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.
8,537
datasets available to search
ShareScore release 0.9.0
Dataset results
8,537 results for “Openness”
Dataset related to the publication Rotbarth et al 2024, "Boreal forests are heading for an open state", PNAS
<p>This dataset contains all variables involved in data analysis in the publication Rotbarth et al 2024, "Boreal forests are heading for an open state", PNAS. </p> <p>The meta data file contains explanations for each variable in the main dataset. The second dataset includes mean annual temperatures for the years 2020-2100 based in projections from the EC-Earth3-Veg-LR model. More details on both datasets are provided in the Supplementary information Table S1. </p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open (IFSopen) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side
<p>3D localization AFM (3D-LAFM) density map of glutamate transporter GltPh inward facing state open, kinetically locked, (IFSopen-1) conformation at apo condition, imaged from the cytoplasmic side , in <code>.afm</code> format and in <code>.mrc</code> format.</p> <p>Note: The <code>.afm</code> file encodes details for constructing 3D-LAFM density maps and includes experimental conditions in its header. Using <code>.afm</code> files requires the additional installation of the AFM file encoder (available from <a href="https://github.com/rafaeljiang23/3D-LAFM/tree/main/ChimeraX-AfmFormat_v2">GitHub</a>). Once the relevant installation is complete, <code>.afm</code> files can be opened in ChimeraX via drag-and-drop. In contrast, <code>.mrc</code> files, which encode only the density values equivalent to <code>.afm</code> files, can be directly opened in ChimeraX without requiring additional software installation.</p> <p>The deposited <code>.afm</code> file follows the 'AFM1' (metacode) format standard.</p>
Chinese Reference Population: open-source age-dependent computational phantoms of reference Chinese population
<p>The<strong> Chinese Reference Population (CRP)</strong> phantoms dataset encompass <strong>30 phantoms</strong> available in both voxel and NURBS formats, with age in 0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 15, 18 years and adult male and female, as well as 4 pregnant women and fetus in early pregnancy, first trimester, second trimester and third trimester.</p> <ul> <li><strong>Voxelized phantoms</strong> are accessible in NII format :<strong> <em>"XXX.nii", which could be opened in AMIDE software.</em></strong></li> <li>Excel file<strong> </strong>containing<strong> organ masses and other descriptive information </strong>:<strong> <em>"CRP_descriptive_Info.xlsx"</em></strong></li> <li>In the application of F18−FDG dose calculation, <strong>organ absorbed doses per unit activity administered </strong>is provided in an Excel file :<strong> <em>"Application_F18-FDG.xlsx"</em></strong></li> </ul> <p>All data are stored on Zenodo and can be publicly accessed.</p>
Mnemosine Digital Library. Open Science Laboratory
<p>Mnemosine Digital Library functional architecture. </p>
ISPRA Linked Open Dataset part of the WHOW-KG
<p>This linked open dataset contains Italian data for:</p> <ul> <li>soil consumption (soilc);</li> <li>spread in water of Ostreopsis ovata (ostreopsis);</li> <li>environmental protection interventions (dissesto);</li> <li>Italian national tide gauge network (rmn);</li> <li>Italian National Wave Network (ron);</li> <li>bathing waters (bathw).</li> </ul>
GloHydroRes - a global dataset combining open-source hydropower plant and reservoir data
<div> <div> <div> <div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <p>Analyzing the impacts of drought and climate change on hydropower requires detailed data not only on hydropower attributes such as plant type, head, and installed capacity, but also on reservoir characteristics like area, depth, and volume. Current open-source hydropower datasets typically lack information on reservoirs, while reservoir datasets often omit hydropower details. GloHydroRes is a global dataset that integrates open-source hydropower and reservoir data, offering 29 attributes, including key information such as installed capacity, plant type, dam height, reservoir depth, area, volume, and river name. Overall, GloHydroRes provides data on 7,775 hydropower plants across 128 countries.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div> </div> </div>
PRKN gene Introns in families from Order Primates show common Open Reading Frames
<p>Supplementary material:</p> <p><strong>01 Supplementary material. </strong>Contains Table 1, named: “<u>PRKN</u><em> gene determined in Primates in Gene database NCBI. Supplementary material</em>”. Data are shown for the primate species identified for this study. In addition, a Folder with title “<em>PRKN</em> database”, that contains the subfolders of the 34 primate species for which the <em>PRKN</em> gene was reported until April 2023. Species used for this study are indicated with the title “0 Selected-species”, and those not used only with the species name. In turn, all have four subfolders:</p> <ul> <li><strong>01 <em>PRKN</em> gene and mRNA variants-Fasta format</strong>: contains the sequences of the mRNA variants in FASTA format and of the <em>PRKN</em> gene.</li> <li><strong>02 Alignment <em>PRKN</em> gene and mRNA variants</strong>: contains the <em>PRKN</em> gene and mRNA sequences in FASTA format and the result of the alignment between the gene and the variants. Additionally, a Word document with the manual verification of the previous result. This document is not included in the primate species folders that were not used in this study.</li> <li><strong>03 <em>PRKN</em> aminoacid variants-Fasta format: </strong>contains the amino acid sequences of the <em>PRKN </em>gene variants in FASTA format.</li> <li><strong>04 PRKN Protein variants domains</strong>: contains documents with the graphical representation of the PRKN isoform domains, in Scalable Vector Graphic (SVG) format.</li> </ul> <p><strong>02 Supplementary material.</strong> Table 2 in Excel document containing the positions of the introns and exons of <em>PRKN </em>gene the 16 primate species used in this study. The first sheet contains the size of the introns/exons and their location by species. In the following sheets, the species name, intron/exon position and size are indicated.</p> <p><strong>03 Supplementary material. </strong>Contains two files:</p> <ul> <li><strong>Figure 1.</strong> Document with the alignments of the Primate PRKN proteins encoded by each mRNA variant. Also, are indicated the amino acids whose codons contain the intron with their respective phase.</li> <li><strong>Table 3, named “<em>Intron Phase for </em>PRKN<em> gene</em>”</strong>, in Excel document containing the phases, codons, their respective amino acids and the location of the introns of the primate <em>PRKN</em> gene. The first sheet contains the consensus data for each species, grouped according to families. In the following sheets, the species name, the Phases, the codon and the respective amino acid for each intron are indicated. Phases 0, 1, and 2 are indicated in purple, red, and green letters, respectively. To Phases 1 and 2, the position of the intron in the codon is indicated with a vertical bar, whereas, for Phase 0, each codon is in separate boxes. The mRNA variants that showed loss or fusion of introns are indicated as “Non determine”. Variants with alternatively spliced mRNA and phase change are indicated in yellow boxes.</li> </ul> <p><strong>04 Supplementary material. </strong>Contains two Excel documents and a subfolder with the putative <em>PRKN</em> introns Open Reading Frame (ORF):</p> <ul> <li><strong>Excel file: Table 4, named “<em>Positive ORFs</em>”</strong>. The first sheet contains the general data of 5’-3’ and 3’-5’ ORFs, characterized for each species by intron and organized by families. The second sheet corresponds to the total number of exons determined for each ORF, with the previously mentioned organization. The additional sheets are divided by intron and the species are organized by families along with the different characterized ORFs.</li> <li><strong>Excel file: Table 5 named “<em>ORFs Introns</em>”</strong>. In the first sheet, the number of ORFs of the introns is indicated, according to the grouping performed in this study. The other Sheets correspond to both unique and common ORFs determined for each species per intron, organized by family.</li> <li><strong>Subfolder named “<em>ORFs alignments</em>”</strong>: Contains the alignments between the ORFs of each intron by species. Exclusive and common ORFs are also indicated.</li> </ul> <p><strong>05 Supplementary material.</strong> Contains two Excel documents with putative functional sites of PRKN introns ORFs protein:</p> <ul> <li><strong>Excel file: Table 6 named “<em>Family Common Functional Sites</em>”</strong>. The first sheet contains the general information of the functional sites characterized in the ORFs, determined in the introns of each species ordered by family. Also, the total count of ORFs per family. The other sheets correspond to the categories of cellular processes. Within these are the functional site according to the species that presented it, ordered by family and the total count of each site.</li> <li><strong>Excel file: Table 7 named “<em>Protein Domain ORFs</em>”</strong>. Functional sites determined only for ORFs common to two or more species are found. The position of each functional site is indicated according to the amino acid sequence and in relation to each species.</li> </ul> <p><strong>06 Supplementary material. </strong>Contains two subfolders that comprise BLAST and Protein Data Bank search from mRNA and PRKN ORF intron proteins:</p> <ul> <li><strong>BLAST subfolder comprises a Word document and 7 plain text documents. </strong>The Word document corresponds to the sequences reported in the Transcriptome Shotgun Assembly protein (tsa_nr) and Expressed sequence tags (est) databases, for ORFs that are common between Homo sapiens and the other species, according to the intron. The plaintext-like documents correspond to BLAST analyses that yielded more than six sequences similar to the reference ORF. The documents are named according to the sequences analyzed: the ORF name and database (Protein or Nucleotide).</li> <li><strong>Protein Data Bank subfolder. </strong>Contains 6 files in .PDF format, that correspond to protein structures that showed similarities with human ORFs 32 and 11, from Introns 6 and 9, respectively. Each document contains the protein structure information indicated in the Protein Data Bank (PDB) and the amino acid alignment between the Intron ORF sequence and the PDB protein sequence. The documents are named according to the protein ID obtained in the PDB and the respective ORF.</li> </ul>
Open-SEA-Rice-10: Open Access High-Resolution Maps of Rice Harvested Area and Cropping Intensity in Southeast Asia
<h2>Cite this article</h2> <p>Ginting, F.I., Rudiyanto, R., Fatchurrachman, F., Mohd Shah, R., Che Soh, N., Goh Eng Giap, S., Fiantis, D., Setiawan, B.I., Schiller, S., Davitt, A., Minasny, B. High-resolution maps of rice cropping intensity across Southeast Asia. Scientific Data [12, 1408] (2025). <a href="https://www.nature.com/articles/s41597-025-05722-1">https://doi.org/10.1038/s41597-025-05722-1</a></p> <p> </p> <h2>Data Description</h2> <p>The datasets are high-resolution mapping of rice cropping intensity across Southeast Asia using the integration of Sentinel-1 and Sentinel-2 data</p> <ul> <li>The data file is in “.tif" format</li> <li>Spatial extent: Southeast Asia</li> <li>Pixel size: 10 m</li> <li>Projection information: EPSG: 4326</li> <li>CropType: Paddy rice.</li> <li>Year: Values from 2021</li> <li>Raster class:<br>-1 is single rice cropping area; <br>-2 is double rice cropping area and<br>-3 is triple rice cropping area</li> <li>The data also can be viewed on the GEE App (<a href="https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10">https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10</a>) and the Climate TRACE platform (<a href="https://climatetrace.org/">https://climatetrace.org/</a>) </li> <li>Correspondence to: Rudiyanto (rudiyanto@umt.edu.my) and Budiman Minasny (budiman.minasny@sydney.edu.au)</li> </ul> <p> </p> <p> </p>
E2EGit: A Dataset of End-to-End Web Tests in Open Source Projects
<p><strong>ABSTRACT </strong><br>End-to-end (E2E) testing is a software validation approach that simulates realistic user scenarios throughout the entire workflow of an application. In the context of web<br>applications, E2E testing involves two activities: Graphic User Interface (GUI) testing, which simulates user interactions with the web app’s GUI through web browsers, and performance testing, which evaluates system workload handling. Despite its recognized importance in delivering high-quality web applications, the availability of large-scale datasets featuring real-world E2E web tests remains limited, hindering research in the field.<br>To address this gap, we present E2EGit, a comprehensive dataset of non-trivial open-source web projects collected on GitHub that adopt E2E testing. By analyzing over 5,000 web repositories across popular programming languages (JAVA, JAVASCRIPT, TYPESCRIPT, and PYTHON), we identified 472 repositories implementing 43,670 automated Web GUI tests with popular browser automation frameworks (SELENIUM, PLAYWRIGHT, CYPRESS, PUPPETEER), and 84 repositories that featured 271 automated performance tests implemented leveraging the most popular open-source tools (JMETER, LOCUST). Among these, 13 repositories implemented both types of testing for a total of 786 Web GUI tests and 61 performance tests.</p> <p><br><strong>DATASET DESCRIPTION </strong><br>The dataset is provided as an SQLite database, whose structure is illustrated in Figure 3 (in the paper), which consists of five tables, each serving a specific purpose.<br>The <em>repository </em>table contains information on 1.5 million repositories collected using the SEART tool on May 4. It includes 34 fields detailing repository characteristics. The<br><em>non_trivial_repository</em> table is a subset of the previous one, listing repositories that passed the two filtering stages described in the pipeline. For each repository, it specifies whether it is a web repository using JAVA, JAVASCRIPT, TYPESCRIPT, or PYTHON frameworks. A repository may use multiple frameworks, with corresponding fields (e.g., is web java) set to true, and the field web dependencies listing the detected web frameworks. For Web GUI testing, the dataset includes two additional tables; <em>gui_testing_test _details</em>, where each row represents a test file, providing the file path, the browser automation framework used, the test engine employed, and the number of tests implemented in the file. <em>gui_testing_repo_details</em>, aggregating data from the previous table at the repository level. Each of the 472 repositories has a row summarizing<br>the number of test files using frameworks like SELENIUM or PLAYWRIGHT, test engines like JUNIT, and the total number of tests identified. For performance testing, the <em>performance_testing_test_details</em> table contains 410 rows, one for each test identified. Each row includes the file path, whether the test uses JMETER or LOCUST, and extracted details such as the number of thread groups, concurrent users, and requests. Notably, some fields may be absent—for instance, if external files (e.g., CSVs defining workloads) were unavailable, or in the case of Locust tests, where parameters like duration and concurrent users are specified via the command line.</p> <p><strong>To cite this article refer to this citation:</strong></p> <p>@inproceedings{di2025e2egit,<br> title={E2EGit: A Dataset of End-to-End Web Tests in Open Source Projects},<br> author={Di Meglio, Sergio and Starace, Luigi Libero Lucio and Pontillo, Valeria and Opdebeeck, Ruben and De Roover, Coen and Di Martino, Sergio},<br> booktitle={2025 IEEE/ACM 22nd International Conference on Mining Software Repositories (MSR)},<br> pages={10--15},<br> year={2025},<br> organization={IEEE/ACM}<br>}<br><br>This work has been partially supported by the Italian PNRR MUR project PE0000013-FAIR. </p>
Opening education in the MENA region: In-depth interviews and Focus Group data with experts in open education in Egypt, Jordan, Lebanon, Morocco and Palestine.
<p>The two data sets presented here are part of a study which aimed at identifying cultural barriers in the adoption of open education in higher education in the Middle East, specifically in Egypt, Jordan, Lebanon, Morocco and Palestine. This study was conducted in the context of OpenMed – “Opening up Education in South-Mediterranean countries” (<a href="https://openmedproject.eu/">https://openmedproject.eu/</a>), an international cooperation project co-funded by the <a href="http://eacea.ec.europa.eu/erasmus-plus/actions/key-action-2-cooperation-for-innovation-and-exchange-good-practices/capacity-0_en">Erasmus+ Capacity Building in Higher Education programme</a> of the European Union during the period 15 October 2015 – 14 October 2018.</p> <p>The first document presents the transcripts of in-depth interviews (N=7) conducted in February 2018 through Skype with open education experts, from seven universities: namely: Alexandria University (Egypt), Cairo University (Egypt), German Jordanian University (Jordan), Princess Sumaya University of Technology (Jordan), Notre Dame University (Lebanon), Birzeit University (Palestine) and An-Najah University (Palestine). The second document is a transcript of a focus group which took place in Agadir (Morocco) in May 2018, with open education experts from eight universities: Alexandria University (Egypt), Cadi Ayyad University (Morocco), Cairo University (Egypt), German Jordanian University (Jordan), Ibn Zohr University (Morocco), Princess Sumaya University of Technology (Jordan), and An-Najah University (Palestine). Both the interviews and the focus group were conducted in English, digitally recorded and transcribed verbatim.</p> <p>For a rationale on the cultural factors impacting adoption of open educational practices see:</p> <ul> <li>Maya-Jariego, I. (2017). Localising Open Educational Resources and Massive Open Online Courses. In F. Nascimbeni, D. Burgos, A. Vetrò, E. Bassi, D. Villar-Onrubia, K. Winpenny, I. Maya Jariego, O. Mimi, R. Qasim, & C. Stefanelli (Eds.), <em>Open Education: fundamentals and approaches. A learning journey opening up teaching in higher education</em>. Erasmus+ Programme of the European Union.</li> </ul> <p>Project n.: 561651-EPP-1-2015-1-IT-EPPKA2-CBHE-JP</p>
Evaluation of different Open Science projects based on Vienna principles.
<p>Evaluation of different Open Science projects based on Vienna principles.</p>
Interview with Peter Suber: Unlocking Knowledge for Social Equity (for International Open Access Week 2021)
<p>Interview with Peter Suber – Director of the Harvard Office for Scholarly Communication, Director of the Harvard Open Access Project, Senior Researcher at the Berkman Center for Internet & Society, and Professor of Philosophy Emeritus at Earlham College.</p> <p>In the view of many, Peter Suber has been a generous leader and guide not only among, but also to the brightest lights, clearest thinkers and most effective of collaborative practitioners in the Open Access to scholarly research movement - for at least the last two decades. In this wide ranging and timely interview for International Open Access Week 2021, Peter gives his expert, fact-based opinion on the origin, location and potential destination of various core elements of the Open Access movement and the scholarly communication system which it is so clearly shaping - now at speed.</p> <p>Interview topics covered include but are not limited to:</p> <ul> <li>A brief history of Open Access - Peter Suber’s Open Access story</li> <li>Open Access and its language defined</li> <li>Open Access highlights of the last "5,10 or 15" years: <ul> <li>Open Infrastructure <ul> <li>The Next Generation Repositories (NGR) project by the Coalition of Open Access Repositories (COAR) - over 4,000 institutional repositories in place to accelerate repository-based (Green) Open Access</li> </ul> </li> <li>Plan S and other big funders moves toward Open Access</li> <li>Transformative Agreements</li> <li>International responses to the Journal Impact Factor (JIF) <ul> <li>San Francisco DORA statement</li> <li>Leiden Manifesto</li> </ul> </li> <li>Controlled Digital Lending</li> </ul> </li> <li>Corporate capture of proprietary infrastructure and its scholarly content</li> <li>Structural equity – challenges and green shoots</li> <li>Open peer review</li> <li>The "perfect" Australian Open Access policy</li> </ul>
Numerical simulation of groundwater flow in backfilled open-pits for BDZ deviation performance
<p>This dataset contains simulated groundwater flow entering multiple backfilled open-pits with mine wastes (flowrate results) and some Python script to visualize the data.<br> The objective was to predict the blast damage zone (BDZ) created by mine excavation on the flowrate through the backfilled wastes.<br> Flowrates were obtained by 3D numerical simulation using PFLOTRAN finite volume code (www.pflotran.org).<br> Conceptual model of the BDZ and equivalent permeability was derived from Rousseau and Pabst (in press) and Mourzencko et al. (2012) (see https://hal.archives-ouvertes.fr/hal-01196684/file/mourzenko2012.pdf).<br> Number of cases simulated: 40500, with 40496 converged numerical simulations.<br> Calculated were performed using the High Performance Computing Ressource of Calcul Quebec (www.calculquebec.ca/) and Compute Canada (www.computecanada.ca/)</p>
Illustrations of the Game "Open Up Your Research"
<p>These illustrations were used to create the OS Game "Open Up Your Research" available from the University of Zurich:</p> <p>The Game has been developed by the Main Library, the Center for Reproducible Science and the Open Science Office in collaboration with the Multimedia & E-Learning-Services (MELS) at UZH.</p>
Current and future sources of open citations
<p>Video recording of the presentation done in the context of the Austrian DataCite Consortium on 19 November 2021. The video finishes after the last question.</p>
FIGURE 3 in Evaluation of three pesticides against phytophagous mites and their impact on phytoseiid predators in an eggplant open-field
FIGURE 3: General mean abundance per eggplant leaf (± SE) of Tetranychus urticae (a), Phytoseilus persimilis (b), Polyphagotarsonemus latus (c) and other phytoseiid species (d) in control, fenbutatin oxide (F.O.), acetamiprid (aceta.) and deltamethrin (delta.) treatments.
FIGURE 4 in Evaluation of three pesticides against phytophagous mites and their impact on phytoseiid predators in an eggplant open-field
FIGURE 4: Mean abundance per eggplant leaf (± SE) of Tetranychus urticae (continuous line) and Phytoseiulus persimilis (dotted line) observed during experiments in control (a) treated with water (gray arrows), fenbutatin oxide (b), acetamiprid (c) and deltamethrin (d) (black arrows).
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Matching articles with categories)
<p>This document includes which primary study falls into which category with respect to the RQs in the following study: “Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review”</p>
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Matching articles with categories)
<p>This document includes which primary study falls into which category with respect to the RQs in the following study: “Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review”</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.