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 “OpenITI”
D4.1. YouCount open data sample from the evaluation – current stand
<p>The H2020 YouCount project runs from February 2021 to January 2024. The overarching objectives are to generate new knowledge and innovations to increase the social inclusion of youth through co-creative youth citizen social science (Y-CSS) and to provide evidence of the actual outcomes of Y-CSS. Multiple case studies—consisting of 10 co-creative Y-CSS projects with young citizen scientists (YCS) aged between about 13-29 years old across nine countries in Europe—will provide knowledge about the positive drivers of social inclusion in general. The cases will further produce knowledge as well as innovations in relation to social participation, social belonging, and citizenship.</p> <p>The YouCount evaluation design for process and outcome evaluation of Y-CSS is a multi-method approach that spans across the whole duration of the project and is carried out by the WP4 of UNIVIE. It therefore is to be classified as current work in progress, as some methods only just have been implemented and will be analyzed in the future, to estimated cross-case comparisons. The deliverable aims at making the research design, as well as the current stand of the evaluative studies, transparent and publicly available. This happens in the spirit of open science, with the goal of doing “Science for and with Society”. Hereby outlined is the theoretical design, the way of carrying it out, and the current stand of each study implementation in the overall project.</p> <p>Moreover, the D4.1 includes open data regarding the outcome methodology (pre-survey questionnaire) and a sav.file with a sample of open data collected from the current pre-survey data. See more details in the report. The attached sav.-file can provide knowledge about the used variables, to estimate occurring answering patterns very roughly, ad to familiarize with the implementation of such a pre-post-survey. However, it is to be noted that this data set is exemplary, anonymized and potentially also not complete yet and must be handled and used accordingly. Due to the relatively low number of participants (yet), this research is to be characterized as early stage research and only depicts a moment in time. This being said, the YouCount project is designed to gather a huge quantity of data that promises a variety of concrete research outputs, so future data samples will be richer for in-depth analyses. At this point, more quantitative as well as qualitative data is needed to estimate real impacts of Y-CSS in all its facettes.</p>
D2.2 Open data concerning social inclusion provided on the project homepage - Emerging findings
<p>Authors to the case posters and contributors from consortium partners are described in the deliverable. </p> <p>The H2020 YouCount project runs from February 2021 to January 2024 and the consortium consists of 11 partners from nine European countries. Multiple case studies—consisting of 10 co-creative Y-CSS projects with young citizen scientists (YCS) aged between about 13-29 years old across nine countries in Europe—will provide knowledge about the positive drivers of social inclusion in general. The cases will further produce knowledge as well as innovations in relation to social participation, social belonging, and citizenship.</p> <p>In line with YouCount’s commitment to Open Science and Data Management based on the FAIR Principles, D2.2 provides a sample of open data concerning social inclusion from the research and innovation activities during the implementation period. The open data is based on informed consent and includes the following files included in the report:</p> <p>1. File 1 Homepage 30-06-22, Case descriptions.</p> <p>2. File 2 Case posters 08-06-2022, Experiences with inclusive co-creative Y-CSS in multiple case study. </p> <p>3. File 3 Narrative text 26-06-22, Experiences with developing the YouCount app toolkit, methodology.</p> <p>4. File 4 Quotes 25-06-22, Views and experiences with social inclusion of youths, YouCount/ECSA WG EIE webinars 2021 and YouCount newsletters 2022.</p> <p>5. File 5 Links to YouCount app toolkit, 28-06-22, Youths’ views and experiences with social inclusion opportunities, observations.</p> <p>Notably, the open data are based on a co-creative and flexible research design and comes in an early phase of the case studies. They can thus only be used as emerging data and preliminary findings. Still, the data contain valuable information of the research experiences and voices from young people found in the early phase of conducting hands on co-creative Y-CSS. More systematic open social inclusion data will be provided later in the project. </p> <p>The open data can also be found at the project website <a href="https://www.youcountproject.eu/">Home - YouCount - Social Citizen Science (youcountproject.eu)</a>.</p> <p>Note! They are shared under CC-BY (text) and CC-BY-ND (images case posters) due to confidentiality issues.</p>
Open Science and Authorship of Supplementary Material for the MES research community
<p>This spreadsheet contains the data and the results from the analysis described in the paper "Open Science and Authorship of Supplementary Material. Evidence from a Research Community." being accepted at STI 2022.</p>
Open Satellite Video Single Target Tracking Datasets (OpenSatSTTD)
<p>We collect the latest open-source datasets for satellite video single target tracking (SatSTT) and launch the OpenSatSTTD project to promote the sharing of the latest research datasets in the SatSTT field. Satellite videos in the OpenSatSTTD project are collected from different sensors and platforms, and four targets (i.e., vehicles, trains, airplanes and vessels) are annotated by oriented bounding boxes. Users can obtain all satellite videos in the OpenSatSTTD project from links in the files.</p> <p>Source:</p> <p>Zheng, Ying., Zhu, Q., Luo, J., Li, Z., Lin, Z., Huang, X., and Zhang L.: Single Target Tracking in High-Resolution Satellite Videos: A Comprehensive Review (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6780820, 2022.</p> <p> </p>
Open Science as a nest that welcomes, as caring; and with openness to the world
<p>New infographic proposal on Open Science after those of the umbrella and the mushroom; care and welcome are added</p>
Dataset of Open-Source Software Developers Labeled by their Experience Level and Associated with their Software Metrics
<p>This dataset contains 703 anonymized developers extracted from 17 open-source projects from GitHub. Projects were chosen because they use:</p> <ul> <li>the Java programming language</li> <li>the <a href="https://spring.io/projects/spring-framework">Spring framework</a></li> <li><a href="https://maven.apache.org/">Maven</a> / <a href="https://gradle.org/">Gradle</a> build tools</li> </ul> <p>For all these developers, 23 software metrics were calculated for each project to which they contribute. These metrics are either calculated by analyzing the source code or relative to project management metadata. Each of these developers then have been manually annotated. To do this, developers have been searched for in professionnal social media such as:</p> <ul> <li><a href="https://www.linkedin.com/">Linkedin</a></li> <li><a href="https://twitter.com/">Twitter</a></li> <li><a href="https://github.com/">Github</a></li> </ul> <p><strong>This dataset is published in the following journal article: </strong></p> <p><strong>Dataset of Open-Source Software Developers Labeled by their Experience Level in the Project and their Associated Software Metrics, Q. Perez, C. Urtado and </strong><strong>S. Vauttier, Data In Brief, </strong></p> <p><a href="https://www.sciencedirect.com/science/article/pii/S2352340922010459">https://www.sciencedirect.com/science/article/pii/S2352340922010459</a></p>
Projects and Organizations in Open Sustainable Technology
<p><strong>A curated database of open technology projects and organisations working for a stable climate, energy supply and natural resources. The dataset was created by the <a href="https://opensustain.tech/">Open Sustainable Technology Initiative</a></strong></p>
Monitoring open access publishing of NWO funded research (2015-2021) data set
<p>This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (<a href="https://doi.org/10.5281/zenodo.7041897">https://doi.org/10.5281/zenodo.7041897</a>)</p> <p>The report presents statistics on the extent to which publications from the period 2015–2021 funded by NWO are available in Open Access. The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw. This report builds on two earlier reports, published in <a href="https://zenodo.org/record/4446042">2020</a> and <a href="https://zenodo.org/record/5056043">2021</a>, covering publications from the period 2015–2018 and 2015-2020, respectively.</p>
DOIs linked by the English Wikipedia which could be made available in green Open Access
<p>List of citations from the English Wikipedia articles extracted from the enwiki-20170720-pages-articles XML dump via https://pypi.org/project/mwcites/ , DOIs cleaned with custom regular expressions.</p> <p>The list of scholarly publications identified by the DOIs has been filtered to exclude those which are already available in Open Access and those which may not be depositable according to SHERPA/RoMEO policy summaries, first via the Dissemin API and then by the oaDOI API, with the attached Python script (https://github.com/nemobis/bots/blob/master/doi-doai-openaccess.py ).</p> <p>This produced a list of 194913 DOIs available in open access and 430230 DOIs unavailable and depositable (as of 2017-08-22 data, which for oaDOI was partly v1 and partly v2).</p>
FG-OVD: Fine-grained Open-Vocabulary Object Detection Benchmark Suite
<p>A collection of annotations for PACO images containing free-form fine-grained textual captions of objects, their parts, and their attributes. It also comprises several sets of negative captions that can be used to test and evaluate the fine-grained recognition ability of open-vocabulary models.</p>
Supplementary data to `Do science maps from open access literature capture the overall topic structure of an academic field?`
<p>The dataset contains the 8,528 academic articles records related to Sustainable Food research sourced with the query `TS=("sustainab*" NEAR/2 "food*")` .</p> <p>They are the records present in the largest component of the citation network, as specified in the manuscript. </p> <p>The dataset was sourced from OpenAlex based on the original data used in the manuscript and it is composed of the following columns:</p> <table> <tbody> <tr> <td><em><strong>Column</strong></em></td> <td><em><strong>Description</strong></em></td> </tr> <tr> <td>Id</td> <td>OpenAlex ID</td> </tr> <tr> <td>DOI</td> <td>Document Object Identifier</td> </tr> <tr> <td>display_name</td> <td>The article title</td> </tr> <tr> <td>publication_year</td> <td>The publication year of the article</td> </tr> <tr> <td>open_access</td> <td>An object with details of the open access status of the article</td> </tr> </tbody> </table> <p>We choose the `.rdata` format for easy loading in R. Use the function `load()` to add the data frame to the enviroment. </p>
Usability of Open Data Portals
<p><span>This dataset reports on the usability of Open Data Portals as reported by the European public. Knowledge of open science and data concepts is also reported. </span></p>
LLODIA (Linguistic Linked Open Data for Diachronic Analysis)
<p>LLODIA (Linguistic Linked Open Data for Diachronic Analysis) model developed within the Nexus Linguarum WG4 UC4.2.1 use case in humanities.</p>
Open-source DGGS comparison data supplement
<p>A DGGS is a type of spatial reference system that partitions the globe into many individual, evenly spaced, and well-aligned cells to encode location. We calculated normalized area and compactness of cell geometries for 5 open-source DGGS implementations - Uber H3, Google S2, RiskAware OpenEAGGR, rHEALPix by Landcare Research New Zealand, HEALPix by NASA Jet Propulsion Labs, and DGGRID by Southern Oregon University - to evaluate their suitability for a global-level statistical data cube.</p> <p>This repository contains all generated data and statistics.</p> <ul> <li>EAGGR doesn't seem to have a predefined logic of hierarchical cell resolutions for ISEA3H</li> <li>EAGGR doesn't seem to have a region filling algorithm available, neither for ISEA4T nor ISEA3H</li> <li>rHEALPix is pure Python (with Numpy/Scipy support), but cell generation/conversion is slower than the other C/C++ based implementations</li> <li>DGGRID is a commandline tool and can predominantly only be used to generate a grid and fill with sampling data, the Python API is only a wrapper</li> <li>healpy is a Python package to handle pixelated data on the sphere. It is based on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) scheme and bundles the HEALPix C++ library.</li> </ul> <p>Kmoch et. al (2022). Area and Shape Distortions in Open-Source Discrete Global Grid Systems. <strong><em>Big Earth Data</em></strong></p>
AllergyMap: An Open Source Corpus of Allergy Mention Normalizations
<p>AllergyMap is a mapping between free-text entered allergy medication to standard non-proprietary ontologies.</p>
Data for: The State of Open Infrastructure Grant Funding, 2024 State of Open Infrastructure Report
<p>The purpose of the analysis based on these data was to better understand the amount, distribution, impact, and limitations of grant funding to open infrastructures that support research and scholarship.</p> <p>The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “The state of open infrastructure grant funding.” The full report is available at https://doi.org/10.5281/zenodo.10934089.</p> <p>A readme, data dictionary, and additional metadata definition file are provided with the dataset with additional detail.</p>
Data for: Open Infrastructure Governance: Current structures, nomenclature, composition, and service trends, 2024 State of Open Infrastructure Report
<p>The purpose of the analysis based on these data was to<span> record information about community governance groups for open infrastructures, focused primarily on the individuals and institutions that serve in these groups. The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “Open infrastructure governance: Current structures, nomenclature, composition, and trends.” The full report is available at <a href="The%20data%20were%20summarized%20and%20reported%20in%20the%20&ldquo;2024%20State%20of%20Open%20Infrastructure%20Report&rdquo;%20section%20&ldquo;Open%20infrastructure%20governance:%20Current%20structures,%20nomenclature,%20composition,%20and%20trends,&rdquo;%20available%20at%20https:/doi.org/10.5281/zenodo.10934089.">https://doi.org/10.5281/zenodo.10934089</a>.</span></p> <p><span>A readme is provided with the dataset with additional detail.</span></p>
Coverage and quality of open metadata for Dutch research output - dataset
<p>Record level data underlying the figures and tables in the report:<strong><br><br>Coverage and quality of open metadata for Dutch research output - report<br></strong></p> <p><a href="https://doi.org/10.5281/zenodo.10629457" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10629457</a><br><br>The current dataset contains 3 csv files:</p> <ul> <li><em>rpo_nl_list_long_20240201.csv</em> - list of identifiers (ROR ID, OpenAlex ID, OpenAIRE ID) of Dutch research performing organisations. <p>Identifiers were collected for the following groups of Dutch RPOs (see Appendix A):</p> <ul> <li> <p>Universities, organised in Universities of the Netherlands (UNL, n=14);</p> </li> <li> <p>University Medical Centres, organised in the Dutch Federation of University Medical Centres (NFU, n=9); </p> </li> <li> <p>National research institutes under the umbrella organisation of the Foundation for Dutch Scientific Research Institutes (NWO-i, n= 9);</p> </li> <li> <p>Research institutes of the Royal Netherlands Academy of Arts and Sciences (KNAW, n=10);</p> </li> <li> <p>Universities of Applied Sciences affiliated to the Netherlands Association of Universities of Applied Sciences (Vereniging Hogescholen) (VH, n=35 of 37)<br><br></p> </li> </ul> </li> <li><em>openalex_works_20231223_rpo_nl_2022 </em>- record-level data of OpenAlex records retrieved for all Dutch RPOs in scope of the pilot (UNL/NFU, NWO-i, KNAW, VH) for publication year 2022<br><br></li> <li><em>openaire_products_20240116_rpo_nl_2022 - </em>record-level data of OpenAlex records retrieved for all Dutch RPOs in scope of the pilot (UNL/NFU, NWO-i, KNAW, VH) for publication year 2022</li> </ul>
2023 Utrecht University Open Access Monitor (peer reviewed journal articles)
<p>Results of the OA monitor of Utrecht University (UU) and University Medical Center Utrecht(UMCU) for the year 2023. It lists the open access availability of all peer reviewed journal articles registered in the CRIS (Pure) of Utrecht University and/or University Medical Center Utrecht. </p>
Dataset - DeepWealth: A Generalizable Open-Source Deep Learning Framework using Satellite Images for Well-Being Estimation
<p>This dataset encapsulates the Checkpoints obtained during the training process of the Deep Learning model, which can be used for new estimations.</p> <p>The aim of the DeepWealth package is to provide a generalizable Deep Learning framework for the use of remote sensing in poverty estimation. The combination of Deep Learning and Earth Observation data is increasingly being used to estimate socioeconomic conditions at regional and global scales. The proposed framework aligns with the Sustainable Development Goal SDG1 of ending poverty. The framework provides open-source data, code, and training models (checkpoints) for reproducibility and replicability.</p> <ul> <li>The source code can be found in <a href="https://github.com/PARSECworld/DeepWealth" target="_blank" rel="noopener">https://github.com/PARSECworld/DeepWealth</a></li> <li>The metadata from source code can be found in <a href="https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf" target="_blank" rel="noopener">https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf</a></li> <li>The paper describing the development of this framework can be found at: Ben Abbes, A., Machicao, J., Corrêa, P. L. P., Specht, A., Devillers, R., Ometto, J. P., Kondo, Y., & Mouillot, D. (2024). DeepWealth: A generalizable open-source deep learning framework using satellite images for well-being estimation. <em>SoftwareX</em>, 27, 101785. <a href="https://doi.org/10.1016/j.softx.2024.101785">https://doi.org/10.1016/j.softx.2024.101785</a> </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.