Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,782
datasets available to search
ShareScore release 0.9.0
Dataset results
6,782 results for “project”
UCLM_UJA_OPEN_Pesticides Copper Data SOIL O-LIVE PROJECT 2024 RP1
<p>Database with Pesticides and Copper Data within the frame of the WP2 Diagnosis phase in SOIL O-LIVE PROJECT</p>
VariCity: Visualizing Object-Oriented Variability Implementations as Cities — Scenarios projects
<p>This archive contains the sources for the three projects used in the scenarios presented in the paper "VariCity: Visualizing Object-Oriented Variability Implementations as Cities", along with the plugin configurations.</p> <pre>- <a href="https://github.com/jfree/jfreechart/tree/v1.5.0/">JFreeChart 1.5.0</a> - <a href="https://github.com/apache/netbeans/tree/12.2/">NetBeans 12.2</a> - <a href="https://github.com/NationalSecurityAgency/ghidra/tree/Ghidra_10.0.4_build/">Ghidra 10.0.4</a> </pre> <p>The plugin configuration consists in a hidden .varicity directory, placed at the root of each project.<br> It contains a JSON being the variation points and variants identified by symfinder, as well as the VariCity configuration to display the city with the parameters given in the paper.</p>
Supplementary material 1 from: Fernandez D, Millán A, Rizzo V, Comas J, Lleopard E, Pastor J, Pallarés S, Abellán P, Spada M, Bilton DT, Ribera I (2018) The CAVEheAT project: climate change, thermal niche and conservation of subterranean biodiversity. ARPHA Conference Abstracts 1: e30105. https://doi.org/10.3897/aca.1.e30105
The CAVEheATproject: climate change, thermal niche and conservation of subterranean biodiversity
Datasets used of Aquilegia oxysepala for this project
<p>Datasets used of <em>Aquilegia oxysepala</em> for this project</p>
Machine Learning class final project datasets
<p>This is the zip file for the machine learning course final project. it has training and testing data sets.</p>
Available datasets of Aquilegia oxysepala for this project
<p>Available datasets of <em>Aquilegia oxysepala</em> for this project</p>
G0.412+0.052 continuum image & data cubes (ALMA project 2016.1.00949.S)
<p>This dataset contains data from the ALMA project 2016.1.00949.S for G0.412+0.052 as presented in Williams et al. (2022). These data include the dust continuum, and data cubes for SiO (5-4), 13CO (2-1), and three transitions of H2CO. Both primary beam- and non primary beam-corrected data are included.</p>
Available datasets of Aquilegia oxysepala for this project
<p>Available datasets of <em>Aquilegia oxysepala</em> in this project</p>
Porosity by onboard Moisture and Density (MAD) measurements in NanTroSEIZE project
<p>This datasets include depth and sample porosity by onboard Moisture and Density (MAD) measurements at IODP Sites C0001, C0002, C0004, C0006, C0009, C0012, and C0024 at the Nankai Trough. </p> <p>These datasets were originally downloaded from http://sio7.jamstec.go.jp, which is now temporary closed.</p> <p>Uploaded with permission of JAMSTEC.</p>
Human Connectome Project resting-state fMRI Connectivity Matrices (Young Adult + Aging)
<p>This database contains the connectivity matrices of the resting-state functional MRI scans that were collected in two databases of the Human Connectome Project, Young Adult and Aging. These matrices contain the functional connectivity between brain regions (here, several different brain atlases were used, leading to several different connectivity matrices for each subject). The connectivity matrices are symmetrical <em>n x n </em>matrices. Here, <em>n</em><em> </em>indicates the number of regions present in the atlas, and any number <em>n<sub>i,j </sub></em>in the matrix is generated by calculating a simple Pearson correlation coefficient between the functional time series that describe the functional activation of regions <em>i </em>and <em>j</em> throughout the resting-state functional scan. The matrices presented in this database are present as .pconn.nii files (which can be handled using software like wb_command) or as .txt file. </p> <p>A full explanation of the database and the brain atlases used here, as well as all the scripts used to generate these connectivity matrices can be found on the GitHub page of this project: <a href="https://github.com/floristijhuis/HCP-rfMRI-repository">floristijhuis/HCP-rfMRI-repository (github.com)</a>.</p>
ECFAS Pan-EU Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211), www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><strong><em>Duo, E., Montes, J., Le Gal, M., Fernández-Montblanc, T., Ciavola, P., and Armaroli, C.: Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines, Nat. Hazards Earth Syst. Sci., 25, 13–39, <a href="https://doi.org/10.5194/nhess-25-13-2025">https://doi.org/10.5194/nhess-25-13-2025</a>, 2025.</em></strong></p> <p><em>Montes, J., Duo, E., Souto, P., Gastal, V., Grigoriadis, D., Le Gal, M., Fernández-Montblanc, T., Delbour, S., Ieronymidi, E., Armaroli, C., and Ciavola, P.: Evaluating coastal flood impacts at the EU-scale: the ECFAS approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11295, <a href="https://doi.org/10.5194/egusphere-egu22-11295">https://doi.org/10.5194/egusphere-egu22-11295</a>, 2022.</em></p> <p><strong>Description of the files contained in the Dataset</strong></p> <p>The ECFAS Pan-EU Impact Catalogue collects impact layers associated to the flood scenarios contained in the ECFAS Pan-EU Flood Catalogue. To produce the Flood Catalogue, the coast was divided into geographic regions embracing similar oceanographic conditions, and subsequently into coastal sectors. The coastal sectors can be identified by its region index RXXX and its own index CSYYY. Impacts associated to the flood maps were calculated following the approach described in the technical document of the ECFAS Deliverable 5.3 Algorithms for Impact Assessment (ECFAS Impact Tool; Duo et al., 2021). The ECFAS Impact Tool was adapted to assess the affected population, the damage to buildings, roads and railways and the exposure of a variety of other assets (e.g. agriculture, points of interest, etc.) for the flood scenarios included in the ECFAS Flood Catalogue.</p> <p>The shapefile of the polygons defining the coastal sectors as defined for the catalogue implementation is included in the database.</p> <p><strong>The Pan-EU Impact Catalogue is associated with the following additional ECFAS products: </strong></p> <p><strong>- The ECFAS Pan-EU Flood Catalogue</strong></p> <p><strong>Flood Catalogue in Zenodo</strong>: Le Gal, M., Fernández Montblanc, T., Montes Pérez, J., Duo, E., Souto Ceccon, P.E., Cabrita, P., & Ciavola, P. (2022). ECFAS Pan-EU Flood Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211), https://www.ecfas.eu/ (1.2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6778807">https://doi.org/10.5281/zenodo.6778807</a></p> <p><em><strong>Flood Catalogue Reference literature</strong>: Le Gal, M., Fernández-Montblanc, T., Duo, E., Montes Perez, J., Cabrita, P., Souto Ceccon, P., Gastal, V., Ciavola, P., and Armaroli, C.: A new European coastal flood database for low–medium intensity events, Nat. Hazards Earth Syst. Sci., 23, 3585–3602, <a href="https://doi.org/10.5194/nhess-23-3585-2023">https://doi.org/10.5194/nhess-23-3585-2023</a>, 2023.</em></p> <p><strong>- The ECFAS Impact Tool</strong></p> <p><strong>Impact Tool in Zenodo</strong>: Duo, E., Montes Pérez, J., and Souto-Ceccon, P.E. (2021). ECFAS Impact Tool, D5.3 – Algorithms for impact assessment - ECFAS project (GA 101004211), <a href="http://www.ecfas.eu/">www.ecfas.eu</a>, link: <a href="https://doi.org/10.5281/zenodo.5809296">https://doi.org/10.5281/zenodo.5809296</a></p> <p> </p> <p>The Impact Catalogue is accompanied by a technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues:</p> <p>- Duo, E., Le Gal, M., Souto Ceccon, P.E., Montes Pérez, J., 2022. <a href="https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5ee287a5d&appId=PPGMS">Technical document</a> on the ECFAS Flood and Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). <a href="http://www.ecfas.eu/">www.ecfas.eu</a></p> <p> </p> <p>This ECFAS <strong>Impact Catalogue</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the Impact Catalogue are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>The <strong>technical document</strong> describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p>*The size of the uncompressed dataset is 211 GB.</p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </p>
Turn behavior JABS project
<p>JABS project with videos and pose files labeled and trained for "turn left" and "turn right" behavior.</p>
STRIDE Project I2
<p>The datasets contain three parts: 1) Inputs;2)Outputs;3) README(data dictionary and instructions)</p> <p>1. Inputs:<br> - uber_imgs.zip file: Contains screenshots collected from Uber drivers around Birmingham, AL. [will leave it to Mostafa for more details]<br> - excel_files folder: Contains annotations done by students. Each student was assigned about 60 images to annotate.<br> - instructions.pdf file: Contains instructions on how to annotate an Uber-trip's screenshot.<br> 2.Outputs:<br> - MATSim: (https://www.matsim.org/)<br> - experiments folder:<br> - experiment_no_uber folder: This experiment simulate traffic without uber driver. Only cars and public transit.<br> It contains inputs files needed to execute the experiment and "output_events.xml.gz" which contains the output events. <br> Use "network.xml" (same for all experiments) and "output_events.xml.gz" to visualize the traffic using Via. (https://simunto.com/via/)<br> - experiment_uber_200 folder: This experiment simulate traffic with 200 uber drivers.<br> - experiment_uber_400 folder: This experiment simulate traffic with 400 uber drivers.<br> - experiment_uber_800 folder: This experiment simulate traffic with 800 uber drivers.<br> - visualization folder:<br> - D3 folder: (https://d3js.org/)<br> - demo.html: a demo to run D3 visualization in broswer. It shows uber trips origin-destination from different Zip Codes.<br> - vis-uber.zip: contains the source code and instructions how to run it.<br> - Folium folder: (https://python-visualization.github.io/folium/)<br> - Trajectory_Viz.html: shows some Uber trajectories using Folium heatmap.<br> </p>
Final Project
This is my final project for CGT 116. It is a set of late Imperial Roman weapons and armor propped up against a tree stump. Source: Objaverse 1.0 / Sketchfab
HoloNeXT project datasets
<h1>Source</h1> <p>This data has been captured within the research and innovation project HoloNeXT. This project has received funding from the AI4Media open call under European Union’s Horizon 2020 research and innovation programme.</p> <p>The dataset consists of two videos recorded with a Pixel 6 handheld device:</p> <ul> <li>One video of a plant.</li> <li>One video of a dining room.</li> </ul> <div> <p> </p> <h1>Copyright</h1> </div> <p>This data has been captured by Fundació Privada Internet i Innovació Digital a Catalunya (i2CAT).</p> <p>i2CAT is a <em>non-profit research and innovation centre</em> that promotes mission-driven knowledge to solve business challenges, co-create solutions with a transformative impact, empower citizens through open and participative digital social innovation with territorial capillarity, and promote pioneering and strategic initiatives.</p> <p>i2CAT <em>aims to transfer</em> research project results to private companies in order to create social and economic impact via the out-licensing of intellectual property and the creation of spin-offs.</p> <p>Find more information of i2CAT projects and IP rights at <a href="https://i2cat.net/tech-transfer/" rel="nofollow">https://i2cat.net/tech-transfer/</a></p> <div> <p> </p> <h1>License</h1> </div> <p>This code is licensed under the terms <em>AGPLv3</em>. Information about the license can be located at <a href="https://www.gnu.org/licenses/agpl-3.0.html" rel="nofollow">link</a>.</p> <p>If you find that this license doesn't fit with your requirements regarding the use, distribution or redistribution of our code for your specific work, please, don’t hesitate to contact the intellectual property managers in i2CAT at the following address: <a href="mailto:techtransfer@i2cat.net">techtransfer@i2cat.net</a></p>
Almond yield projection from LOCA and MACA
<p>This datasets contain generated yield projections for LOCA (ssp245 and ssp585) and MACA (rcp4.5 and rcp8.5). </p> <p>yield_all_* : state-averaged yield projection for all climate models (8 for LOCA and 18 for MACA) and statistical models (1000)</p> <p>yield_across_state_* : state-averaged and statistical model-averaged yield projection for all climate models</p> <p>yield_average_model_* : state-averaged and climate model-averaged yield projection for all statistical models</p> <p>yield_all_model_* : flattened county-level yield projection for all climate and statistical models.</p> <p>suffix "_m" refers to medium innovation scenario (decelerating innovation )</p> <p>suffix "_s" refers to stopped innovation scenario (no innovation )</p>
Dataset on Characterizing Bugs and Inspecting Projects
<p>Temporary anonymous data link for paper review: "A Comprehensive Study on Deep Learning Application Bugs in Python," including bugs after manual labeling and projects after manual inspection.</p>
FIGURE 3 in The Belgrade PaleoBlitz: A pilot project to engage amateur paleontologists
FIGURE 3. Sample question from the immediate post-event questionnaire.
MGB-SA hydrological projections (CMIP6)
<p>Climate change projections on floods obtained by forcing the hydrodinamic-hydrological model MGB-SA with CMIP6 climate projections. </p>
Fig. 33 in Unrecorded species of Korean invertebrates discovered through the project of 'Discovery of Korean Indigenous Species' II
Fig. 33. Hemipenis of Callistocythere hosonosuensis Okubo, 1979. A. right valve; B. hemipenis.
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.