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.
13,499
datasets available to search
ShareScore release 0.9.0
Dataset results
13,499 results for “researcher”
The Research Life Cycle
<p>A diagram of the Research Life Cycle as used for communication by the Vrije Universiteit (VU) Amsterdam.</p> <p>This diagram was partly inspired by a diagram by <a href="https://www.jisc.ac.uk/guides/research-data-management">JISC and Bonner McHardy</a> that was released under a <a href="http://creativecommons.org/licenses/by-nc-nd/3.0">CC BY-NC-ND</a> licence.</p>
About ERIGrid 2.0 - Connecting European Smart Grid Research Infrastructures
<p>This video provides a brief overview of the activities and services of the <a href="https://ec.europa.eu/programmes/horizon2020/en">H2020</a> <a href="https://erigrid2.eu/">ERIGrid 2.0</a> research infrastructure project.</p>
Questionnaire data to research small-scale farmers' information sharing for adapting to climate change in Mozambique (2019-2020)
<p>Data collected from individual questionnaires with local communities of 4 districts of Mozambique in November 2019 and July 2020. It contains as well data from nine individual questionnaires to institutions (government and NGOs) working with local communities for their development.</p> <p>Data are replies from interviews containing open and closed questions about a) climate change adaptation options necessary for Mozambican small scale farmers, about b) the most used and preferred information sources of farmers, about c) the main barriers for a better exchange of information, and about d) proposals for improving it. The questionnaire can be consulted in Appendix A (in English and Portuguese). The open questions had the purpose to understand the causes and explanations about the themes presented. The closed questions followed a 0-5 likert scale approach, where 5 meant a very important factor and 0 non important one. This format was pursued for developing statistical analysis and comparison between the different types of participants. We used the same questions and format for interviewing farmers and stakeholders, although the questionnaire for farmers included also personal aspects like gender, age, and education.</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Continuous)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
The Hearpiece database of individual transfer functions of an openly available in-the-ear earpiece for hearing device research
<p>We present a database of acoustic transfer functions of the Hearpiece, an openly available multi-microphone multi-driver in-the-ear earpiece for hearing device research. The database includes HRTFs for 87 incidence directions as well as responses of the drivers, all measured at the four microphones of the Hearpiece as well as the eardrum in the occluded and open ear. The transfer functions were measured in both ears of 25 human subjects and a KEMAR with anthropometric ears for five reinsertions of the device. We describe the measurements of the database and analyse derived acoustic parameters of the device. All regarded transfer functions are subject to differences between subjects as well as variations due to reinsertion into the same ear. Also, the results show that KEMAR measurements represent a median human ear well for all assessed transfer functions. The database is a rich basis for development, evaluation and robustness analysis of multiple hearing device algorithms and applications.</p>
Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"
<p>Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "<a href="https://chemrxiv.org/articles/preprint/A_Revised_Pseudo-Second_Order_Kinetic_Model_for_Adsorption_Sensitive_to_Changes_in_Sorbate_and_Sorbent_Concentrations/12008799">A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations</a>".</p>
Research Data Life cycle
<p>Research Life cycle Headings & Key Points.</p> <p>1- Planning:</p> <ul> <li>Data management planning (DMPs)</li> <li>Data description and metadata extraction</li> <li>Data documentation</li> <li>Choice of repositories</li> <li>Choices of file formats</li> <li>Data re-use</li> <li>Funders requirements</li> <li>File naming</li> <li>Ethics and Research conduct</li> <li>Funding for RDM activities</li> </ul> <p>2- Managing:</p> <ul> <li>Storage and backup & security</li> <li>Active Metadata collection</li> <li>Tools and software solutions</li> <li>Curation</li> <li>Versioning</li> <li>Provenance</li> </ul> <p>3- Sharing</p> <ul> <li>Data access and Sharing rights</li> <li>Data privacy and GDPR compliance</li> <li>Data ownership, licensing</li> <li>Data Transfer</li> <li>GDPR</li> </ul> <p>4- Preservation and Publication</p> <ul> <li>Citation</li> <li>PrePrint</li> <li>DOI</li> <li>Publishing requirements</li> <li>Long Term Storage</li> <li>Archival and Disposal policies</li> </ul>
Research Data of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland
<p>The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.</p> <p>This 2014 Census data set represents the data collected in three different ways:</p> <ul> <li>qualitative analysis of the open access repository websites</li> <li>automatic validation of the metadata via OAI-PMH using the DINI-Validator [2] </li> <li>online survey of repository operators</li> </ul> <p>As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.</p> <p> </p> <p>[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. <em>D-Lib Magazine</em>, 19. http://dx.doi.org/10.1045/november2013-vierkant </p> <p>[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml</p> <p>[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. fig<strong>share</strong>. <br /> http://dx.doi.org/10.6084/m9.figshare.677099</p>
Global scientific research commons under the Nagoya Protocol: Towards a collaborative economy model for the sharing of basic research assets
<p>This paper aims to get a better understanding of the motivational and transaction cost features of<br /> building global scientific research commons, with a view to contributing to the debate on the design of<br /> appropriate policy measures under the recently adopted Nagoya Protocol. For this purpose, the paper<br /> analyses the results of a world-wide survey of managers and users of microbial culture collections, which<br /> focused on the role of social and internalized motivations, organizational networks and external<br /> incentives in promoting the public availability of upstream research assets. Overall, the study confirms<br /> the hypotheses of the social production model of information and shareable goods, but it also shows the<br /> need to complete this model. For the sharing of materials, the underlying collaborative economy in<br /> excess capacity plays a key role in addition to the social production, while for data, competitive pressures<br /> amongst scientists tend to play a bigger role.</p>
Dataset with the results of the e-infrastructures Austria National Survey about Research Data
<p>This is the dataset accompanying the report with the results of our national survey regarding the management of research data</p>
Jisc Research Data Shared Service metadata focus group use cases
<p>Dataset of use cases collected between July and October 2016 during a series of metadata focus groups conducted with a number of the <strong>Research Data Shared Service</strong> pilots who volunteered for the process.</p> <p>This dataset is available in two formats (including an open format) with the same content: 180 use cases in the following user story structure: </p> <ul> <li><strong>As a</strong></li> <li><strong>Theme</strong></li> <li><strong>I want </strong></li> <li><strong>So that</strong></li> <li><strong>Comments</strong></li> </ul> <p>The .xlsx file contains additional formatting grouping the use cases by theme, role, data and community.</p>
The Landscape of Research Data Repositories in 2015. A re3data Analysis
<p>The attached data sets provides an overview of the landscape of research data repositories in 2015. They are based on an analysis of the re3data - registry of research data repositories from December 2015.</p>
Dataset supplementing the article Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129.
<p>This dataset supplements the publication<br> Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129. doi: 10.1016/j.visres.2017.02.001</p> <p>Use is free for scientific purposes, provided the aforementioned reference is appropriately cited.<br> Description of files<br> - conditionsByObserver.csv<br> contains for each of the 16 observers the color and grating direction that had been coupled to either the low-pitch or the high-pitch tone<br> column 1: observer number<br> column 2: color associated with low-pitch tone<br> column 3: color associated with high-pitch tone<br> column 4: drift direction associated with low-pitch tone<br> column 5: drift direction associated with high-pitch tone</p> <p>- conditionsByObserver.mat contains the same information as matlab variables (as four vectors/cell arrays with one entry per observer)</p> <p>- toneByBlockAndTrial.csv<br> contains the conditions for all 18 rivalry trials (6 rivalry blocks with 3 trials each) for each observer<br> column 1: observer number<br> column 2: block number<br> column 3: trial number<br> column 4: tone (low [pitch], high [pitch], none) played in this trial<br> Note that due to a technical error for observer #16, block 6 was presented first, followed by 1,2,3,4,5; for all other observers blocks were used in the order given (1,2,3,4,5,6).</p> <p>- toneByBlockAndTrial.mat contains the same information as a 16x6x3 matrix named toneByBlockAndTrial ; tones are coded numerically (1-low pitch,2-high pitch,3-none)</p> <p>- eyeTraces.mat contains three cell arrays of dimensions 16x6x3 (observer x rivalry block x rivalry trial) called xEye, oknGain, and timeSinceTrialStart;</p> <p>o each entry of xEye contains the horizontal eye position for<br> the respective trial in eye-tracker coordinates (which correspond to screen pixels, except that (1/1) is the upper right rather than the upper left and values increase from right to left due to the setup configuration)</p> <p>o oknGain contains the gain computed from these eye positions.</p> <p>o timeSinceTrialStart contains the time in seconds since onset of the trial</p> <p><br> For all variables, the sampling rate is 500 Hz, in eye-tracker coordinates the speed of the grating is 240 units/ms. Blinks were removed from both eye-data variables, fast-phases were removed from the gain data. Removed data were set to NaN in eye-data variables.</p> <p>- Matlab functions figure1d.m, figure 2.m, figure3.m and figure4.m compute raw versions of the aforementioned paper's figures from the datafiles to exemplify their usage.</p> <p>[Note: In the originally published version of the article, the first two means and their standard errors of section 3.3 were stated incorrectly. All figures and statistical analyses are based on the correct data].</p>
OpenUP survey on researchers' current perceptions and practices in peer review, impact measurement and dissemination of research results
<p>OpenUP project (http://openup-h2020.eu/) conducted a survey to capture current perceptions and practices in peer review, dissemination of research results and impact measurement among European researchers. The survey was coducted between 20 January and 23 February 2017. It consisted of four sections. The first section asked a series of questions on the respondents’ scientific discipline, career stage, gender and other characteristics. The following sections asked a series of questions on peer review practices, dissemination of research results and impact measurement/use of altmetrics. The questionnaire was collaboratively prepared by the OpenUP consortium. </p> <p>The survey was implemented via surveygizmo tool (https://www.surveygizmo.com/). Invitations to participate were sent to a random sample of researchers from arXiv, Pubmed and RePEc. The OpenUP team mined researchers’ contact details from these platforms. The OpenUP project team made efforts to further boost the repondent sample for certain underrepresented areas through the DARIAH website, THESIS network, EURODOC, AIMS portal, the Parthenos community and other channels. The survey targeted researchers from the EU-28, Switzerland and Norway. The goal was to get around 1,000 responses. In total, there were 976 completed response and completion rate was 72.4%. </p> <p>The attached documents include the questionnaire and the dataset. In the dataset (cvs file) the top row contains numbered questions that correspond to the numberring in the questionnaire (word file). The data was exported as an excel file, anonymised by creating respondent IDs and IP data were deleted. The file was then converted to CSV.</p> <p> </p>
Supplementary data files for manuscript titled "From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research"
<div>This data repository contains all the necessary supplementary files for the manuscript titled "<strong>From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research.</strong>"</div> <div> </div> <div>The repository is a copy of the <a href="https://github.com/IMI-COMBINE/template2graphs">GitHub page</a> with the source code used to generate the graph and additional files required for the Lab Data Template.</div> <div> </div> <div>Below we provide a brief overview of the data files in the `additional folder` and their underlying purpose:</div> <div> <ul> <li>The <strong>Data Survey</strong> collects relevant project and data set information to set up a Data Management Plan. It can serve as an input for Lab Data Template development.</li> <li>The <strong>Lab Data Templates</strong> facilitate the collection of AMR research data (in vivo and in vitro) in several sub-tables. The Excel format is compatible with upload procedures into the data repository 'grit' and serves as input for a knowledge graph workflow.</li> <li>The <strong>Data dictionary</strong> is connected to the Lab Data Templates and ensures harmonized data entries. In addition, the dictionaries collect metadata beyond the content of the Lab Data Template (e.g. bacterial strain information or compound information) and link to ontologies where possible.</li> <li>The <strong>FAIR assessments</strong> have been used as a primer for improving the template. This report is generated using the FAIR-DSM model.</li> </ul> </div> <div>The templates have been used during the IMI2 GNA NOW project to collect information and have been improved according to FAIR standards in collaboration with the IMI FAIRplus project ("post FAIRification").</div>
National Open Access Monitor, Ireland - Research product metadata
<p>This dataset contains the foundational data for the National Open Access Monitor under an open license. OpenAIRE will routinely provide monthly data dumps to Zenodo, encompassing a comprehensive set of data and indicators related to the Open Access Monitor. This collaborative effort ensures accessibility and openness in sharing the data, promoting transparency and facilitating its use for research and analysis purposes.<br>The dataset comprises of the metadata of the research products metadata stored in the parquet format. The file contains two columns ("id", "xml"), the first of which is the OpenAIRE identifier of the research product and the the second the xml representation of the metadata. The schema of the metadata can be found in https://www.openaire.eu/schema/1.0/oaf-1.0.xsd and a detailed description of the contents can be found in https://graph.openaire.eu/docs/ and https://zenodo.org/records/2643199</p>
A 31-Year Bibliometric Review of Team Effectiveness Research: Evolution, Trends, and Future Directions (DATA).
<p>This dataset is derived from a comprehensive study of team effectiveness over three decades, utilizing bibliometric techniques to examine scholarly publications across multiple databases. The repository includes bibliometric data from 6,051 publications related to team effectiveness, featuring metadata such as authors, titles, publication years, citations, and keywords.</p> <h3>Repository Structure:</h3> <p>The data shared in this repository is organized as follows:</p> <ul> <li><strong>Bibliometrix database.xlsx:</strong> This excel file contains information on 6051 publications in the field of Team Effectiveness downloaded from the Scopus and Web of Science databases using specific search terms and inclusion criteria detailed in our associated paper (see our publication for more information on the methodology). The file contains the necessary headers for direct use in the Biblioshiny interface of the bibliometrix library for R.</li> <li><strong>List of stop words.txt:</strong> This file contains keywords, separated by commas, that we have decided to eliminate from our analyses due to their potential to introduce bias in the results.</li> <li><strong>List of synonyms.txt:</strong> This file contains groups of semantically synonymous keywords, separated by semicolons. Each line represents a group of synonyms, with the first keyword being the one that Bibliometrix will use to replace all other keywords in that line.</li> <li><strong>Appendix [A-H].pdf:</strong> List of appendices that complement the results of the study carried out.</li> </ul> <h3>Recommended Usage:</h3> <div> <div> <div> <div> <p>This dataset is ideal for researchers interested in conducting new analyses of research trends, co-authorship network analysis, and thematic evolution in the field of team effectiveness. For example, researchers can narrow the scope to focus exclusively on team effectiveness in educational contexts. Our publication provides all the necessary details to replicate or extend our methodology. For any queries related to the data, please contact <strong>Yeray Barrios-Fleitas</strong> at <em><a rel="noreferrer">y.d.c.barriosfleitas@utwente.nl</a></em>.</p> </div> </div> </div> </div> <div> </div> <h3>License and Citation:</h3> <p>The data are distributed under the CC BY license. Please cite any use of this dataset using the following format:<br>Barrios Fleitas, Y., Marcella A.M.G., Eysink, T.H., & Rensink , A. (2025). A 31-Year Bibliometric Review of Team Effectiveness Research: Evolution, Trends, and Future Directions. ZENODO, <a href="https://doi.org/10.5281/zenodo.12082529" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12082529</a></p>
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection
<p><strong>NeSy4VRD</strong></p> <p>NeSy4VRD is a multifaceted, multipurpose resource designed to foster neurosymbolic AI (NeSy) research, particularly NeSy research using Semantic Web technologies such as OWL ontologies, OWL-based knowledge graphs and OWL-based reasoning as symbolic components. The NeSy4VRD research resource pertains to the <em>computer vision</em> field of AI and, within that field, to the application tasks of <em>visual relationship detection (VRD) and scene graph generation</em>.</p> <p>Whilst the core motivation of the NeSy4VRD research resource is to foster computer vision-based NeSy research using Semantic Web technologies such as OWL ontologies and OWL-based knowledge graphs, AI researchers can readily use NeSy4VRD to either: 1) pursue computer vision-based NeSy research without involving Semantic Web technologies as symbolic components, or 2) pursue computer vision research without NeSy (i.e. pursue research that focuses purely on deep learning alone, without involving symbolic components of any kind). This is the sense in which we describe NeSy4VRD as being <em>multipurpose</em>: it can readily be used by diverse groups of computer vision-based AI researchers with diverse interests and objectives.</p> <p>The NeSy4VRD research resource in its entirety is distributed across two locations: Zenodo and GitHub.</p> <p> </p> <p><strong>NeSy4VRD on Zenodo: the NeSy4VRD dataset package</strong></p> <p>This entry on Zenodo hosts the <em>NeSy4VRD dataset package</em>, which includes the <em>NeSy4VRD dataset</em> and its companion <em>NeSy4VRD ontology</em>, an OWL ontology called VRD-World.</p> <p>The <em>NeSy4VRD dataset</em> consists of an image dataset with associated visual relationship annotations. The images of the <em>NeSy4VRD dataset</em> are the same as those that were once publicly available as part of the <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">VRD</a> dataset. The NeSy4VRD visual relationship annotations are a highly customised and quality-improved version of the original VRD visual relationship annotations. The <em>NeSy4VRD dataset</em> is designed for computer vision-based research that involves detecting objects in images and predicting relationships between ordered pairs of those objects. A visual relationship for an image of the <em>NeSy4VRD dataset</em> has the form <'subject', 'predicate', 'object'>, where the 'subject' and 'object' are two objects in the image, and the 'predicate' describes some relation between them. Both the 'subject' and 'object' objects are specified in terms of bounding boxes and object classes. For example, representative annotated visual relationships are <'person', 'ride', 'horse'>, <'hat', 'on', 'teddy bear'> and <'cat', 'under', 'pillow'>.</p> <p>Visual relationship detection is pursued as a computer vision application task in its own right, and as a building block capability for the broader application task of scene graph generation. Scene graph generation, in turn, is commonly used as a precursor to a variety of enriched, downstream visual understanding and reasoning application tasks, such as image captioning, visual question answering, image retrieval, image generation and multimedia event processing.</p> <p>The <em>NeSy4VRD ontology</em>, VRD-World, is a rich, well-aligned, companion OWL ontology engineered specifically for use with the <em>NeSy4VRD dataset.</em> It directly describes the domain of the <em>NeSy4VRD dataset</em>, as reflected in the NeSy4VRD visual relationship annotations. More specifically, all of the object classes that feature in the NeSy4VRD visual relationship annotations have corresponding classes within the VRD-World OWL class hierarchy, and all of the predicates that feature in the NeSy4VRD visual relationship annotations have corresponding properties within the VRD-World OWL object property hierarchy. The rich structure of the VRD-World class hierarchy and the rich characteristics and relationships of the VRD-World object properties together give the VRD-World OWL ontology rich inference semantics. These provide ample opportunity for OWL reasoning to be meaningfully exercised and exploited in NeSy research that uses OWL ontologies and OWL-based knowledge graphs as symbolic components. There is also ample potential for NeSy researchers to explore supplementing the OWL reasoning capabilities afforded by the VRD-World ontology with Datalog rules and reasoning.</p> <p>Use of the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset </em>is, of course, purely optional, however. Computer vision AI researchers who have no interest in NeSy, or NeSy researchers who have no interest in OWL ontologies and OWL-based knowledge graphs, can ignore the <em>NeSy4VRD ontology</em> and use the <em>NeSy4VRD dataset </em>by itself.</p> <p>All computer vision-based AI research user groups can, if they wish, also avail themselves of the other components of the NeSy4VRD research resource available on GitHub.</p> <p> </p> <p><strong>NeSy4VRD on GitHub: open source infrastructure supporting extensibility, and sample code</strong></p> <p>The NeSy4VRD research resource incorporates additional components that are companions to the <em>NeSy4VRD dataset package</em> here on Zenodo. These companion components are available at <a href="https://github.com/djherron/NeSy4VRD/">NeSy4VRD on GitHub</a>. These companion components consist of:</p> <ul> <li>comprehensive open source Python-based infrastructure supporting the extensibility of the NeSy4VRD visual relationship annotations (and, thereby, the extensibility of the <em>NeSy4VRD ontology</em>, VRD-World, as well)</li> <li>open source Python sample code showing how one can work with the NeSy4VRD visual relationship annotations in conjunction with the <em>NeSy4VRD ontology</em>, VRD-World, and RDF knowledge graphs.</li> </ul> <p>The NeSy4VRD infrastructure supporting extensibility consists of:</p> <ul> <li>open source Python code for conducting deep and comprehensive analyses of the <em>NeSy4VRD dataset</em> (the VRD images and their associated NeSy4VRD visual relationship annotations)</li> <li>an open source, custom-designed <em>NeSy4VRD protocol</em> for specifying visual relationship annotation customisation instructions declaratively, in text files</li> <li>an open source, custom-designed <em>NeSy4VRD workflow, </em>implemented using Python scripts and modules, for applying small or large volumes of customisations or extensions to the NeSy4VRD visual relationship annotations in a configurable, managed, automated and repeatable process.</li> </ul> <p>The purpose behind providing comprehensive infrastructure to support extensibility of the NeSy4VRD visual relationship annotations is to make it easy for researchers to take the <em>NeSy4VRD dataset</em> in new directions, by further enriching the annotations, or by tailoring them to introduce new or more data conditions that better suit their particular research needs and interests. The option to use the NeSy4VRD extensibility infrastructure in this way applies equally well to each of the diverse potential NeSy4VRD user groups already mentioned.</p> <p>The NeSy4VRD extensibility infrastructure, however, may be of particular interest to NeSy researchers interested in using the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset. </em>These researchers can of course tailor the VRD-World ontology if they wish without needing to modify or extend the NeSy4VRD visual relationship annotations in any way. But their degrees of freedom for doing so will be limited by the need to maintain alignment with the NeSy4VRD visual relationship annotations and the particular set of object classes and predicates to which they refer. If NeSy researchers want full freedom to tailor the VRD-World ontology, they may well need to tailor the NeSy4VRD visual relationship annotations first, in order that alignment be maintained.</p> <p>To illustrate our point, and to illustrate our vision of how the NeSy4VRD extensibility infrastructure can be used, let us consider a simple example. It is common in computer vision to distinguish between <em>thing</em> objects (that have well-defined shapes) and <em>stuff</em> objects (that are amorphous). Suppose a researcher wishes to have a greater number of <em>stuff</em> object classes with which to work. Water is such a <em>stuff</em> object. Many VRD images contain water but it is not currently one of the annotated object classes and hence is never referenced in any visual relationship annotations. So adding a <em>Water</em> class to the class hierarchy of the VRD-World ontology would be pointless because it would never acquire any instances (because an object detector would never detect any). However, our hypothetical researcher could choose to do the following:</p> <ul> <li>use the analysis functionality of the NeSy4VRD extensibility infrastructure to find images containing water (by, say, searching for images whose visual relationships refer to object classes such as 'boat', 'surfboard', 'sand', 'umbrella', etc.);</li> <li>use free image analysis software (such as GIMP, at gimp.org) to get bounding boxes for instances of water in these images;</li> <li>use the <em>NeSy4VRD protocol</em> to specify new visual relationships for these images that refer to the new 'water' objects (e.g. <'boat', 'on', 'water'>);</li> <li>use the <em>NeSy4VRD workflow</em> to introduce the new object class 'water' and to apply the specified new visual relationships to the sets of annotations for the affected images;</li> <li>introduce class Water to the class hierarchy of the VRD-World ontology (using, say, the free Protege ontology editor);</li> <li>continue experimenting, now with the added benefit of the additional <em>stuff</em> object class 'water';</li> <li>contribute the enriched set of NeSy4VRD visual relationship annotations, and the enriched companion VRD-World ontology, to research communities.</li> </ul> <p> </p> <p><strong>Information pertaining to the VRD dataset</strong></p> <p>Information about the original VRD dataset is available <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">here</a>. </p> <p>Public availability of the VRD images (via information accessible from that location) ceased sometime in the latter part of 2021. We thank Dr. Ranjay Krishna, one of the principals associated with the VRD dataset, for granting us permission to re-establish the public availability of the VRD images as part of NeSy4VRD.</p> <p>The original VRD visual relationship annotations are still publicly available from that location. But our deep analysis of those annotations, driven by our desire to design a robust companion ontology, revealed them to be highly problematic in many ways that made credible ontology modelling infeasible. They were also found to be replete with all manner of errors. The NeSy4VRD visual relationship annotations are far superior and we recommend them over the original VRD annotations to anyone contemplating conducting research using the VRD images. The NeSy4VRD annotations also have the added benefit of the rich, well-aligned companion <em>NeSy4VRD ontology</em>, VRD-World, for those whose research requires such a companion ontology.</p> <p>Researchers wishing to use the original VRD dataset may still do so. They can access the VRD images here, from within the <em>NeSy4VRD dataset</em> on Zenodo, and access the VRD visual relationship annotations from the location in the link.</p> <p><em>A note of caution</em>: the <em>NeSy4VRD ontology</em>, VRD-World, is <em>not</em><strong> </strong>compatible with the original VRD visual relationship annotations and cannot be used in conjunction with them. The VRD-World ontology has been engineered in relation to the highly customised and quality-improved NeSy4VRD visual relationship annotations. The customisations that were applied include ones that introduced many new object classes, merged some of the existing object classes, introduced one new predicate, and changed several predicate names.</p> <p>However, researchers can, if they wish, use the NeSy4VRD extensibility infrastructure (described above) to undertake their own customisation and quality-improvement exercise with respect to the original VRD visual relationship annotations. This is precisely how the NeSy4VRD visual relationship annotations were created in the first place. The primary intended use case of NeSy4VRD's extensibility infrastructure, however, is for researchers to use the NeSy4VRD visual relationship annotations as their starting point, and to take these annotations forward with onward customisations and extensions, as illustrated in the example use case given above.</p> <p> </p> <p> </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.