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.
3,172
datasets available to search
ShareScore release 0.9.0
Dataset results
3,172 results for “Publications”
Data, Metadata, R-codes and R data files for publication "Comparative ungulate diversity and biomass change with human use and drought: implications for community stability and protected area prioritization in African savannas" by Bartzke et al. in Ecology and Evolution
<p>These files contain data and metadata for modeling ungulate diversity and biomass in the Maasai Mara ecosystem in Kenya in the drought year of 1999 and a year with normal rainfall, 2002. The files also contain R codes and R data files.</p> <p>Metadata.pdf: Metadata for files "mc_333m.csv" and "mc_1km.csv"</p> <p>mc_333m.csv: A data file for 333-meter-by-333-meter sub-blocks.</p> <p>prepare_data.r: R code to impute missing vegetation records in 333-meter-by-333-meter subblocks and summarize the data over 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>krige_vegetation.RData: An R data file containing the imputed vegetation records.</p> <p>mc_1km.csv: A data file for 1-kilometer-by-1-kilometer blocks for analysis.</p> <p>mc_1km.r: R code for modeling ungulate diversity and biomass; mc_1km_mod.RData: An R data file for loading the ungulate diversity and biomass models.</p> <p>mc_1km.RData: An R data file containing model predictions of ungulate diversity and biomass.</p> <p>mc_1km_plots.r: R code for plotting model predictions of ungulate diversity and biomass.</p> <p>MMNR_boundary.shp: A shapefile of the Maasai Mara National Reserve boundary in Kenya and associated files. These files are used for plotting the predictions of ungulate diversity and biomass.</p> <p>MMNR_border.zip: A shapefile and associated files for the Maasai Mara National Reserve border with Tanzania. These files are also used for plotting predictions of ungulate diversity and biomass.</p>
The Great British Liverwort Hunt – collecting wild accessions for molecular biology research while engaging the public
<p>During the lockdowns of the COVID-19 pandemic, outreach activities of universities were significantly perturbed. The Sainsbury Laboratory Cambridge University (SLCU) outreach project for the Cambridge festival in 2021 was therefore a fully online project, which asked people from across the mainland of the United Kingdom to send in plant samples of <i>Marchantia polymorpha</i>, and <i>Lunularia cruciata. </i>The samples were, upon arrival, established into axenic culture for use in research. Here, we deposit the names and locational data of the lines established in axenic culture for the wider use of the research community. Out of 76 samples received, 68 were successfully established in sterile tissue culture. This dataset also includes an analysis of the sex of the <i>M. polymorpha</i> accessions.</p>
Code for ESS publication - Quantifying the uncertainty of ice-crystal-related parameters to simulated winter precipitation over the Korean Peninsula
<p>In this repository, we include the source codes used in the ESS publication "Quantifying the uncertainty of ice-crystal-related parameters to simulated winter precipitation over the Korean Peninsula"</p><p>There are WDM6 codes and parameter sets in "Model_codes" for WRF simulation, model output files in "Model_outputs", AWS datas in "AWS", Scripts for calculating statistical values in a table in "Table", and "Figures" has a scripts for the figure of the manuscript.</p><p>In "Model_codes", the text file starting with LHS is 50 parameters sets generated by the Latin hypercube sampling method, and the text files starting with the case are the set of parameters used for the SEN experiment for each case. There are two WDM6 codes, the CTL code uses the parameter set in Table 1 as CTL, and the LHS code uses the 50 parameters set in the text file.</p><p>In "Figures", there is a script corresponding to each figure of the paper.</p>
Data and Code 1/3 "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders"
<p>Data and Code "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders", published in Global Environmental Change, authored by David Wuepper (1,2)*, Thomas Crowther (3), Thomas Lauber (3), Devin Routh (3,4), Solen Le Clec'h (5), Rachael D. Garrett (6,7), Jan Börner (8,9). (1 Land Economics Group, University of Bonn, Germany, 2 Agricultural Economics and Policy Group, ETH Zurich, Switzerland, 3 Global Ecosystem Ecology Group, ETH Zurich, Switzerland, 4 SIT Group, Uiversity of Zurich, Switzerland, 5 Environmental Systems Analysis Group, Wageningen University, Netherlands, 6 Conservation and Development, Cambridge University, UK, 7 Environmental Policy Lab, ETH Zürich, Switzerland, 8 Institute for Food and Resource Economics, University of Bonn, Germany, 9 Center for Development Research, University of Bonn, Germany. </p>
Data and Code 2/3 "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders"
<p>Data and Code "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders", published in Global Environmental Change, authored by David Wuepper (1,2)*, Thomas Crowther (3), Thomas Lauber (3), Devin Routh (3,4), Solen Le Clec'h (5), Rachael D. Garrett (6,7), Jan Börner (8,9). (1 Land Economics Group, University of Bonn, Germany, 2 Agricultural Economics and Policy Group, ETH Zurich, Switzerland, 3 Global Ecosystem Ecology Group, ETH Zurich, Switzerland, 4 SIT Group, Uiversity of Zurich, Switzerland, 5 Environmental Systems Analysis Group, Wageningen University, Netherlands, 6 Conservation and Development, Cambridge University, UK, 7 Environmental Policy Lab, ETH Zürich, Switzerland, 8 Institute for Food and Resource Economics, University of Bonn, Germany, 9 Center for Development Research, University of Bonn, Germany. </p>
Data and Code 3/3 "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders"
<p>Data and Code "Public Policies and Global Forest Conservation: Empirical Evidence from National Borders", published in Global Environmental Change, authored by David Wuepper (1,2)*, Thomas Crowther (3), Thomas Lauber (3), Devin Routh (3,4), Solen Le Clec'h (5), Rachael D. Garrett (6,7), Jan Börner (8,9). (1 Land Economics Group, University of Bonn, Germany, 2 Agricultural Economics and Policy Group, ETH Zurich, Switzerland, 3 Global Ecosystem Ecology Group, ETH Zurich, Switzerland, 4 SIT Group, Uiversity of Zurich, Switzerland, 5 Environmental Systems Analysis Group, Wageningen University, Netherlands, 6 Conservation and Development, Cambridge University, UK, 7 Environmental Policy Lab, ETH Zürich, Switzerland, 8 Institute for Food and Resource Economics, University of Bonn, Germany, 9 Center for Development Research, University of Bonn, Germany. </p>
The ULS23 Challenge Public Training Dataset Part 4
<p>This dataset contains part of the imaging data for the <a href="https://uls23.grand-challenge.org/">Universal Lesion Segmentation Challenge (ULS23).</a> It contains lesion volumes-of-interest (VOI's) for part of the weakly annotated DeepLesion data. The annotations are made available through the <a href="https://github.com/MJJdG/ULS23">Challenge repository on GitHub</a>.<br><br>The Universal Lesion Segmentation 2023 (ULS23) data is licensed under CC BY-NC-SA 4.0 </p>
The ULS23 Challenge Public Training Dataset Part 3
<p>This dataset contains part of the imaging data for the <a href="https://uls23.grand-challenge.org/">Universal Lesion Segmentation Challenge (ULS23).</a> It contains lesion volumes-of-interest (VOI's) for previously released data. It consists of 76 lung lesions from the MDSC_Task06 dataset, 283 pancreas lesion from MDSC_Task07 and 133 colon lesions from MDSC_Task10, 558 abdominal lymph nodes, 379 mediastinal lymph nodes from the NIH-LN dataset. It also contains the weakly annotated CCC18 data, 1.211 lesions, and part of the DeepLesion dataset. The annotations are made available through the <a href="https://github.com/MJJdG/ULS23">Challenge repository on GitHub</a>.<br><br>The Universal Lesion Segmentation 2023 (ULS23) data is licensed under CC BY-NC-SA 4.0 </p>
Multilevel Interventions for Mental Health in SMEs and Public Workplaces - H-Work EU Project Dataset (Only for Partners)
<p>This Dataset includes data collected as part of the H-Work project, at timepoints T1-T7. It contains data from each timepoint per site in single files and merged Data from multiple sites and timepoints, for more information see info file.</p>
Anatomy of top 1% most highly-cited publications. An empirical comparison of two approaches. Dataset
<p>Supplementary material containing tables with the main pieces of data used in the publication entitled Anatomy of top 1% most highly-cited publications. An empirical comparison of two approaches. This pieces of data were downloaded from the November 2022 snapshot of OpenAlex.</p>
The ULS23 Challenge Public Training Dataset Part 6
<p>This dataset contains part of the imaging data for the <a href="https://uls23.grand-challenge.org/">Universal Lesion Segmentation Challenge (ULS23).</a> It contains lesion volumes-of-interest (VOI's) for part of the weakly annotated DeepLesion data. The annotations are made available through the <a href="https://github.com/MJJdG/ULS23">Challenge repository on GitHub</a>.<br><br>The Universal Lesion Segmentation 2023 (ULS23) data is licensed under CC BY-NC-SA 4.0 </p>
Dataset for "Public Health Benefits from Improved Identification of Severe Air Pollution Events with Geostationary Satellite Data"
<p>Dataset for "Public Health Benefits from Improved Identification of Severe Air Pollution Events with Geostationary Satellite Data" to be published in GeoHealth doi: 10.1029/2023GH000890</p>
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (2/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>2 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (3/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>3 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)</p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (1/4)
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a></p><p>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.</p><p>All zip files should unzipped into a parent folder called /data inside the Github repository above.</p><p><strong>Important:</strong> Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload <strong>1 out of 4</strong>. The other 3 uploads can be found at: </p><p>Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)</p><p>Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)</p><p>Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)</p><p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Dataset for Testing a conservation compromise: No evidence that public wolf hunting in Slovakia reduced livestock losses
<p>Dataset for analyses perfomed in the study accepted in Conservation Letters entitled Testing a conservation compromise: No evidence that public wolf hunting in Slovakia reduced livestock losses.</p>
A twofold perspective on the quality of research publications: The use of ICTs and research activity models
<p>Results of the study</p>
Benchmarking of Apps for Early Childhood: Comparing Public and Educators' Evaluation
<p>Children frequently encounter content that may not be developmentally appropriate or are exposed to commercials that incite a desire to acquire superfluous products. Therefore, it is advantageous to use safety filters on personal devices in order to curate age-appropriate content and, in all circumstances, to closely monitor children's usage of these technologies. In this particular context, it is crucial to ascertain the calibre of early childhood applications and the extent to which parents and education professionals contribute to facilitating children's understanding of the digital realm. Some ideas include looking into how useful educational apps for babies and toddlers really are and what could go wrong if they are used in the wrong way, especially when it comes to safety, privacy, and ethical concerns.</p><p>This record introduces a sample of thirty applications commonly used by families, which are available for download on the 'Google Play' store. The primary objective of the study carried out over this data was to elucidate intriguing perspectives on the notions of quality, privacy, and the correlation between children and technology. The benchmarking tool utilised in this study was developed under the framework of the DataChild Map project. Its purpose was to assess several attributes of mobile applications, including the examination of their privacy policies.</p><p>The record is composed of three main elements:</p><p>1. The instrument adopted, English and Italian Version</p><p>2: The R script used to carry out the analysis</p><p>3: The dataset</p>
FIGURE 9 in <p><strong>Nomenclatural and taxonomic problems related to</strong> <strong>the electronic publication of new nomina and nomenclatural acts in zoology, with brief comments on optical discs and on the situation in botany</strong></p>
FIGURE 9. Three documents concerning Gingerich et al. (2009) downloaded from the PLoS website on 18 December 2012: (a) first web page devoted to this work on the website; (b) first page of PDF of work, dated 4 February 2009; (c) comment 2, dated 22 May 2009, accessed through the link "Comments: 2" on top of (a).
FIGURE 10 in <p><strong>Nomenclatural and taxonomic problems related to</strong> <strong>the electronic publication of new nomina and nomenclatural acts in zoology, with brief comments on optical discs and on the situation in botany</strong></p>
FIGURE 10. Three documents concerning Franzen et al. (2009) downloaded from the PLoS website on 18 December 2012: (a) first web page devoted to this work on the website; (b) comment 7, dated 21 May 2009, accessed through the link "Comments: 13" on top of (a); (c) "formal correction", dated 21 July 2009, accessed through the link "Formal correction: 1" on top of (a).
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.