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.
6,782
datasets available to search
ShareScore release 0.9.0
Dataset results
6,782 results for “project”
Projects' Statistical Information
<div> <p>The dataset contains statistical information about the projects we used in our experiments.</p> <p> </p> </div> <p> </p>
Reinforcing RRI project reviews
<p>Collection of reviews of 203 Responsible Research and Innovation (RRI) related projects. Short reviews on structured templates including objectives, key results, Reinforcing relevant approches and tools, and link to the project web-site for further information. Files named after reviewing organization and reviewed project (acronyms).</p>
Reddit's NetSec forum GitHub projects, typology, maturity and popularity indicators
<p>Dataset including information of GitHub projects shared on Reddit's forum /r/netsec. It includes information about the posts itselves, like the author, title, image, tags, number of upvotes and number of comments, as well as repository information, such as number of stars, forks, issues, collaborators and the repository URL and last modification date.</p>
Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
<p>Here are the datasets for our publication entitled "<a href="https://www.nature.com/articles/s41467-024-48779-z">Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</a>" published in Nature Communications. </p> <p>The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature. A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.</p> <p>Other details can be found in "Data Introduction.pdf" file.</p> <p>The <strong>Python Code</strong> for reading and preprocessing this dataset is available at: <a href="https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library">https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library</a></p> <p>Summary of articles using the this dataset: <a href="https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary">https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary</a></p> <p> </p> <p>If you find this data helpful, please consider citing our paper:</p> <p>Wang, F., Zhai, Z., Zhao, Z. <em>et al.</em> Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. <em>Nat Commun</em> <strong>15</strong>, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z</p>
IRIS Carbon Mapping Project: Curated Dataset
<p>Dataset to support IRIS Carbon Mapping Project final report.</p>
Data from: the great tit HapMap project: a continental-scale analysis of genomic variation in a songbird
<p>A major aim of evolutionary biology is to understand why patterns of genomic diversity vary within taxa and space. Large-scale genomic studies of widespread species are useful for studying how environment and demography shape patterns of genomic divergence. Here, we describe one of the most geographically comprehensive surveys of genomic variation in a wild vertebrate to date; the great tit (<em>Parus major</em>) HapMap project. We screened <em>ca</em> 500,000 SNP markers across 647 individuals from 29 populations, spanning ~30 degrees of latitude and 40 degrees of longitude - almost the entire geographic range of the European subspecies. Genome-wide variation was consistent with a recent colonisation across Europe from a South-East European refugiam, with bottlenecks and reduced genetic diversity in island populations. Differentiation across the genome was highly heterogeneous, with clear "islands of differentiation", even among populations with very low levels of genome-wide differentiation. Low local recombination rates were a strong predictor of high local genomic differentiation (F<sub>ST</sub>), especially in island and peripheral mainland populations, suggesting that the interplay between genetic drift and recombination causes highly heterogeneous differentiation landscapes. We also detected genomic outlier regions that were confined to one or more peripheral great tit populations, probably as a result of recent directional selection at the species' range edges. Haplotype-based measures of selection were related to recombination rate, albeit less strongly, and highlighted population-specific sweeps that likely resulted from positive selection. Our study highlights how comprehensive screens of genomic variation in wild organisms can provide unique insights into spatio-temporal evolutionary dynamics.</p>
Individual project on theme "Cervus L." (MOOC: CompTech in biology)
<p><strong><span>Данный кейс является результатом реализации индивидуального проекта </span></strong><em><span>Попова Игоря</span></em><strong><span> по теме "</span></strong><em><span>C</span></em><em><span>ervus</span></em><em><span> </span></em><span>L</span><span>.</span><strong><span>" в рамках онлайн курса "Компьютерные технологии в биологии" (автор – А.В. Ваганов) (https://stepik.org/course/60859/promo)</span></strong></p>
Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900-2050. Data and Code. Project BES-SIM 1.
<p>This archive contains all scripts and data used for analysis and figures for the paper <strong>Pereira et al. (2024). Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900-2050. Science. </strong>The paper is the result of the BES SIM 1 project. </p>
Estimating Usage Of Open Source Projects - Flutter Telemetry Case Study - MSR' 24
<p>This dataset (CSV) was assembled to support analysis within a case study that will be published in the proceedings of the <a href="https://conf.researchr.org/home/msr-2024">Mining Software Repositories</a> conference (MSR ‘24) April 14-15 2024: <a title="Estimating Usage Of Open Source Projects" href="https://doi.org/10.1145/3643991.3645066">Estimating Usage Of Open Source Projects.</a></p> <p>This case study explored whether publicly available metrics could serve as proxies to estimate usage of an open source project. Using the <a href="https://flutter.dev/">Flutter</a> project as our case study, we collected monthly proxy metrics from GitHub, StackOverflow and Slack to compare with Flutter’s monthly active user count over the same time period: January 2018 through February 2021.</p> <p>All metrics correspond to the last day of the month and/or represent aggregate activity in that month. Data from GitHub shows aggregate activity counts across the entire <a href="https://github.com/flutter">Flutter GitHub organization</a> (up to 33 repositories). </p> <p>Our specific metrics include:</p> <div> <table> <tbody> <tr> <td> <p>Source</p> </td> <td> <p>Metric</p> </td> <td> <p>Aggregation method</p> </td> <td> <p>Details</p> </td> </tr> <tr> <td> <p>Flutter</p> </td> <td> <p>Monthly Active Users (MAU)</p> </td> <td> <p>Google internal tooling</p> </td> <td> <p>Flutter users active in the last 30 days, collected on the last day of each month</p> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>PullRequest Authors in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> <p>As Google governs changes to this code base, we excluded known Google employees in code change related metrics </p> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Issues Created in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Issue Authors in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Fork events in month</p> </td> <td> <p><a href="http://gharchive.org">gharchive.org</a></p> </td> <td> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Fork cumulative count at the end of month</p> </td> <td> <p><a href="http://gharchive.org">gharchive.org</a></p> </td> <td> </td> </tr> <tr> <td> <p>StackOverflow</p> </td> <td> <p>Question Authors in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>StackOverflow</p> </td> <td> <p>Questions in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>Slack</p> </td> <td> <p>Claimed (cumulative) members at the end of the month</p> </td> <td> <p>fluttercommunity.slack</p> </td> <td> </td> </tr> <tr> <td> <p>Slack</p> </td> <td> <p>Cumulative messages at the end of the month</p> </td> <td> <p>fluttercommunity.slack</p> </td> <td> </td> </tr> </tbody> </table> </div> <p>Tools: </p> <ul> <li> <p>We used an instance of <a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a> hosted by <a href="https://bitergia.com/">Bitergia</a> to aggregate GitHub PullRequest Authors, GitHub Issue Authors, GitHub Issues, across all repositories under the Flutter Organization, and StackOverflow Question Authors, and StackOverflow Questions for questions that mention Flutter. We used the <a href="http://github.com/chaoss/grimoirelab-sortinghat">Sorting Hat</a> of feature GrimoireLab to identify Google employees in this sample.</p> </li> </ul> <ul> <li> <p><a href="http://gharchive.org">GHArchive</a> via <a href="https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code">BigQuery</a> was used to count GitHub Star and Fork events across all repositories under the Flutter Organization</p> </li> <li> <p>We pulled Slack activity directly from <a href="http://fluttercommunity.slack.com">Flutter's slack channel </a>dashboard</p> </li> </ul>
Hellenistic-Roman archaeobotany data - MetaMobility project
<p>This database includes archaeobotanical data from the Central and Eastern Mediterranean and Middle East dating to the Hellenistic and Roman periods. It was created as part of the MetaMobility project (EXCELLENCE/0421/0376), which is implemented under the programme of social cohesion “THALIA 2021-2027” cofunded by the European Union, through the Research and Innovation Foundation.</p>
Harmonized directory of beneficiaries of the PDAC project - Congo-Brazzaville
<p><strong>Description:</strong> This datasets contains a list of all groups of agricultural, livestock, fisheries and agrifoodstuff initiatives that benefitted from the "Projet d'appui au Dévéloppement de l'Agriculture Commerciale" (PDAC) project from the Ministère de l'Agriculture de l'Elevage et de la Pêche (MAEP) of the Republic of Congo, along with their locations at departmental and district levels, and initiative names. For more details see the project details at <a href="https://pdacmaep.cg">https://pdacmaep.cg</a></p> <p><strong>Scope of data :</strong> This dataset lists 850+ beneficiary initiatives country-wide, covering 33 activities along the 12 departments and 99 districts. List of activities is as follows :</p> <ul> <li>aliment pour bétail</li> <li>ananas</li> <li>apiculture</li> <li>arachide</li> <li>arboriculture</li> <li>banane</li> <li>cacao</li> <li>chambre froide</li> <li>commercialisation</li> <li>élevage bovin</li> <li>élevage de cailles</li> <li>élevage ovins</li> <li>élevage pondeuse</li> <li>élevage porcin</li> <li>fumure</li> <li>gingembre</li> <li>grenadille</li> <li>haricot</li> <li>huile de palme</li> <li>igname</li> <li>maïs</li> <li>manioc</li> <li>maraîchage</li> <li>pêche</li> <li>pisciculture</li> <li>pois d'angol</li> <li>pomme de terre</li> <li>prestation</li> <li>production</li> <li>service vétérinaire</li> <li>soja</li> <li>taro</li> <li>transformation</li> </ul> <p><strong>Data Collection and Processing</strong>: This dataset was assembled from the different departmental directories of beneficiaries openly available online on April 18th, 2024 from the project website at <a href="https://pdacmaep.cg/index.php?page=documents">https://pdacmaep.cg/index.php?page=documents</a> and contains harmonized activity type, initiative name, department and district. Contact names and phone coordinates were removed for privacy.</p> <p><strong>Project Context</strong>: This work is part of the <a href="https://www.cirad.fr/dans-le-monde/cirad-dans-le-monde/projets/projet-pudt-congo">PUDT Congo</a> project.</p>
Data and Code for "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" Paper Submission to AGU GRL
<p>This contains the emergent constraint code, the offline CMIP6 hydrology data, and the shapefiles for each region used in the paper "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" submitted to AGU GRL.</p>
Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach - Data Supplement
<p>This contribution contains data and analysis scripts for the manuscript "Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach" by Lenin Del Rio Amador, Mathieu Boudreault and David A. Carozza.</p>
Soil grid data for agricultural fields in Spain for STEROPES (EJP Soil) project (ECe, texture, soil organic carbon, pH)
<p><span>Soil data collected in an agricultural area with vegetable crops in Spain (Campo de Cartagena). The data refers to soil properties of 141 soil samples collected at a depth of 0-10 cm, considering a regular sampling grid, in different commercial fields with varying soil salinity. The samples were collected at a period when the soil was bare, during two consecutive summers, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, soil texture, stone content and pH1:2.5. </span></p> <p><span> </span></p> <p><span>The data may be representative of the soil conditions of the area, which is an intensive productive agricultural low land, potentially prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP3, WP5 and WP6).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing.</span></p> <p><span> </span></p> <p><span>Data in the form of MS Excel file (xlsx).</span></p> <p><span> </span></p> <p><span> </span></p>
GENEActiv accelerometer files collected in Vanuatu during FALAH project (anonymized version - second part)
<p><a title="GENEActiv" href="https://activinsights.com/technology/geneactiv/" target="_blank" rel="noopener">GENEActiv</a> accelerometer .csv and .RData files converted with a 1 second epoch from raw GENEActiv .bin files recorded in Vanuatu during <a title="FALAH website" href="https://falah.unc.nc/" target="_blank" rel="noopener">FALAH</a> project. Devices are 60-Hz triaxial accelerometers.</p> <p>This dataset also contains <strong>participantCharacteristics.csv</strong> that povides basic information about participants and <strong>read_a_binFile_share.R</strong> that is a short R code aiming at converting and saving accelerometer data from .bin files in 1 second epoch .csv files (consider the Methods section).</p> <p>Participant characteristics: 13 to 17 years old students.</p> <p>Number of participants: 72.</p> <p>Year of the study: 2023.</p> <p>Place of the study: Vanuatu.</p> <p>The accelerometer .csv and .RData files with a 1 second epoch and extracted from raw .bin files are available in the restricted datasets:</p> <ul> <li><a title="Anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043332" target="_blank" rel="noopener">anonymized version (first part)</a></li> <li><a title="Anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089478" target="_blank" rel="noopener">anonymized version (second part)</a></li> </ul> <p>The accelerometer raw .bin files are available in the restricted datasets:</p> <ul> <li><a title="Non-anonymized dataset (first part)" href="https://doi.org/10.5281/zenodo.14043547" target="_blank" rel="noopener">non-anonymized version (first part)</a></li> <li><a title="Non-anonymized dataset (second part)" href="https://doi.org/10.5281/zenodo.14089527">non-anonymized version (second part)</a></li> </ul> <p>Other participant characteristics (age, place of living, ...) and responses to questionnaires are available in <a title="Information and questionnaire associated with GENEActiv accelerometer files collected in Vanuatu during FALAH project (non-anonymized information)" href="https://doi.org/10.5281/zenodo.14189884" target="_blank" rel="noopener">a restricted non-anonymized dataset</a>.</p>
Real-Time Dataset of Metheorological Measurements Collected During the Project
<p>This dataset contains a collection of real-time meteorological data during the Resisto project. This information has been recorded and is structured into different fields, each representing a specific meteorological variable of the deployed sensors in the Doñana National Park. </p>
Real-Time Dataset of Fire Sensor Measurements Collected During the Resisto Project
<p>This dataset contains real-time environmental measurements from fire detection sensors across multiple locations. These sensors has been deployed on diferent locations, principally on the Doñana National Park. </p>
X-Rays micro-tomography of 54 Egyptian predynastic ceramic vessels from the MAN (Musée d'Archéologie Nationale-Domaine national du château de Saint-Germain-en-Laye, France) made in the frame of the project TECHNOPREGYPT 2021/43/P/HS3/03262. STACK DATA
<p><span>54 complete pots and sherds from the collection of the <span><span>Musée d’Archéologie Nationale-Domaine national du château de Saint-Germain-en-Laye, France, were scanned with X-Rays microtomography (CT-Scan) to carry out the technological analysis of the samples. The aim was to be able to see the internal structure of the ceramic vessels to reconstruct the manufacturing process. </span></span></span></p> <p><span>The scans are part of the project </span><span>TECHNOPREGYPT</span><em><span> <span>Ceramic technology and the socio-political environment of Predynastic Egypt</span></span></em><span><span> </span></span><span><span>2021/43/P/HS3/03262 directed by dr Jade BAJEOT. </span></span></p> <p><a title="TECHNOPREGYPT website" href="https://technopregypt.iksio.pan.pl/index.php/about-the-project"><span><span>TECHNOPREGYPT website</span></span></a></p> <p> </p> <p><strong>Funding</strong></p> <p><span>This research was led at the Institute of Mediterranean and Oriental Cultures, Polish Academy of Sciences and was </span><span>co-funded by the Polish National Science Centre and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 945339.</span><strong><span> </span></strong><span>For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.</span></p>
Karyorelict ciliate transcriptome assemblies from project Karyocode
<p>Transcriptome assemblies of karyorelict ciliates, as described in the publication "Karyorelict ciliates use an ambiguous genetic code with context-dependent stop/sense codons" (https://doi.org/10.24072/pcjournal.141).</p> <p>Files are named with internal short names for each read library. The corresponding INSDC accessions of the read libraries are documented in the following spreadsheet:</p> <p>"Dataset accessions for comparative analysis of ciliate genetic codes", <a href="https://doi.org/10.17617/3.XWMBKT" target="_blank" rel="noopener">https://doi.org/10.17617/3.XWMBKT</a>, Edmond, V1; Table_S1.xlsx [fileName]</p> <p>Files with the suffix ".polyA_min7.fasta" contain the subset of assembled transcripts with a poly-A tail of at least 7 nt; this was done to filter out likely bacterial contaminants.</p>
Dataset corresponding to the project "Aging, loneliness, and well-being in Spain"
<p>Dataset corresponding to the project "Aging, loneliness, and well-being in Spain"</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.