Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
18
datasets available to search
ShareScore release 0.9.0
Dataset results
18 results for “csv dataset”
OpenCitations Meta CSV dataset of all bibliographic metadata
<p>This dataset contains all the bibliographic metadata (in CSV format) included in OpenCitations Meta. In particular, each line of the CSV file defines a bibliographic resource, and includes the following information:</p> <ul> <li><strong>[field "id"]</strong> the IDs for the document described within the line;</li> <li><strong>[field "title"]</strong> the document's title;</li> <li><strong>[field "author"]</strong> the authors of the document;</li> <li><strong>[field "pub_date"]</strong> the date of publication;</li> <li><strong>[field "venue"]</strong> information about the venue, i.e. the bibliographical resource to which the document belongs;</li> <li><strong>[field "volume"]</strong> the volume sequence identifier (e.g. a number) to which the entity belongs;</li> <li><strong>[field "issue"]</strong> the issuesequence identifier (e.g. a number) to which the entity belongs;</li> <li><strong>[field "page"]</strong> the page range of the resource described in the row;</li> <li><strong>[field "type"]</strong> the type of resource described in the row;</li> <li><strong>[field "publisher"]</strong> the entity responsible for making the resource available;</li> <li><strong>[field "editor"] </strong>the editors of the document.</li> </ul> <p>This version of the dataset contains:</p> <ul> <li>114,621,237 bibliographic entities</li> <li>298,847,794 authors and 2,465,711 editors (counted by their roles, without disambiguating individual</li> <li>711,711 publication venues</li> <li>241,783 publishers</li> </ul> <p>The zipped dataset weighs 11 GB, while, when extracted, it weighs 46 GB on an ext4 filesystem.</p> <p>Additional information about OpenCitations Meta at <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
Energy Climate dataset consitent with ENTSO-E TYNDP2020 studies (CSV & NetCDF) for ACDC-ESM
<p><strong>Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format</strong></p> <p><strong>TL;DR</strong>: this is a tidy and friendly version of a recreation of ENTSO-E's PECD 2021.3 data by using ERA5: hourly capacity factors for wind onshore, offshore, solar PV and hourly electricity demand are provided. All the data is provided for 28-71 climatic years (1950-2020 for wind and solar, 1982-2010 for demand).</p> <p><strong>Description</strong><br> Country averages of energy-climate variables generated using the Python scripts, based on the <a href="https://2020.entsos-tyndp-scenarios.eu/">ENTSO-E's TYNDP 2020 study</a>. For the following scenario's data is available</p> <ul> <li>National trends 2025 (NT 2025)</li> <li>National trends 2030 (NT 2030)</li> <li>National trends 2040 (NT 2040)</li> <li>Distributed Energy 2030 (DE 2030)</li> <li>Distributed Energy 2040 (DE 2040)</li> <li>Global Ambitions (GA 2030)</li> <li>Global Ambitions (GA 2040)</li> </ul> <p>The time-series are at hourly resolution and the included variables are:</p> <ul> <li>Generation wind offshore (aggregated for all years per scenario in a .zip)</li> <li>Generation wind onshore (aggregated for all years per scenario in a .zip)</li> <li>Generation solar photovoltaic (aggregated for all years per scenario in a .zip)</li> <li>Total energy demand (all zones combined in single file per scenario)</li> </ul> <p>The Files are provided in CSV (.csv) & NetCDF (.nc). The data is given per ENTSO-E's bidding zone as used within the TYNDP2020.<br> </p> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p>
Brussel mobility Twitter sentiment analysis CSV Dataset
<p>SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality. <br>SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.<br>This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future.<br>The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility. <br>As part of writing a chapter for the SSH CENTRE book on ‘Mobility’, we set out to analyse the sentiment of users on Twitter regarding shared and active mobility modes in Brussels. This involved us collecting tweets between 2017-2022. A tweet was collected if it contained a previously defined mobility keyword (for example: metro) and either the name of a (local) politician, a neighbourhood or municipality, or a (shared) mobility provider. The files attached to this Zenodo webpage is a csv files containing the tweets collected.”. </p>
MatKG csv Dataset
<p>This is the dataset for MatKG, the largest Knowledge Graph in the field of Material Science that is extracted from Scientific Literature. The dataset identifies some 200,000 concepts and 6.5 million relations between materials, their properties, applications, characterization methods, descriptors, synthesis methods, and symmetry labels expressed as a graph database. </p> <p>The subject_REL_object file contains the main list of entities and their relationships expressed as triple in the form [subject, rel, obj] with a 'count' paramater that is a measure of the relevance in the relation. </p> <p>The rawEntity_NER_DOI dataset gives the raw entities as extracted from the text along with their NER Tag and the DOI from which it was extracted. </p> <p>The rawEntity_Clean1_Clean2_Clean3_file gives the different cleaned version of the initially extracted raw text. </p> <p>An initial preprint of the data is available here: https://arxiv.org/abs/2210.17340</p> <p> </p>
CNF and CSV files for Lalonde dataset
<p>To facilitate reproducibility for the experiments of efficient inference with decision diagram paper, all CNF vars and CSV files are made available for the Lalonde dataset (they are too big for GitHub)</p>
Semantic links between selected CSV datasets harvested by the European Data Portal and the DBpedia knowledge graph
<p>These dataset contains the results of the interlinking process between selected csv datasets harvested by the European DAta Portal and the DBpedia knowledge graph. </p> <p>We aim at answering the following questions:<br> What are the more popular column types? This will provide hindsight about what the datasets hold and how they can be joined. It will also provide hindsight on what specific linking schemes could be applied in future elements.<br> What datasets have columns of the same type? This will suggest datasets that may be similar or related.<br> What entities appear in most datasets (co-referent entities)? This will suggest entities for which more data is published.<br> What datasets share a particular entity? This will suggest datasets that may be joined, or are related through that particular entity</p> <p>Results are provided as augmented tables, that contain the columns of the original csv, plus a metadata file in JSON-LD format. The metadata files can be loaded in an RDF-store and queried.</p> <p>Refer to the accompanying report of activities for more details on the methodolog and how to query the dataset.</p> <p><br> </p>
ETER - Full dataset (csv; March 26th, 2024)
<p>This dump includes</p> <ul> <li>the whole ETER dataset as of 26th of March 2024 (including data for the academic year 2021/2022),</li> <li>all corresponding metadata, and</li> <li>a file including all labels for variable names, and labels for special and variable codes.</li> </ul>
LENS: A LEO Satellite Network Measurement Dataset (CSV) - 202405
<p>LENS dataset 2024-05 (CSV format)</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset (CSV) - 202404
<p>LENS dataset 2024-04 (CSV format)</p> <p>Please see <a href="https://github.com/clarkzjw/LENS" target="_blank" rel="noopener">https://github.com/clarkzjw/LENS</a> for the complete description of the dataset.</p>
CSV Dataset Files and JSON OpenRefine Recipes for Alignment of the Schoenberg Dataset of Manuscripts (SDBM) Name Authority with Wikidata
<p>Dataset CSVs and JSON recipe files for OpenRefine for a project to align Name Authority records in the Schoenberg Dataset of Manuscripts (SDBM) with Wikidata Items</p>
Mapping the world's free-flowing rivers: CSV Dataset
<p>These data were originally generated by: Grill, G., Lehner, B., Thieme, M. <em>et al.</em> Mapping the world’s free-flowing rivers. <em>Nature</em> 569, 215–221 (2019). <a href="https://doi.org/10.1038/s41586-019-1111-9">https://doi.org/10.1038/s41586-019-1111-9</a>.</p> <p>The dataset here is a CSV generated from the original data curated and generated by Grill <em>et al.</em> to facilitate processing of the data using a variety of computational tools. The attribute table of the data generated in the study were extracted from an ArcGIS geodatabase format using QGIS and converted to a CSV.</p> <p>The description of the variables and attributes can be found in the documentation of the original dataset: <a href="https://figshare.com/articles/Mapping_the_world_s_free-flowing_rivers_data_set_and_technical_documentation/7688801">https://figshare.com/articles/Mapping_the_world_s_free-flowing_rivers_data_set_and_technical_documentation/7688801</a>. </p>
US FDA FAERS Drug Adverse Event Dataset Processed as Relational CSV
<p>WARNING: This is an early attempt at processing a large dataset, and I have not yet completely validated the processing scripts or the resulting CSV files.</p> <p>This is a compressed archive of the US FDA FAERS Drug Averse Event database, obtained from open.fda.gov (https://open.fda.gov) and processed with scripts that I wrote (https://github.com/dbuijs/faers2csv) to convert the 100 GB of JSON files into relational CSV that take up approximately 6 GB of space and are suitable for loading into a relational database such as PostgreSQL. </p> <p>This version of the data was processed on January 29, 2016. </p> <p>faerscsv.tar.bz2 expands into a folder called csv, with 4 relational CSV files for each source JSON file.</p> <p>faerstats.tar.bz2 expands into a folder called stats, with a text file for each CSV file containing summary statistics generated by csvstat (Python CSVkit).</p>
Dataset in csv format containing labor force data in a synthetic population
Open the record for dataset details and reuse information.
11 Benchmark Clean-Clean ER datasets in CSV format
<p>Contains:</p> <ul> <li><strong>D1</strong>: Contains restaurant descriptions, first introduced in OAEI 2010.</li> <li><strong>D2</strong>: Includes duplicate products from Abt.com and Buy.com.</li> <li><strong>D3</strong>: Matches product descriptions from Amazon and Google Base.</li> <li><strong>D4</strong>: Compares bibliographic data from DBLP and ACM.</li> <li><strong>D5, D6, D7</strong>: Contain descriptions of television shows and movies from TheTVDB, IMDb, and TMDb.</li> <li><strong>D8</strong>: Matches product descriptions from Walmart and Amazon.</li> <li><strong>D9</strong>: Involves bibliographic data from DBLP and Google Scholar.</li> <li><strong>D10</strong>: Links movie descriptions from IMDb and DBpedia.</li> <li><strong>D11</strong>: A large-scale dataset with millions of heterogeneous entities from two DBpedia versions spanning a 3-year gap.</li> </ul>
Master-thesis supplement Del 2 CSV-files of dataset for calanus and others
<p>CSV-files of dataset for calanus and others</p>
Business power against redistribution – the case of watered-down tax reform in Poland (csv. dataset)
<p>For our research purpose we compile a dataset of press articles, concerning the tax reform, published between May 15th, 2021 and October 1st, 2021 – namely, between the day of the announcement of the “Polish Deal” and the day when the final legal act with changes favourable for the high income self-employed was passed by the Sejm. We consider articles from the three most opinion-forming Polish newspapers – Rzeczpospolita, Gazeta Wyborcza and Dziennik Gazeta Prawna – according to the ranking prepared by the Institute of Media Monitoring for 2021. The ranking is prepared based on the number of citations of articles/statements of one medium by other media. Therefore, it may be interpreted as a measure of the impact of a given medium on the public debate.</p> <p>We study the electronic versions of the paper editions and include in the dataset all the articles that somehow concerned the tax reform. Additionally, we have separately distilled all the comments of external experts used in the articles – usually short (1-5 sentences) statements. The initial dataset consisted of 359 articles and 378 comments.</p>
Csv subset of the Agri4cast (https://agri4cast.jrc.ec.europa.eu/DataPortal/Index.aspx) dataset for the Spain region
<p>Educational climate dataset</p>
ETER - Full dataset (csv)
<p>This dump includes</p> <ul> <li>the whole ETER dataset as of 27th of April 2023,</li> <li>all corresponding metadata, and</li> <li>a file including all labels for variable names, and labels for special and variable codes.</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.