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.
747
datasets available to search
ShareScore release 0.7.1
Dataset results
747 results for “Open Data”
Global demand data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>resource file </strong>contains demand time-series generated by <a href="https://github.com/niclasmattsson/GlobalEnergyGIS/blob/b23206f8701acafdf7359f9cc952dfd4e7b819e5/src/downloaddatasets.jl">GEGIS</a> covering the world. The time series are produced for different socio-economic scenarios (SSP), weather years, and prediction years<strong>.</strong></p>
Is Open Data Strategy for Covid-19 used for other global health threats? A systematic review of the literature
<p>Dataset result of the literature review run between march and may 2022 on PubMed, Cinahl, Scopus and Google Scholar about open data and growing trend in scientific research about infection risk comparing Covid-19, Anti Microbial Resistance (AMR), and Health Assistance Correlated Infections (HAIs).</p>
Open data
<p>file1: AGV wheel engine speed</p> <p>file2: AGV wheel engine acceleration</p> <p>file 3: acceleration and temperature registered by mean of sensors, each one installed on a different container, during the shipment of components</p>
Integrated Statistical Indicators from Scottish Linked Open Government Data
<p>Integrated statistical indicators that were retrieved from the official Scottish data portal in order to facilitate the exploitation of Machine Learning methods in Open Government Data. Data include 60 statistical indicators from seven categories such as health and social care, housing, and crime and justice. The indicators refer to the 6,976 “2011 data zones” of Scotland, while the year of reference is 2015. Data are ready to be used by the research community, students, policy makers, and journalists and give rise to plenty of social, business, and research scenarios that can be solved using Machine Learning technologies and methods.</p>
Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition"
<p>Open data for the article "Low-resistivity, high-resolution W-C electrical contacts fabricated by direct-write focused electron beam induced deposition", which will be published in Open Research Europe</p>
Image data for bioRxiv article named: mtFociCounter - Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci
<p>Raw imaging data to reproduce and test the findings of the bioRxiv article: <strong>mtFociCounter </strong>- Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci. It contains data from three imaging days and 2 or three technical replicates on each day.</p> <p> </p>
Bayesian Samples and Data Behind Figures: Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I
<p>Auxiliary data associated with the article <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.6145P/abstract">"Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I: M 35, NGC 6819, NGC 188" by Penev, K & Schussler, J</a></p> <p>The type of data corresponds to a particular filename format. Bayesian samples are in HDF5 format, directly as saved by the <a href="https://emcee.readthedocs.io/en/stable/index.html">emcee</a> sampler (see <a href="https://emcee.readthedocs.io/en/stable/user/backends/">https://emcee.readthedocs.io/en/stable/user/backends/</a>). All other files are in AAS-journal style machine readable tables format generated by <a href="https://github.com/cds-astro/cds.pyreadme">cdspyreadme</a> python library.</p> <p>Description of contents by filename format:</p> <pre><code><CLUSTER>_<BINARY ID>_.*.h5</code></pre> <p>Bayesian analysis samples constraining the tidal dissipation efficiency of the given binary. The values of the sampled system and tidal dissipation parameters are stored as blobs (<a href="https://emcee.readthedocs.io/en/stable/user/blobs/">https://emcee.readthedocs.io/en/stable/user/blobs/)</a></p> <pre><code><CLUSTER>_<BINARY ID>_lgQ_period.mrt</code></pre> <p>The 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary as a function of tidal period</p> <pre><code><CLUSTER>_<BINARY ID>_burnin_period.mrt</code></pre> <p>The MCMC burn-in period before the 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary are considered converged (see article text).</p> <pre><code><CLUSTER>_<BINARY ID>_cdfstd_period.mrt</code></pre> <p>The standard deviation of the <span class="math-tex">\(CDF(\log_{10}Q_\star')\)</span> for the given binary as a function of tidal period for each of the quantiles. The maximum likelihood value is the target percentile, i.e. one of: 2.3%, 15.9%, 84.1%, and 97.7%</p>
Data for FEgrow: An Open-Source Molecular Builder and Free Energy Preparation Workflow
<p>Data illustrating the use of de novo design in building and scoring protein-ligand complexes.</p> <p>This is relationship to the FEgrow publication with the intiial preprint here: <br> https://chemrxiv.org/engage/chemrxiv/article-details/6287bb98a42e9c78d34769f6<br> </p> <p>The FEgrow software snapshot used can be found here: https://zenodo.org/record/7105647#.YzFwINLMIUE</p>
Open source measurement data of the ground water physio-chemical parameters in the Peshawar District, Pakistan
<p>This excel file provides the measurement data of the physio-chemical parameters in the Peshawar district, Pakistan. The physio-chemical parameters include pH, electrical conductivity, total dissolved solids, Ca hardness, Mg hardness, Total hardness, Turbidity, Nitrate and Chloride. The data also include the latitude and longitude and location and depth to groundwater level (water table). The data was collected and sample were analyzed in June-August 2012.</p> <p>The full data collection and case studies are described in Adnan and Iqbal 2014, Adnan et al. 2018 and Adnan et al. 2019.</p>
Making the Most out of a Hydrological Model Dataset: Sensitivity Analyses to Open the Model Black-Box (data and code)
<p>This is "data and code" repository for the Water Resources Research Article 2017WR020401 by Borgonovo et al. (2017): "Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box". Each sub-directory contains the Matlab or R scripts to reproduce all paper plots. </p> <p>Note, that the data of this repository (i.e. under ./data_input ) are identical to the data analysed by Rakovec et al. (2014).</p> <p>References:</p> <ul> <li>Borgonovo, E., Lu, X., Plischke, E., Rakovec, O. and Hill, M. C. (2017), Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box. Water Resour. Res.. Accepted Author Manuscript. doi:10.1002/2017WR020767</li> <li>Rakovec, O., M. C. Hill, M. P. Clark, A. H. Weerts, A. J. Teuling, and R. Uijlenhoet (2014), Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 409–426, doi:10.1002/2013WR014063.</li> </ul>
PLOS ONE – a case study of citation analysis of research papers based on the data in an open citation index (The OpenCitations Corpus)
<p>This is a dataset used in and produced by research described in article "PLOS ONE - a case study of citation analysis of research papers based on the data in an open citation index (The OpenCitations Corpus)" that is translation of the original Polish text "PLOS ONE – studium przypadku analizy cytowań prac naukowych na podstawie danych otwartego indeksu cytowań (OpenCitations Corpus)" published by EBiB bulletin (2017, No 176).</p> <p>Data were extracted, as nodes (PLOS_cited_nodes.csv) and edges (PLOS_edges.csv) files from the OpenCitations Corpus (http://opencitations.net/download) on 2017.07.25 and describe all cited papers published by PLOS ONE (nodes), and all citing relations (edges). The research was conducted using Gephi (https://gephi.org/) platform so the same source data are also avaiable as GEXF file (for "one-click" import capabilities). In addition, the same data are published in NET format (but be warned that due to this format limitations, information about the publication year of papers has been lost) used by PAJEK platform, as it is very popular tool for analysis of network data.</p> <p>Published figures have prefix names corresponding to figures captions in the original paper, where they have been thoroughly discussed. This data set contains also the additional figure not published in the article, showing most cited paper with citing chains of articles of lenght not greater than 3.<br> These pictures have much better quality than those published in the article, which allows for "drill down"/zoom-in analysis and large format printing.</p>
Data from: The State of OA: A large-scale analysis of the prevalence and impact of Open Access articles
<p>This is the raw data behind the publication: </p> <p><strong>The State of OA: A large-scale analysis of the prevalence and impact of Open Access articles.</strong></p> <p>Despite growing interest in Open Access (OA) to scholarly literature, there is an unmet need for large-scale, up-to-date, and reproducible studies assessing the prevalence and characteristics of OA. We address this need using oaDOI, an open online service that determines OA status for 67 million articles. We use three samples, each of 100,000 articles, to investigate OA in three populations: 1) all journal articles assigned a Crossref DOI, 2) recent journal articles indexed in Web of Science, and 3) articles viewed by users of Unpaywall, an open-source browser extension that lets users find OA articles using oaDOI. We estimate that at least 28% of the scholarly literature is OA (19M in total) and that this proportion is growing, driven particularly by growth in Gold and Hybrid. The most recent year analyzed (2015) also has the highest percentage of OA (45%). Because of this growth, and the fact that readers disproportionately access newer articles, we find that Unpaywall users encounter OA quite frequently: 47% of articles they view are OA. Notably, the most common mechanism for OA is not Gold, Green, or Hybrid OA, but rather an under-discussed category we dub Bronze: articles made free-to-read on the publisher website, without an explicit Open license. We also examine the citation impact of OA articles, corroborating the so-called open-access citation advantage: accounting for age and discipline, OA articles receive 18% more citations than average, an effect driven primarily by Green and Hybrid OA. We encourage further research using the free oaDOI service, as a way to inform OA policy and practice.</p>
Cities' open data portals: Current Status
<p>This is the whole sample of our survey regarding the current status of cities' open data portals. Asking data users from several backgrounds this survey aimed to determine the most used features, most important barriers, and data users perceptions regarding open geographic data available in cities' open data portals. Our goal was studying the way stakeholders especially developers and analysts looking and reuse geographic data in cities.</p> <p>This survey has 21 questions, mostly multiple choice questions, but also include open-questions, the study was entirely voluntary and publicly shared, having more replies in latino American countries and Spain. Most of the replies are in Spanish.</p> <p> </p>
Open access in Africa: scopus citation data
<p>The following citation dataset was retrieved from Scopus in June 24, 2017 (3am, Western Indonesian time).</p> <p>It consists of 3 sets of data based on our searches. Each search was saved both in 'csv' and 'bib':</p> <ol> <li>OA_Africa_inTitle.xxx: "Open Access" AND Africa IN TITLE</li> <li>OA_Africa_inTitle_inAbstract_inKeywords.xxx: "Open Access" AND Africa IN TITLE, IN ABSTRACT, IN KEYWORDS</li> <li>OAmovement_Africa_inTitle_inAbstract_inKeywords.xxx: "Open Access movement" AND Africa IN TITLE, IN ABSTRACT, IN KEYWORDS</li> </ol> <p>The access to Scopus was provided by The Central Library of Institut Teknologi Bandung (Indonesia)</p>
Data from: Open access levels: a quantitative exploration using Web of Science and oaDOI data
<p>This is the raw data behind the publication (on PeerJ Preprints):</p> <p><strong>Open access levels: a quantitative exploration using Web of Science and oaDOI data</strong></p> <p>Across the world there is growing interest in open access publishing among researchers, institutions, funders and publishers alike. It is assumed that open access levels are growing, but hitherto the exact levels and patterns of open access have been hard to determine and detailed quantitative studies are scarce. Using newly available open access status data from oaDOI in Web of Science we are now able to explore year-on-year open access levels across research fields, languages, countries, institutions, funders and topics, and try to relate the resulting patterns to disciplinary, national and institutional contexts. With data from the oaDOI API we also look at the detailed breakdown of open access by types of gold open access (pure gold, hybrid and bronze), using universities in the Netherlands as an example. There is huge diversity in open access levels on all dimensions, with unexpected levels for e.g. Portuguese as language, Astronomy & Astrophysics as research field, countries like Tanzania, Peru and Latvia, and Zika as topic. We explore methodological issues and offer suggestions to improve conditions for tracking open access status of research output. Finally, we suggest potential future applications for research and policy development. We have shared all data and code openly.</p>
MJFF Data Community - Creative Commons Training: Copyright and Open Licensing
<p>This training, provided by Shanna Hollich, the Learning and Training Manager of Creative Commons (CC), was hosted by the Michael J. Fox Foundation's Data Community of Practice (DCOP). For more information on the DCOP, please contact: researchcommunity@michaeljfox.org.</p> <p>In the ever-evolving digital landscape, the management of research outputs, including data licensing and copyright, is of utmost importance. This 1.5-hour training provided a forum for participants to learn more about open licensing and copyright. It also aimed to equip participants with the knowledge and best practices they need to effectively navigate the complexities of CC licensing and copyright when using and generating research outputs such as scholarly publications, datasets, and white papers.<br><br>By the end of the workshop, participants developed an understanding of the basic principles of copyright, how it works, and where it applies. Participants are now able to describe the benefits of open licensing and the basics of how Creative Commons licenses work, have a deeper understanding of how research outputs interact with copyright and open licensing, and know where to find additional information and resources.</p> <p>To access a stream of this video with variable resolution, please <a href="https://share.vidyard.com/watch/5vMMsxsZK6q48yNHsDTkPe" target="_blank" rel="noopener">visit this link</a>.</p>
A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on Parkinson's Disease. We provide data extracted via web scraping, along with metadata resulting from the extraction process using the NCBI API. The data pertains to the article titled 'A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation'.</p> <h2>Metadata Description</h2> <ul> <li> <h3>Analisys_MJFF_05_04_2024.xlsx</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>AU</td> <td>List of authors in abbreviated format.</td> <td>Text</td> </tr> <tr> <td>AF</td> <td>List of authors with full names.</td> <td>Text</td> </tr> <tr> <td>TI</td> <td>Full title of the article.</td> <td>Text</td> </tr> <tr> <td>SO</td> <td>Name of the journal or publication.</td> <td>Text</td> </tr> <tr> <td>SO_CO</td> <td>Country of origin of the publication.</td> <td>Text</td> </tr> <tr> <td>LA</td> <td>Language of the article.</td> <td>Text</td> </tr> <tr> <td>DT</td> <td>Type of document, such as "Journal Article".</td> <td>Text</td> </tr> <tr> <td>DE</td> <td>Keywords or descriptors associated with the article.</td> <td>Text</td> </tr> <tr> <td>MESH</td> <td>MeSH terms that describe the content of the article.</td> <td>Text</td> </tr> <tr> <td>DI</td> <td>Digital Object Identifier (DOI).</td> <td>Text</td> </tr> <tr> <td>PG</td> <td>Number of pages or page range.</td> <td>Numeric</td> </tr> <tr> <td>GRANT_ID</td> <td>Identification of funding, when available.</td> <td>Text</td> </tr> <tr> <td>GRANT_ORG</td> <td>Organization that provided the funding.</td> <td>Text</td> </tr> <tr> <td>UT, PMID</td> <td>Unique identifiers of the article.</td> <td>Numeric</td> </tr> <tr> <td>DB</td> <td>Name of the database where the article is indexed.</td> <td>Text</td> </tr> <tr> <td>AU_UN</td> <td>Information about the academic unit or institution of the authors.</td> <td>Text</td> </tr> </tbody> </table> <ul> <li> <h3>References_MJFF_v2_Final_Corrected.csv</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>Title</td> <td>Name of the article or publication.</td> <td>Text</td> </tr> <tr> <td>Authors</td> <td>List of authors who contributed to the article.</td> <td>Text</td> </tr> <tr> <td>Journal Name</td> <td>Name of the journal or periodical where the article was published.</td> <td>Text</td> </tr> <tr> <td>Publisher</td> <td>Name of the publisher who published the article.</td> <td>Text</td> </tr> <tr> <td>Volume</td> <td>Volume number of the journal in which the article appears.</td> <td>Numeric or Text</td> </tr> <tr> <td>Edition Number</td> <td>Number of the edition of the journal in which the article is found.</td> <td>Numeric or Text</td> </tr> <tr> <td>Starting Page</td> <td>Number of the first page of the article in the publication.</td> <td>Numeric</td> </tr> <tr> <td>Ending Page</td> <td>Number of the last page of the article.</td> <td>Numeric</td> </tr> <tr> <td>Publication Date</td> <td>Date on which the article was published.</td> <td>Date</td> </tr> <tr> <td>Open Access Status</td> <td>Indicates whether the article is available in open access.</td> <td>Text</td> </tr> <tr> <td>License</td> <td>Type of license under which the article was published.</td> <td>Text</td> </tr> <tr> <td>DOI (Digital Object Identifier)</td> <td>Unique identifier for the article that provides a permanent link to the online access.</td> <td>Text</td> </tr> <tr> <td>OA Location URL</td> <td>Direct URL to the article, if available in open access.</td> <td>Text</td> </tr> <tr> <td>Citation Count</td> <td>Number of times the article has been cited by other publications.</td> <td>Numeric</td> </tr> </tbody> </table> <p> </p>
Open-source quality control routine and multi-year power generation data of 175 PV systems
<p><strong>Description</strong></p> <p>The repository contains an extensive dataset of PV power measurements and a python package (qcpv) for quality controlling PV power measurements. The dataset features four years (2014-2017) of power measurements of 175 rooftop mounted residential PV systems located in Utrecht, the Netherlands. The power measurements have a 1-min resolution.</p> <p><strong>PV power measurements</strong></p> <p>Three different versions of the power measurements are included in three data-subsets in the repository. Unfiltered power measurements are enclosed in <em>unfiltered_pv_power_measurements.csv</em>. Filtered power measurements are included as <em>filtered_pv_power_measurements_sc.csv </em>and<em> filtered_pv_power_measurements_ac.csv</em>. The former dataset contains the quality controlled power measurements after running single system filters only, the latter dataset considers the output after running both single and across system filters. The metadata of the PV systems is added in<em> metadata.csv</em>. This file holds for each PV system a unique ID, start and end time of registered power measurements, estimated DC and AC capacity, tilt and azimuth angle, annual yield and mapped grids of the system location (north, south, west and east boundary).</p> <p><strong>Quality control routine</strong></p> <p>An open-source quality control routine that can be applied to filter erroneous PV power measurements is added to the repository in the form of the Python package qcpv (<em>qcpv.py</em>). Sample code to call and run the functions in the qcpv package is available as <em>example.py.</em></p> <p><strong>Objective</strong></p> <p>By publishing the dataset we provide access to high quality PV power measurements that can be used for research experiments on several topics related to PV power and the integration of PV in the electricity grid.</p> <p>By publishing the qcpv package we strive to set a next step into developing a standardized routine for quality control of PV power measurements. We hope to stimulate others to adopt and improve the routine of quality control and work towards a widely adopted standardized routine. </p> <p><strong>Data usage</strong></p> <p>If you use the data and/or python package in a published work please cite: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Units</strong></p> <p>Timestamps are in UTC (YYYY-MM-DD HH:MM:SS+00:00).</p> <p>Power measurements are in Watt.</p> <p>Installed capacities (DC and AC) are in Watt-peak.</p> <p><em><strong>Additional information</strong></em></p> <p>A detailed discussion of the data and qcpv package is presented in: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Corrections are discussed in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2024. </em><em>Erratum: Open-source quality control routine and multiyear power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Acknowledgements </strong></p> <p>This work is part of the Energy Intranets (NEAT: ESI-BiDa 647.003.002) project, which is funded by the Dutch Research Council NWO in the framework of the Energy Systems Integration & Big Data programme. The authors would especially like to thank the PV owners who volunteered to take part in the measurement campaign. </p>
washopenresearch: Dataset about open research data information in Water, Sanitation, and Hygiene
The goal of washopenresearch is to provide an overview of open research data related to Water Sanitation and Hygiene (WASH). The package provides access to two datasets `washdev` and `uncnewsletter`. Each dataset collects information on scientific articles about (1) article metadata (e.g. title, first author, correspondence author), (2) supplementary material information, (3) data availability statement, and (4) semantic information (e.g. keywords).
Supporting material for: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data
<p>Supplementary material for the paper called: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data. The data without "indexes" in the name are map-projected M3 cubes, they can be used in the python library <i><strong>MoonIndex </strong></i>to obtain the spectral indexes stored in the files with "indexes" in the name.</p><p>This research was done on the framework of the EXPLORE project, that has received funding from the European Union's 2020 research and innovation program under grant agreement No 101004214. </p>
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.