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.

description: 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.; abstract: 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.

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.

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.

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: 

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.

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

Three datasets are available, each consisting of 15 csv files. Each file containing the voxelised shower information obtained from single particles produced at the front of the calorimeter in the |η| range (0.2-0.25) simulated in the ATLAS detector. Two datasets contain photons events with different statistics; the larger sample has about 10 times the number of events as the other. The other dataset contains pions. The pion dataset and the photon dataset with the lower statistics were used to train the corresponding two GANs presented in the AtlFast3 paper SIMU-2018-04.

The information in each file is a table; the rows correspond to the events and the columns to the voxels. The voxelisation procedure is described in the AtlFast3 paper linked above and in the dedicated PUB note ATL-SOFT-PUB-2020-006. In summary, the detailed energy deposits produced by ATLAS were converted from x,y,z coordinates to local cylindrical coordinates defined around the particle 3-momentum at the entrance of the calorimeter. The energy deposits in each layer were then grouped in voxels and for each voxel the energy was stored in the csv file. For each particle, there are 15 files corresponding to the 15 energy points used to train the GAN. The name of the csv file defines both the particle and the energy of the sample used to create the file.

The size of the voxels is described in the binning.xml file. Software tools to read the XML file and manipulate the spatial information of voxels are provided in the FastCaloGAN repository.

Updated on February 10th 2022. A new dataset photons_samples_highStat.tgz was added to this record and the binning.xml file was updated accordingly.

Updated on April 18th 2023. A new dataset pions_samples_highStat.tgz was added to this record.

The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -link, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (eprint link). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code (github link) used to annotate the figures, which could be adapted to any other kind of data annotation task.

This dataset contains all “license files” extracted from a snapshot of the Software Heritage archive taken on 2022-04-25. (Other, possibly more recent, versions of the datasets can be found at https://annex.softwareheritage.org/public/dataset/license-blobs/).

In this context, a license file is a unique file content (or “blob”) that appeared in a software origin archived by Software Heritage as a file whose name is often used to ship licenses in software projects. Some name examples are: COPYING, LICENSE, NOTICE, COPYRIGHT, etc. The exact file name pattern used to select the blobs contained in the dataset can be found in the SQL query file 01-select-blobs.sql. Note that the file name was not expected to be at the project root, because project subdirectories can contain different licenses than the top-level one, and we wanted to include those too.

Format

The dataset is organized as follows:

blobs.tar.zst: a Zst-compressed tarball containing deduplicated license blobs, one per file. The tarball contains 6’859’189 blobs, for a total uncompressed size on disk of 66 GiB.

The blobs are organized in a sharded directory structure that contains files named like blobs/86/24/8624bcdae55baeef00cd11d5dfcfa60f68710a02, where:

blobs/ is the root directory containing all license blobs

8624bcdae55baeef00cd11d5dfcfa60f68710a02 is the SHA1 checksum of a specific license blobs, a copy of the GPL3 license in this case. Each license blob is ultimately named with its SHA1:

$ head -n 3 blobs/86/24/8624bcdae55baeef00cd11d5dfcfa60f68710a02 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007

$ sha1sum blobs/86/24/8624bcdae55baeef00cd11d5dfcfa60f68710a02 8624bcdae55baeef00cd11d5dfcfa60f68710a02 blobs/86/24/8624bcdae55baeef00cd11d5dfcfa60f68710a02

86 and 24 are, respectively, the first and second group of two hex digits in the blob SHA1

One blob is missing, because its size (313MB) prevented its inclusion; (it was originally a tarball containing source code):

swh:1:cnt:61bf63793c2ee178733b39f8456a796b72dc8bde,1340d4e2da173c92d432026ecdc54b4859fe9911,"AUTHORS"

blobs-sample20k.tar.zst: analogous to blobs.tar.zst, but containing “only” 20’000 randomly selected license blobs

license-blobs.csv.zst a Zst-compressed CSV index of all the blobs in the dataset. Each line in the index (except the first one, which contains column headers) describes a license blob and is in the format SWHID,SHA1,NAME, for example:

swh:1:cnt:94a9ed024d3859793618152ea559a168bbcbb5e2,8624bcdae55baeef00cd11d5dfcfa60f68710a02,"COPYING" swh:1:cnt:94a9ed024d3859793618152ea559a168bbcbb5e2,8624bcdae55baeef00cd11d5dfcfa60f68710a02,"COPYING.GPL3" swh:1:cnt:94a9ed024d3859793618152ea559a168bbcbb5e2,8624bcdae55baeef00cd11d5dfcfa60f68710a02,"COPYING.GLP-3"

where:

SWHID: the Software Heritage persistent identifier of the blob. It can be used to retrieve and cross-reference the license blob via the Software Heritage archive, e.g., at: https://archive.softwareheritage.org/swh:1:cnt:94a9ed024d3859793618152ea559a168bbcbb5e2

SHA1: the blob SHA1, that can be used to cross-reference blobs in the blobs/ directory

NAME: a file name given to the license blob in a given software origin. As the same license blob can have different names in different contexts, the index contain multiple entries for the same blob with different names, as it is the case in the example above (yes, one of those has a typo in it, but it’s an original typo from some repository!).

blobs-fileinfo.csv.zst a Zst-compressed CSV mapping from blobs to basic file information in the format: SHA1,MIME_TYPE,ENCODING,LINE_COUNT,WORD_COUNT,SIZE, where:

SHA1: blob SHA1

MIME_TYPE: blob MIME type, as detected by libmagic

ENCODING: blob character encoding, as detected by libmagic

LINE_COUNT: number of lines in the blob (only for textual blobs with UTF8 encoding)

WORD_COUNT: number of words in the blob (only for textual blobs with UTF8 encoding)

SIZE: blob size in bytes

blobs-scancode.csv.zst a Zst-compressed CSV mapping from blobs to software license detected in them by ScanCode, in the format: SHA1,LICENSE,SCORE, where:

SHA1: blob SHA1

LICENSE: license detected in the blob, as an SPDX identifier (or ScanCode identifier for non-SPDX-indexed licenses)

SCORE: confidence score in the result, as a decimal number between 0 and 100

There may be zero or arbitrarily many lines for each blob.

blobs-scancode.ndjson.zst a Zst-compressed line-delimited JSON, containing a superset of the information in blobs-scancode.csv.zst. Each line is a JSON dictionary with three keys:

sha1: blob SHA1

licenses: output of scancode.api.get_licenses(..., min_score=0)

copyrights: output of scancode.api.get_copyrights(...)

There is exactly one line for each blob. licenses and copyrights keys are omitted for files not detected as plain text.

blobs-origins.csv.zst a Zst-compressed CSV mapping of where license blobs come from. Each line in the index associate a license blob to one of its origins in the format SWHIDURL, for example:

swh:1:cnt:94a9ed024d3859793618152ea559a168bbcbb5e2 https://github.com/pombreda/Artemis

Note that a license blob can come from many different places, only an arbitrary (and somewhat random) one is listed in this mapping.

If no origin URL is found in the Software Heritage archive, then a blank is used instead. This happens when they were either being loaded when the dataset was generated, or the loader process crashed before completing the blob’s origin’s ingestion.

blobs-nb-origins.csv.zst a Zst-compressed CSV mapping of how many origins of this blob are known to Software Heritage. Each line in the index associate a license blob to this count in the format SWHIDNUMBER, for example:

swh:1:cnt:94a9ed024d3859793618152ea559a168bbcbb5e2 2822260

Two blobs are missing because the computation crashes:

swh:1:cnt:e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 swh:1:cnt:8b137891791fe96927ad78e64b0aad7bded08bdc

This issue will be fixed in a future version of the dataset

blobs-earliest.csv.zst a Zst-compressed CSV mapping from blobs to information about their (earliest) known occurence(s) in the archive. Format: SWHIDEARLIEST_SWHIDEARLIEST_TSOCCURRENCES, where:

SWHID: blob SWHID

EARLIEST_SWHID: SWHID of the earliest known commit containing the blob

EARLIEST_TS: timestamp of the earliest known commit containing the blob, as a Unix time integer

OCCURRENCES: number of known commits containing the blob

replication-package.tar.gz: code and scripts used to produce the dataset

licenses-annotated-sample.tar.gz: ground truth, i.e., manually annotated random sample of license blobs, with details about the kind of information they contain.

Changes since the 2021-03-23 dataset

More input data, due to the SWH archive growing: more origins in supported forges and package managers; and support for more forges and package managers. See the SWH Archive Changelog for details.

Values in the NAME column of license-blobs.csv.zst are quoted, as some file names now contain commas.

Replication package now contains all the steps needed to reproduce all artefacts including the licenseblobs/fetch.py script.

blobs-nb-origins.csv.zst is added.

blobs-origins.csv.zst is now generated using the first origin returned by swh-graph’s leaves endpoint, instead of its randomwalk endpoint. This should have no impact on the result, other than a different distribution of “random” origins being picked.

blobs-origins.csv.zst was missing ~10% of its results in previous versions of the dataset, due to errors and/or timeouts in its generation, this is now down to 0.02% (1254 of the 6859445 unique blobs). Blobs with no known origins are now present, with a blank instead of URL.

blobs-earliest.csv.zst was missing ~10% of its results in previous versions of the dataset. It is complete now.

blobs-scancode.csv.zst is generated with a newer scancode-toolkit version (31.2.1)

blobs-scancode.ndjson.zst is added.

Errata

A file name .tmp_1340d4e2da173c92d432026ecdc54b4859fe9911 was present in the initial version of the dataset (published on 2022-11-07). It was removed on 2022-11-09 using these two commands:

pv blobs-fileinfo.csv.zst | zstdcat | grep -v ".tmp" | zstd -19 pv blobs.tar.zst| zstdcat | tar --delete blobs/13/40/.tmp_1340d4e2da173c92d432026ecdc54b4859fe9911 | zstd -19 -T12

The total uncompressed size was announced as 84 GiB based on the physical size on ext4, but it is actually 66 GiB.

Citation

If you use this dataset for research purposes, please acknowledge its use by citing one or both of the following papers:

[pdf, bib] Jesús M. González-Barahona, Sergio Raúl Montes León, Gregorio Robles, Stefano Zacchiroli. The software heritage license dataset (2022 edition). Empirical Software Engineering, Volume 28, Number 6, Article number 147 (2023).

[pdf, bib] 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.

References

The dataset has been built using primarily the data sources described in the following papers:

[pdf, bib] Roberto Di Cosmo, Stefano Zacchiroli. Software Heritage: Why and How to Preserve Software Source Code. In Proceedings of iPRES 2017: 14th International Conference on Digital Preservation, Kyoto, Japan, 25-29 September 2017.

[pdf, bib] Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli. The Software Heritage Graph Dataset: Public software development under one roof. In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Pages 138-142, IEEE 2019.

Errata (v2, 2024-01-09)

licenses-annotated-sample.tar.gz: some comments not intended for publication were removed, and 4

All global records with record depths of 30 m and deeper were downloaded from the Ocean Biodiversity Information System (OBIS). Following methods described in Bridges and Howell (2025), data were segregated into benthic and pelagic datasets using advanced statistical techniques. For the full methodology, please see Bridges and Howell (2025). For the code to rerun the segregation pipeline on new datasets, please see: https://github.com/ameliabridges/Bridges-Howell_OA_repo_pipeline.

Datasets are available as separate CSV files, or TIFF files where the total number of records are binned into 1x1degree cells.

Bridges, A.E.H. and Howell, K.L. In press. Prioritisation of ocean biodiversity data collection to deliver a sustainable ocean. Nature Communications Earth and Environment.

Analysis of ‘E-Designations: CSV file’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/13ba2f51-b7cd-48fc-86d4-273a0ae3502c on 13 February 2022.

--- Dataset description provided by original source is as follows ---

This data set contains information and locations of (E) Designations, including CEQR Environment Requirements (Table 1) and CEQR Restrictive Declarations (Table 2), in Appendix C of the Zoning Resolution. An (E) Designation provides notice of the presence of an environmental requirement pertaining to potential hazardous materials contamination, high ambient noise levels or air emission concerns on a particular tax lot.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

--- Original source retains full ownership of the source dataset ---

This dataset was generated using an open-source large language model and carefully curated prompts, simulating realistic clinical narratives while ensuring no real patient data is included. The primary purpose of this dataset is to support the development, evaluation, and benchmarking of Artificial Intelligence tools for clinical and biomedical applications in the Portuguese language, especially European Portuguese. It is particularly valuable for information extraction (IE) tasks such as named entity recognition, clinical note classification, summarization, and synthetic data generation in low-resource language settings. The dataset promotes research on the responsible use of synthetic data in healthcare and aims to serve as a foundation for training or fine-tuning domain-specific Portuguese language models in clinical IE and other natural language processing tasks. About the dataset XML files comprising 98,571 fully synthetic clinical notes in European Portuguese, divided into 4 types: 24,759 admission notes, 24,411 ambulatory notes, 24,639 discharge summaries, and 24,762 nursing notes; CSV file with prompts and responses from prompt engineering; CSV files with prompts and responses from synthetic dataset generation; CSV file with results from human evaluation; TXT files containing 1,000 clinical notes (250 of each type) taken from the synthetic dataset and used during automatic evaluation.

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.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

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.

What’s Included

The Spanish Open-Ended Question Answering Dataset is a meticulously curated collection of comprehensive Question-Answer pairs. It serves as a valuable resource for training Large Language Models (LLMs) and Question-answering models in the Spanish language, advancing the field of artificial intelligence.

This QA dataset comprises a diverse set of open-ended questions paired with corresponding answers in Spanish. There is no context paragraph given to choose an answer from, and each question is answered without any predefined context content. The questions cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more.

Each question is accompanied by an answer, providing valuable information and insights to enhance the language model training process. Both the questions and answers were manually curated by native Spanish people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This question-answer prompt completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains questions and answers with different types of rich text, including tables, code, JSON, etc., with proper markdown.

To ensure diversity, this Q&A dataset includes questions with varying complexity levels, ranging from easy to medium and hard. Different types of questions, such as multiple-choice, direct, and true/false, are included. Additionally, questions are further classified into fact-based and opinion-based categories, creating a comprehensive variety. The QA dataset also contains the question with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate varied learning experiences, the dataset incorporates different types of answer formats. These formats include single-word, short phrases, single sentences, and paragraph types of answers. The answer contains text strings, numerical values, date and time formats as well. Such diversity strengthens the Language model's ability to generate coherent and contextually appropriate answers.

This fully labeled Spanish Open Ended Question Answer Dataset is available in JSON and CSV formats. It includes annotation details such as id, language, domain, question_length, prompt_type, question_category, question_type, complexity, answer_type, rich_text.

The dataset upholds the highest standards of quality and accuracy. Each question undergoes careful validation, and the corresponding answers are thoroughly verified. To prioritize inclusivity, the dataset incorporates questions and answers representing diverse perspectives and writing styles, ensuring it remains unbiased and avoids perpetuating discrimination.

Both the question and answers in Spanish are grammatically accurate without any word or grammatical errors. No copyrighted, toxic, or harmful content is used while building this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Continuous efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to collect custom question-answer data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now ready for commercial use. Researchers, data scientists, and developers can utilize this fully labeled and ready-to-deploy Spanish Open Ended Question Answer Dataset to enhance the language understanding capabilities of their generative ai models, improve response generation, and explore new approaches to NLP question-answering tasks.

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.

This research data file contains the necessary software and the dataset for estimating the missing prices of house units. This approach combines several machine learning techniques (linear regression, support vector regression, the k-nearest neighbors and a multi-layer perceptron neural network) with several dimensionality reduction techniques (non-negative factorization, recursive feature elimination and feature selection with a variance threshold). It includes the input dataset formed with the available house prices in two neighborhoods of Teruel city (Spain) in November 13, 2017 from Idealista website. These two neighborhoods are the center of the city and “Ensanche”.

This dataset supports the research of the authors in the improvement of the setup of agent-based simulations about real-estate market. The work about this dataset has been submitted for consideration for publication to a scientific journal.

The open source python code is composed of all the files with the “.py” extension. The main program can be executed from the “main.py” file. The “boxplotErrors.eps” is a chart generated from the execution of the code, and compares the results of the different combinations of machine learning techniques and dimensionality reduction methods.

The dataset is in the “data” folder. The input raw data of the house prices are in the “dataRaw.csv” file. These were shuffled into the “dataShuffled.csv” file. We used cross-validation to obtain the estimations of house prices. The outputted estimations alongside the real values are stored in different files of the “data” folder, in which each filename is composed by the machine learning technique abbreviation and the dimensionality reduction method abbreviation.