2014). Note that the gauge id for four basins changed between the original CAMELS-AUS version and v2. Those gauges are ['camelsaus_224213A', 'camelsaus_224214A', 'camelsaus_227225A', 'camelsaus_403213A'] that all lost their trailing "A". To stay synced with CAMELS-AUS (v2), we also adapted the new naming.
  • Added VERSION file to the root directory that contains the current version number.
  • Updated the code to the most recent GitHub snapshot (commit 6eab036).
  • Due to the 50GB repository limit, we had to split the netCDF version and the CSV version into two separate repositories. The CSV version can be found under https://zenodo.org/records/15530021

The BuildingsBench datasets consist of: Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock. 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB). BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below: ElectricityLoadDiagrams20112014 Building Data Genome Project-2 Individual household electric power consumption (Sceaux) Borealis SMART IDEAL Low Carbon London A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.

A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaboration

Version 162 of the dataset. NOTES: Data for 3/15 - 3/18 was not extracted due to unexpected and unannounced downtime of our university infrastructure. We will try to backfill those days by next release. FUTURE CHANGES: Due to the imminent paywalling of Twitter's API access this might be the last full update of this dataset. If the API access is not blocked, we will be stopping updates for this dataset with release 165 - a bit more than 3 years after our initial release. It's been a joy seeing all the work that uses this resource and we are glad that so many found it useful.

The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.

The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,395,222,801 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (361,748,721 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688)

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used.

Abstract MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over 40,000 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012 [1]. The MIMIC-III Clinical Database is available on PhysioNet (doi: 10.13026/C2XW26). Though deidentified, MIMIC-III contains detailed information regarding the care of real patients, and as such requires credentialing before access. To allow researchers to ascertain whether the database is suitable for their work, we have manually curated a demo subset, which contains information for 100 patients also present in the MIMIC-III Clinical Database. Notably, the demo dataset does not include free-text notes.

Background In recent years there has been a concerted move towards the adoption of digital health record systems in hospitals. Despite this advance, interoperability of digital systems remains an open issue, leading to challenges in data integration. As a result, the potential that hospital data offers in terms of understanding and improving care is yet to be fully realized.

MIMIC-III integrates deidentified, comprehensive clinical data of patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts, and makes it widely accessible to researchers internationally under a data use agreement. The open nature of the data allows clinical studies to be reproduced and improved in ways that would not otherwise be possible.

The MIMIC-III database was populated with data that had been acquired during routine hospital care, so there was no associated burden on caregivers and no interference with their workflow. For more information on the collection of the data, see the MIMIC-III Clinical Database page.

Methods The demo dataset contains all intensive care unit (ICU) stays for 100 patients. These patients were selected randomly from the subset of patients in the dataset who eventually die. Consequently, all patients will have a date of death (DOD). However, patients do not necessarily die during an individual hospital admission or ICU stay.

This project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the project did not impact clinical care and all protected health information was deidentified.

Data Description MIMIC-III is a relational database consisting of 26 tables. For a detailed description of the database structure, see the MIMIC-III Clinical Database page. The demo shares an identical schema, except all rows in the NOTEEVENTS table have been removed.

The data files are distributed in comma separated value (CSV) format following the RFC 4180 standard. Notably, string fields which contain commas, newlines, and/or double quotes are encapsulated by double quotes ("). Actual double quotes in the data are escaped using an additional double quote. For example, the string she said "the patient was notified at 6pm" would be stored in the CSV as "she said ""the patient was notified at 6pm""". More detail is provided on the RFC 4180 description page: https://tools.ietf.org/html/rfc4180

Usage Notes The MIMIC-III demo provides researchers with an opportunity to review the structure and content of MIMIC-III before deciding whether or not to carry out an analysis on the full dataset.

CSV files can be opened natively using any text editor or spreadsheet program. However, some tables are large, and it may be preferable to navigate the data stored in a relational database. One alternative is to create an SQLite database using the CSV files. SQLite is a lightweight database format which stores all constituent tables in a single file, and SQLite databases interoperate well with a number software tools.

DB Browser for SQLite is a high quality, visual, open source tool to create, design, and edit database files compatible with SQLite. We have found this tool to be useful for navigating SQLite files. Information regarding installation of the software and creation of the database can be found online: https://sqlitebrowser.org/

Release Notes Release notes for the demo follow the release notes for the MIMIC-III database.

Acknowledgements This research and development was supported by grants NIH-R01-EB017205, NIH-R01-EB001659, and NIH-R01-GM104987 from the National Institutes of Health. The authors would also like to thank Philips Healthcare and staff at the Beth Israel Deaconess Medical Center, Boston, for supporting database development, and Ken Pierce for providing ongoing support for the MIMIC research community.

Conflicts of Interest The authors declare no competing financial interests.

References Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Mo...

These datasets are a subset of the CMS Open data with 2021 data-taking conditions for education purposes. In this version, the data and simulation files are compressed into one big file for easy access. They are stored in two different formats (CSV and PKL) with the same content, therefore just use one of them. Once unzipped: - Data files, starting with output_data_CMS_Run2012B, correspond to 4429.37 /pb of data collected by the CMS Experiment. They are a subset of the dataset on reference [1]. - Simulation files, starting with output_sim_CMS_MonteCarlo2012, are a subset of the dataset referenced on [2]. The number of generated events in this case is 30458871, and the cross section is 3503.71. All the files were processed with a modified version of the AOD2NanoAODOutreachTool [3]. The small modifications are related to the number of triggers stored, and some objects like taus were removed. -------------------------------------------------------- [1] CMS collaboration (2017). DoubleMuParked primary dataset in AOD format from Run of 2012 (/DoubleMuParked/Run2012B-22Jan2013-v1/AOD). CERN Open Data Portal. DOI:10.7483/OPENDATA.CMS.YLIC.86ZZ [2] Wunsch, Stefan; (2019). DYJetsToLL dataset in reduced NanoAOD format for education and outreach. CERN Open Data Portal. DOI:10.7483/OPENDATA.CMS.SRRA.2GON [3] https://github.com/cms-opendata-analyses/AOD2NanoAODOutreachTool {"references": ["CMS collaboration (2017). DoubleMuParked primary dataset in AOD format from Run of 2012 (/DoubleMuParked/Run2012B-22Jan2013-v1/AOD). CERN Open Data Portal. DOI:10.7483/OPENDATA.CMS.YLIC.86ZZ", "Wunsch, Stefan; (2019). DYJetsToLL dataset in reduced NanoAOD format for education and outreach. CERN Open Data Portal. DOI:10.7483/OPENDATA.CMS.SRRA.2GON", "https://github.com/cms-opendata-analyses/AOD2NanoAODOutreachTool"]} For the CSV files you might need to open them using pandas as: pandas.read_csv('output_data.csv', index_col=['entry','subentry']) For the pickle files, you might need to use python3.

Once PowerPivot has been installed, to load the large files, please follow the instructions below: Start Excel as normal Click on the PowerPivot tab Click on the PowerPivot Window icon (top left) In the PowerPivot Window, click on the "From Other Sources" icon In the Table Import Wizard e.g. scroll to the bottom and select Text File Browse to the file you want to open and choose the file extension you require e.g. CSV Please read the below notes to ensure correct understanding of the data. Microsoft PowerPivot add-on for Excel can be used to handle larger data sets. The Microsoft PowerPivot add-on for Excel is available using the link in the 'Related Links' section - https://www.microsoft.com/en-us/download/details.aspx?id=43348 Once PowerPivot has been installed, to load the large files, please follow the instructions below: 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Please read the below notes to ensure correct understanding of the data. Fewer than 5 Items Please be aware that I have decided not to release the exact number of items, where the total number of items falls below 5, for certain drugs/patient combinations. Where suppression has been applied a * is shown in place of the number of items, please read this as 1-4 items. Suppressions have been applied where items are lower than 5, for items and NIC and for quantity when quantity and items are both lower than 5 for the following drugs and identified genders as per the sensitive drug list; When the BNF Paragraph Code is 60401 (Female Sex Hormones & Their Modulators) and the gender identified on the prescription is Male When the BNF Paragraph Code is 60402 (Male Sex Hormones And Antagonists) and the gender identified on the prescription is Female When the BNF Paragraph Code is 70201 (Preparations For Vaginal/Vulval Changes) and the gender identified on the prescription is Male When the BNF Paragraph Code is 70202 (Vaginal And Vulval Infections) and the gender identified on the prescription is Male When the BNF Paragraph Code is 70301 (Combined Hormonal Contraceptives/Systems) and the gender identified on the prescription is Male When the BNF Paragraph Code is 70302 (Progestogen-only Contraceptives) and the gender identified on the prescription is Male When the BNF Paragraph Code is 80302 (Progestogens) and the gender identified on the prescription is Male When the BNF Paragraph Code is 70405 (Drugs For Erectile Dysfunction) and the gender identified on the prescription is Female When the BNF Paragraph Code is 70406 (Drugs For Premature Ejaculation) and the gender identified on the prescription is Female This is because the patients could be identified, when combined with other information that may be in the public domain or reasonably available. This information falls under the exemption in section 40 subsections 2 and 3A (a) of the Freedom of Information Act. This is because it would breach the first data protection principle as: a. it is not fair to disclose patients personal details to the world and is likely to cause damage or distress. b. these details are not of sufficient interest to the public to warrant an intrusion into the privacy of the patients. Please click the below web link to see the exemption in full.

"Wake Vision" is a large, high-quality dataset featuring over 6 million images, significantly exceeding the scale and diversity of current tinyML datasets (100x). The dataset contains images with annotations of whether each image contains a person. Additionally, the dataset incorporates a comprehensive fine-grained benchmark to assess fairness and robustness, covering perceived gender, perceived age, subject distance, lighting conditions, and depictions. This dataset hosted on Harvard Dataverse contains images, CSV files, and code to generate a Wake Vision TensorFlow Dataset. We publish the annotations of this dataset under a CC BY 4.0 license. All images in the dataset are from the Open Images v7 dataset, which are sourced images from Flickr and are listed as having a CC BY 2.0 license.

This dataset serves to estimate the status, in particular the size, of a crowd given the impact on radio frequency communication links within a wireless sensor network. To quantify this relation, signal strengths across sub-GHz communication links are collected at the premises of the Tomorrowland music festival. The communication links are formed between the network nodes of wireless sensor networks deployed in three of the festival's stage environments.

The table below lists the eighteen dataset files. They are collected at the music festival's 2017 and 2018 editions. There are three environments, labeled: ‘Freedom Stage 2017’, ‘Freedom Stage 2018’, and ‘Main Comfort 2018’. Each environment has both 433 MHz and 868 MHz data. The measurements at each environment were collected over a period of three festival days. The dataset files are formatted as Comma-Separated Values (CSV).

In addition to the datasets and reference files, a software example is provided to illustrate the data use and visualise the initial findings and relation between crowd size and network signal strength impact.

In order to use the software, please retain the following file structure:

. ├── data ├── data_reference ├── graphs └── software

The peer-reviewed data descriptor for this dataset has now been published in MDPI Data - an open access journal aiming at enhancing data transparency and reusability, and can be accessed here: https://doi.org/10.3390/data5020052. Please cite this when using the dataset.

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.

Supplementary material and data for Pfohl and Cole-Lewis et al., "A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models" (2024).

We include the sets of adversarial questions for each of the seven EquityMedQA datasets (OMAQ, EHAI, FBRT-Manual, FBRT-LLM, TRINDS, CC-Manual, and CC-LLM), the three other non-EquityMedQA datasets used in this work (HealthSearchQA, Mixed MMQA-OMAQ, and Omiye et al.), as well as the data generated as a part of the empirical study, including the generated model outputs (Med-PaLM 2 [1] primarily, with Med-PaLM [2] answers for pairwise analyses) and ratings from human annotators (physicians, health equity experts, and consumers). See the paper for details on all datasets.

We include other datasets evaluated in this work: HealthSearchQA [2], Mixed MMQA-OMAQ, and Omiye et al [3].

• Mixed MMQA-OMAQ is composed of the 140 question subset of MultiMedQA questions described in [1,2] with an additional 100 questions from OMAQ (described below). The 140 MultiMedQA questions are composed of 100 from HealthSearchQA, 20 from LiveQA [4], and 20 from MedicationQA [5]. In the data presented here, we do not reproduce the text of the questions from LiveQA and MedicationQA. For LiveQA, we instead use identifier that correspond to those presented in the original dataset. For MedicationQA, we designate "MedicationQA_N" to refer to the N-th row of MedicationQA (0-indexed).

A limited number of data elements described in the paper are not included here. The following elements are excluded:

1. The reference answers written by physicians to HealthSearchQA questions, introduced in [2], and the set of corresponding pairwise ratings. This accounts for 2,122 rated instances.

2. The free-text comments written by raters during the ratings process.

3. Demographic information associated with the consumer raters (only age group information is included).

References

1. Singhal, K., et al. Towards expert-level medical question answering with large language models. arXiv preprint arXiv:2305.09617 (2023).

2. Singhal, K., Azizi, S., Tu, T. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023). https://doi.org/10.1038/s41586-023-06291-2

3. Omiye, J.A., Lester, J.C., Spichak, S. et al. Large language models propagate race-based medicine. npj Digit. Med. 6, 195 (2023). https://doi.org/10.1038/s41746-023-00939-z

4. Abacha, Asma Ben, et al. "Overview of the medical question answering task at TREC 2017 LiveQA." TREC. 2017.

5. Abacha, Asma Ben, et al. "Bridging the gap between consumers’ medication questions and trusted answers." MEDINFO 2019: Health and Wellbeing e-Networks for All. IOS Press, 2019. 25-29.

Description of files and sheets

1. Independent Ratings [ratings_independent.csv]: Contains ratings of the presence of bias and its dimensions in Med-PaLM 2 outputs using the independent assessment rubric for each of the datasets studied. The primary response regarding the presence of bias is encoded in the column bias_presence with three possible values (No bias, Minor bias, Severe bias). Binary assessments of the dimensions of bias are encoded in separate columns (e.g., inaccuracy_for_some_axes). Instances for the Mixed MMQA-OMAQ dataset are triple-rated for each rater group; other datasets are single-rated. Instances were missing for five instances in MMQA-OMAQ and two instances in CC-Manual. This file contains 7,519 rated instances.

2. Paired Ratings [ratings_pairwise.csv]: Contains comparisons of the presence or degree of bias and its dimensions in Med-PaLM and Med-PaLM 2 outputs for each of the datasets studied. Pairwise responses are encoded in terms of two binary columns corresponding to which of the answers was judged to contain a greater degree of bias (e.g., Med-PaLM-2_answer_more_bias). Dimensions of bias are encoded in the same way as for ratings_independent.csv. Instances for the Mixed MMQA-OMAQ dataset are triple-rated for each rater group; other datasets are single-rated. Four ratings were missing (one for EHAI, two for FRT-Manual, one for FBRT-LLM). This file contains 6,446 rated instances.

3. Counterfactual Paired Ratings [ratings_counterfactual.csv]: Contains ratings under the counterfactual rubric for pairs of questions defined in the CC-Manual and CC-LLM datasets. Contains a binary assessment of the presence of bias (bias_presence), columns for each dimension of bias, and categorical columns corresponding to other elements of the rubric (ideal_answers_diff, how_answers_diff). Instances for the CC-Manual dataset are triple-rated, instances for CC-LLM are single-rated. Due to a data processing error, we removed questions that refer to `Natal'' from the analysis of the counterfactual rubric on the CC-Manual dataset. This affects three questions (corresponding to 21 pairs) derived from one seed question based on the TRINDS dataset. This file contains 1,012 rated instances.

4. Open-ended Medical Adversarial Queries (OMAQ) [equitymedqa_omaq.csv]: Contains questions that compose the OMAQ dataset. The OMAQ dataset was first described in [1].

5. Equity in Health AI (EHAI) [equitymedqa_ehai.csv]: Contains questions that compose the EHAI dataset.

6. Failure-Based Red Teaming - Manual (FBRT-Manual) [equitymedqa_fbrt_manual.csv]: Contains questions that compose the FBRT-Manual dataset.

7. Failure-Based Red Teaming - LLM (FBRT-LLM); full [equitymedqa_fbrt_llm.csv]: Contains questions that compose the extended FBRT-LLM dataset.

8. Failure-Based Red Teaming - LLM (FBRT-LLM) [equitymedqa_fbrt_llm_661_sampled.csv]: Contains questions that compose the sampled FBRT-LLM dataset used in the empirical study.

9. TRopical and INfectious DiseaseS (TRINDS) [equitymedqa_trinds.csv]: Contains questions that compose the TRINDS dataset.

10. Counterfactual Context - Manual (CC-Manual) [equitymedqa_cc_manual.csv]: Contains pairs of questions that compose the CC-Manual dataset.

11. Counterfactual Context - LLM (CC-LLM) [equitymedqa_cc_llm.csv]: Contains pairs of questions that compose the CC-LLM dataset.

12. HealthSearchQA [other_datasets_healthsearchqa.csv]: Contains questions sampled from the HealthSearchQA dataset [1,2].

13. Mixed MMQA-OMAQ [other_datasets_mixed_mmqa_omaq]: Contains questions that compose the Mixed MMQA-OMAQ dataset.

14. Omiye et al. [other datasets_omiye_et_al]: Contains questions proposed in Omiye et al. [3].

Version history

Version 2: Updated to include ratings and generated model outputs. Dataset files were updated to include unique ids associated with each question. Version 1: Contained datasets of questions without ratings. Consistent with v1 available as a preprint on Arxiv (https://arxiv.org/abs/2403.12025)

WARNING: These datasets contain adversarial questions designed specifically to probe biases in AI systems. They can include human-written and model-generated language and content that may be inaccurate, misleading, biased, disturbing, sensitive, or offensive.

NOTE: the content of this research repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.

Large set of whole-slide-images (WSI) of prostatectomy specimens with various grades of prostate cancer (PCa). More information can be found in the corresponding paper: https://doi.org/10.1038/s41598-018-37257-4

The WSIs in this dataset can be viewed using the open-source software ASAP or Open Slide.

Due to the large size of the complete dataset, the data has been split up in to multiple archives.

The data from the training set:

peso_training_masks.zip: Training masks (N=62) that have been used to train the main network of our paper. These masks are generated by a trained U-Net on the corresponding IHC slides.

peso_training_masks_corrected.zip: A subset of the color deconvolution masks (N=25) on which manual annotations have been made. Within these regions, stain and other artifacts have been removed.

peso_training_colordeconvolution.zip: Mask files (N=62) containing the P63&CK8/18 channel of the color deconvolution operation. These masks mark all regions that are stained by either P63 or CK8/18 in the IHC version of the slides.

peso_training_wsi_{1-6}.zip: Zip files containing the whole slide images of the training set (N=62). Each archive contains 10 slides, excluding the last which contains 12. These images are exported at a pixel resolution of 0.48mu/pixels.

The data from the test set:

peso_testset_regions.zip: Collection of annotation XML files with outlines of the test regions. These can be used to view the test regions in more detail using ASAP.

peso_testset_png.zip: Export of the test set regions in PNG format (2500x2500 pixels per region).

peso_testset_png_padded.zip: Export of the test regions in PNG format padded with a 500 pixel wide border (3500x3500 pixels per region). Useful for segmenting pixels at the border of the regions.

peso_testset_mapping.csv: A csv file mapping files from the test set (numbered 1-160) to regions in the xml files. The csv file also contains the label (benign or cancer) for each region.

peso_testset_groundtruth_masks.zip: The ground truth (pixel) masks (N=40) of all regions in the test set. For each pixel in the test set regions, these masks contain the ground truth: 0 for unlabelled, 1 for background and 2 for epithelial tissue.

peso_testset_wsi_{1-4}.zip: Zip files containing the whole slide images of the test set (N=40). Each archive contains 10 slides of the test set. These images are exported at a pixel resolution of 0.48mu/pixels.

This study was financed by a grant from the Dutch Cancer Society (KWF), grant number KUN 2015-7970.

If you make use of this dataset please cite both the dataset itself and the corresponding paper: https://doi.org/10.1038/s41598-018-37257-4

Update July 2021: We have added the ground truth masks for the test set.

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

This dataset was developed by NREL's distributed energy systems integration group as part of a study on high penetrations of distributed solar PV [1]. It consists of hourly load data in CSV format for use with the PNNL taxonomy of distribution feeders [2]. These feeders were developed in the open source GridLAB-D modelling language [3]. In this dataset each of the load points in the taxonomy feeders is populated with hourly averaged load data from a utility in the feeder’s geographical region, scaled and randomized to emulate real load profiles. For more information on the scaling and randomization process, see [1].

The taxonomy feeders are statistically representative of the various types of distribution feeders found in five geographical regions of the U.S. Efforts are underway (possibly complete) to translate these feeders into the OpenDSS modelling language.

This data set consists of one large CSV file for each feeder. Within each CSV, each column represents one load bus on the feeder. The header row lists the name of the load bus. The subsequent 8760 rows represent the loads for each hour of the year. The loads were scaled and randomized using a Python script, so each load series represents only one of many possible randomizations. In the header row, "rl" = residential load and "cl" = commercial load. Commercial loads are followed by a phase letter (A, B, or C). For regions 1-3, the data is from 2009. For regions 4-5, the data is from 2000.

For use in GridLAB-D, each column will need to be separated into its own CSV file without a header. The load value goes in the second column, and corresponding datetime values go in the first column, as shown in the sample file, sample_individual_load_file.csv. Only the first value in the time column needs to written as an absolute time; subsequent times may be written in relative format (i.e. "+1h", as in the sample). The load should be written in P+Qj format, as seen in the sample CSV, in units of Watts (W) and Volt-amps reactive (VAr). This dataset was derived from metered load data and hence includes only real power; reactive power can be generated by assuming an appropriate power factor. These loads were used with GridLAB-D version 2.2.

Browse files in this dataset, accessible as individual files and as a single ZIP file. This dataset is approximately 242MB compressed or 475MB uncompressed.

For questions about this dataset, contact andy.hoke@nrel.gov.

If you find this dataset useful, please mention NREL and cite [1] in your work.

References:

[1] A. Hoke, R. Butler, J. Hambrick, and B. Kroposki, “Steady-State Analysis of Maximum Photovoltaic Penetration Levels on Typical Distribution Feeders,” IEEE Transactions on Sustainable Energy, April 2013, available at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6357275 .

[2] K. Schneider, D. P. Chassin, R. Pratt, D. Engel, and S. Thompson, “Modern Grid Initiative Distribution Taxonomy Final Report”, PNNL, Nov. 2008. Accessed April 27, 2012: http://www.gridlabd.org/models/feeders/taxonomy of prototypical feeders.pdf

[3] K. Schneider, D. Chassin, Y. Pratt, and J. C. Fuller, “Distribution power flow for smart grid technologies”, IEEE/PES Power Systems Conference and Exposition, Seattle, WA, Mar. 2009, pp. 1-7, 15-18.