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.
1,267
datasets available to search
ShareScore release 0.9.0
Dataset results
1,267 results for “data paper”
Acquired data necessary to perform the control algorithm introduced in the scientific paper: "Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention" (Advances in Mechanical Engineering, 2016, vol. 8, pp. 1-13)
<p>Acquired data necessary to perform the control algorithm introduced in this paper.</p> <p>a) Figure 6: Calibration data for the three FSRs placed on the prosthetic hand and covered with silicon caps.<br> b) Figure 9: Data for the cost during the learning of two grasping tasks of an egg: bi-digital grasp and tri-digital grasp.<br> c) Figure 10 and Figure 11: Data for the experimental results with the plastic cup and with the highlighter shown in the paper.<br> </p> <p> </p>
Data set for paper "Ramparts around lakes on Titan impact winds and methane evaporation"
<p>Data and post-processing code used for the paper "Ramparts around lakes on Titan impact winds and methane evaporation", submitted to PSJ in 2024.</p> <p>Are made available:<br> - a list of the simulations (list_simulations_ramparts2D.pdf)<br> - the simulations' netCDF outputs (run-t##.nc.gz)<br> - the input files used to run the simulations (in input_files/)<br> - the post-processing python codes used to plot the figures (in post_processing_codes/)<br> - tables of latent heat flux and horizontal wind values (Tables_LH_and_Uwind.pdf)<br> - a gif of the horizontal wind in the reference run (u_wind_run-t04_speed.gif)<br> - a gif of the vertical wind in the reference run (w_wind_run-t04_speed.gif)</p>
Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"
<p>The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments</p><p>- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) </p><p>Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html</p>
FESOM2.1 model data used in the paper "Atlantic Water warming increases melt below Northeast Greenland's last floating ice tongue"
<p><span>This data set includes the minimal data necessary to reproduce the findings of Wekerle et al., in revision. Output of model simulations with the global ocean sea ice model FESOM2.1 is provided. In particular, the data set includes:</span></p> <p><span>a) long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (</span>Wekerle2024_FESOM2_ltm_REF.nc<span>)</span></p> <p><span>b) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from the reference experiment REF for the years 1970-2021 (</span>Wekerle2024_FESOM2_annual_avg_REF.nc<span>)</span></p> <p><span>c) annual means of maximum potential temperature and basal melt rate of the 79N Glacier from experiment CLIM for the years 2000-2021 (</span>Wekerle2024_FESOM2_annual_avg_CLIM.nc<span>)</span></p> <p><span>d) daily mean basal melt rates of experiments with varying subglacial discharge averaged over the years 2010-2014 (</span>Wekerle2024_FESOM2_daily_avg_EXP_subglacial_discharge.nc<span>)</span></p> <p><span>e) daily mean basal melt rates of experiments with varying drag coefficients for the year 2000 (</span>Wekerle2024_FESOM2_daily_EXP_basal_drag.nc<span>)</span></p> <p><span>Each netcdf file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas).</span></p>
Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"
<p><strong>Example Data for DeepReefMap</strong></p> <p>This dataset contains input videos in MP4 format taken with GoPro Hero 10 Cameras in Reefs in the Red Sea to demonstrate the DeepReefMap tool, which is described in the paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning" by Sauder et al.</p> <p>It contains a directory for model checkpoints for semantic segmentation, and for the 3D SLAM component:</p> <p>```<br>checkpoints/<br> segmentation_net.pth<br> sfm_net.pth<br>```</p> <p>It also contains videos to run the reconstruction with. See the detailed instructions for running reconstructions in https://github.com/josauder/mee-deepreefmap</p> <p>```<br>input_videos/<br> GX_SINGLE_VIDEO.MP4<br> GX_VIDEO_1_OF_2.MP4<br> GX_VIDEO_2_OF_2.MP4<br>```</p>
Data of paper "Global supply chains amplify economic costs of future extreme heat risk"
<p>This is the database of articles "Global supply chains amplify economic costs of future extreme heat risk". The database contains the number of deaths caused by future heat waves in regions around the world under different SSP scenarios (e.g. SSP119, SSP245, SSP585), as well as global health losses, labor losses, and indirect losses as a percentage of regional or sectoral value added under different SSP scenarios. The regions of the database are aggregated using the GTAP 141 aggregating schema.</p>
Accompanying data for the paper "Non destructive qualification of bovine meat tenderness"
<p>This repository contains the raw data used in the article "Non destructive qualification of bovine meat tenderness",<br>authored by Sarah Iaquinta, Axel Conti, Mathis Coué, Étienne Roquefeuil, Jonathan Barés, Romain Léger, Arnaud Regazzi,<br>Stéphane Corn, Xavier Aleyrangues, Alain Peyron, and Anne-Sophie Caro.</p> <p>The repository is organized in three kind of files, where the raw data from the experiments are stored. <br>- "compression_test.xlsx" : Workbook containing the time-displacement-force measured during the compression tests;<br>- "indentation_relaxation.xlsx" : Workbook containing the time-displacement-force measured during the indentation-relaxation tests;<br>- .csv files containing the laser acquisition of the z_distance of the center of the indented surface during time.</p> <p>* The nomenclature of each sheetname and .csv file starts with the experiment date (April the 04th, 2023), then is the meatcut identifier <br>(FF = "faux filet", the french for "sirloin", and RDG = "rond-de-gîte", the french for "round". <br>Then are numbers to identify the number of the testing. </p>
Data and code for the paper "Macroscopic parameterization of positive streamer heads in air"
<p>This dataset contains the following:</p> <ul> <li><strong>ODE_solutions_no_photoi</strong>: the simulation results corresponding to figure 3 (see the README in the folder)</li> <li><strong>ODE_solutions_photoi</strong>: the simulation results corresponding to figure 6 (see the README in the folder)</li> <li><strong>input</strong>: the electron transport data used in the simulations (ionization coefficient, attachment coefficient, electron mobility) as a function of electric field strength</li> <li><strong>ODE_model.py</strong>: The version of the ODE model used in the paper</li> </ul> <p>The most recent version of the ODE model can be found at <a href="https://github.com/MD-CWI/streamer-head-ode">https://github.com/MD-CWI/streamer-head-ode</a></p>
Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"
<div> <div> <div> <div> <p><strong>Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a></strong></p> <p><strong><span>Suppl. File 1: Protein Family Clusters.</span></strong></p> <p><strong><span>Suppl. File 2: Cluster GO enrichments/depletions. </span></strong></p> <p><strong><span>Suppl. File 3: The raw ID-CBR data with annotations. </span></strong></p> <p><strong><span>Suppl. File 4: ­ID-CBR Cluster membership.</span></strong></p> <p><strong><span>Each file has an explanatory header. </span></strong></p> <p> </p> </div> </div> </div> </div>
Data for creating figures to the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."
<p>Processed data to generate figures for the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."</p> <p>The rate at which microscopic ocean plants, or phytoplankton, consume carbon dioxide represents a gap in scientific knowledge that needs to be filled in order to better model the earth system. To aid in this understanding we use a novel technique that allows us to track the growth behavior of phytoplankton in the Yellow Sea and the East Sea-Japan Sea. This is enabled by using satellite data from the Geostationary Ocean Color Imager, which has the unprecedented ability to collect quality biological information from the ocean surface each daylight hour. We find that the results, while in agreement with local observations and other satellite studies, also contain information about how phytoplankton change over daily to annual cycles and how native communities adapt in response to the annual solar cycle. This information is useful to the ocean modeling community, that seeks to understand various ways in which phytoplankton communities affect the cycling of Earth’s carbon.</p>
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort
<p># Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort" - Rev #1</p> <p>This is the dataset for the paper titled "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort".</p> <p>In case of questions, feel free to contact the authors, *anonymised*, ORCID: https://orcid.org/*anonymised*, current affiliation and email: *anonymised*</p> <p>## Survey 2019 ##<br> The raw survey data for the initial 2019 survey is available in the file *survey2019_anon.csv*. Note that the data is anonymised as free-text comments have been removed. Explanations on the variables and their levels are given in the files *variables_survey2019.csv* and *values_survey2019.csv*.<br> The questionnaire for the 2019 survey is contained in *survey2019_instrument.pdf*.</p> <p>## Survey 2020 ##<br> The raw survey data for the 2020 survey is available in the file *rdata_anon_survey2020.csv*. Additional scripts are supplied to reproduce the exploratory factor analysis. The main entry is the file *EFA.R*, which imports the data. The file contains some comments on the process.<br> The questionnaire for the 2020 survey is contained in *survey2020_instrument.pdf*.</p> <p>## Interviews ##<br> The interview guide used for the five interviews is available in the file *interview_instrument.pdf*.</p>
Supporting data to the paper "Modelling charge profiles of electric vehicles based on charges data"
<p>This dataset contains the<em> underling data</em> and the <em>extended data</em> for the paper Modelling charge profiles of electric vehicles based on charges data”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p>HISTORIC DATA 2019 ELECTROLINERES AMB.csv: contains information on the charge events at the public charging points managed by the municipality in the metropolitan area of Barcelona in 2019. Fields are: charging point name; connector typology and number; charge start time; charge stop time; charge duration in minutes, energy delivered in kWh; vehicle manufacturer (optional); vehicle model (optional).</p> <p>STATIC INFORMATION CHARGING POINTS AMB 29042020.csv: contains the information about the public charging points of the metropolitan area of Barcelona. Fields are: charger typology (Quick/Normal); Charging point name and address; OCCP version; charger location; longitude; latitude; 7 flag fields for the connector type; observations; charging point maker.</p> <p>Lataustapahtumat, julkiset latauslaitteet 2019.csv: contains the information about the Turku Energia charge events for the city of Turku in 2019. Fields are: date of record creation, Station ID, Station name, charge start time, charge stop time, charge duration in minutes, energy delivered in Wh, Plug type (AC 22 kW/DC 50 kW), Cumulative energy delivered in the year (Wh), Average charge power (W)</p> <p>EV.csv: containes data on battery size retrived from vehicle datasheet or manufacturer website. Fields are: record ID, vehicle manufacturer ; vehicle model; battery size in kWh.</p> <p>Charge2019_EV_AMB.csv: contains the data on charge requests ( HISTORIC DATA 2019 ELECTROLINERES AMB.csv ) combined with the information on vehicle battery (EV.csv).</p> <p> </p>
Data plotted in Figures in O'Connor et al. paper on methane forcing, including plotting scripts
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Apportionment of the Pre-Industrial to Present-Day Climate Forcing by Methane using UKESM1" submitted for publication to J. Adv. Modeling Earth Sys as csv files. Scripts used for plotting also included. </p>
Second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'
<p>This is the second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'.</p> <p>The paper was published in Geophysical Research Letters. We provide the data that has been smoothed by moving filter and not. The data can be loaded by the <em>raster </em>package in <em>R.</em> Note that the unit is ppmv.</p> <p>Please note that both of these files must be in the same directory to open in <em>R</em> properly<em>.</em></p> <p>Please get in touch with the authors if you have any issues, email: xliu21@smail.nju.edu.cn or wanghk@nju.edu.cn</p>
Data sets and models for the Deep API Learning Revisited paper
<p>Training and test data for the machine learning experiments described in the paper Deep API Learning Revisited paper. Trained models are also included.</p> <p>Deep API Learning Revisited paper: https://doi.org/10.1145/3524610.3527872</p> <p>GitHub repository: https://github.com/hapsby/deepAPIRevisited</p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Experimental data and scripts used for the paper "Experiments and low-order modelling of intermittent transitions between clockwise and anticlockwise spinning thermoacoustic modes in annular combustors"
<p>The folder contains the experimental data, the scripts an the instructions to generate the figures of the paper.</p> <p>Because of difficulties for uploading large files on zenodo, the heaviest files, which are the acoustic measurement files (.TDMS format), are not included in the zip file, but are put aside of it.</p> <p>For the scripts to work correctly, all the tdms files should be moved in the folder Faure-BeaulieuA_StochasticTransitionsAzimuthalMode_PROCI_20200713/01_input_data/</p>
Kimbe Bay Current Meter Data associated with Hydrodynamics Paper
<p>These data are the current meter data from each site and sea surface temperature data used in the paper " Contrasting hydrodynamic regimes of submerged pinnacle and emergent coral reefs". </p>
Raw and aggregated data for the study introduced in the paper "The way we cite: common metadata used across disciplines for defining bibliographic references"
<p>These data have been gathered in the context of a study aiming to investigate citation practices for referencing different types of entities and, in particular, for understanding the most used metadata in bibliographic references. The data are stored in two documents in XLSX format:</p> <ul> <li>file "links-intext-pointers-and-cited-entity-types.xlsx" - it contains information about whether the in-text reference pointers of the various PDF articles of the corpus have specified hypertextual links from the in-text reference pointers to the denoted bibliographic reference, plus information about the types of all the entities cited by each article in the corpus;</li> <li>file "metadata-bibliographic-references.xlsm" - it contains information about the metadata used to identify the various descriptive elements of all the bibliographic references defined in the article of the corpus.</li> </ul> <p>The methodology used to gather all these data is described in:</p> <blockquote> <p>Santos, E. A. d., Peroni, S., Mucheroni, M. L.: Workflow for retrieving all the data of the analysis introduced in the article "Citing and referencing habits in Medicine and Social Sciences journals in 2019". (2020), <a href="https://doi.org/10.17504/protocols.io.bbifikbn">https://doi.org/10.17504/protocols.io.bbifikbn</a></p> </blockquote>
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.