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.
10,119
datasets available to search
ShareScore release 0.9.0
Dataset results
10,119 results for “IT Support”
Data supporting "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products"
<p>Changelog</p> <p>v2: Fixes an identified issue in FluxEngine v4.0.7 that affects the calculation of fCO2atm. Fluxes have been recalculated using FluxEngine v4.0.9.1, and the analysis regenerated. The intergrated air-sea CO2 flux (or ocean sink) has reduced by ~0.2-0.3Pg C yr-1 but uncertainties are unchanged. </p> <p>v1: Initial dataset released along with the supporting manuscript</p> <p> </p> <p>Data included in this repository supports the manuscript "A comprehensive analysis of air-sea CO<sub>2</sub> flux uncertainties constructed from surface ocean data products".</p> <p>Two files are present:</p> <ol> <li>A Python config file used to run the software developed for the analysis (Ford et al., 2024)</li> <li>A ZIP file containing the input, neural network, and output files for the analysis.</li> </ol> <p>Within the ZIP file, multiple folders are present:</p> <ol> <li>Decorrelation contains .csv files that contain the annual estimates of the decorrelation lengths for the parameters requiring these (SST, sea ice, wind, fCO<sub>2</sub> and fCO<sub>2</sub> network).</li> <li>Flux contains the individual FluxEngine output files that provide all the flux calculations, and auxillary data to the flux calculations.</li> <li>Fluxengine_input contains the input files to FluxEngine, which specifies the fCO<sub>2 (sw), </sub>xCO<sub>2 (atm)</sub> and the temperature, salinities for the skin and subskin layers.</li> <li>Inputs contains all the monthly 1 degree input data used. Many of the data used are not native monthly 1 deg, and so these are generated from the higher resolution data. These are all combined into the neural_network_input.nc file, so a single file can be distributed with all the inputs used.</li> <li>Networks contains the TensorFlow neural network (FNN) files, where each province has 10 folders (one for each ensemble).</li> <li>Plots contains output plots for debugging and final plots of uncertainties</li> <li>Scalars contains the scalars used to normalise the data before input into the neural network. These are saved as Python pickle files, as they are needed if the neural network is used on other data.</li> <li>Unc_lut contains the look up tables to generate the parameter uncertainty as described in the manuscript. These are Python pickle files.</li> <li>Validation contains a csv file with the independent test RMSD, along with Python Pickle files of the validation data.</li> </ol> <p>In the main folder, three files are present:</p> <ol> <li>Annual_flux.csv contains the annual air-sea CO<sub>2</sub> flux (or ocean sink estimate) estimated from the fCO<sub>2 (sw)</sub> fields. This also contains the annual integrated uncertainties for each component in the uncertainty flow chart in the manuscript.</li> <li>Output.nc contrains the gridded global fields of the fCO<sub>2 (sw)</sub>, the air-sea CO<sub>2</sub> flux, and the uncertainties for all the individual components. Metadata within the file should provide all the information required.</li> <li>Training.tsv contains the training/validation data alongside the input parameters for neural network training</li> </ol> <p> </p> <p>Please contact Daniel J. Ford (<a href="mailto:d.ford@exeter.ac.uk">d.ford@exeter.ac.uk</a>) if you have any questions.</p> <p><strong>Acknowledgements</strong></p> <p>This work was funded by the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Union under grant agreement no. 101083922 (OceanICU; https://ocean-icu.eu/) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p> </p> <p><strong>References</strong></p> <p>Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschutzer, P., Jersild, A., & Shutler, J. D. (2024, June 30). OceanICU Neural Network Framework with per pixel uncertainty propagation (v1.1) (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.12597803</p>
Supporting information for: An assessment of monazite fission-track thermochronology as a proxy for low-magnitude cooling, Catalina-Rincon Metamorphic Core Complex, AZ, U.S.A.
<p><span>The following supporting information contains: The detailed location and age data for the geochronological, isotopic, and geochemical data used in this study, and their associated publications. Detailed thermochronometric data and associated thermal history modelling information for all thermochronology and modelling presented in the study.</span></p>
Data supporting "Slowest-first translation scheme: Structural asymmetry along protein sequences and co-translational folding"
<p>Contains data for a set of 16,200 non-redundant protein structures taken from the Protein Data Bank. Associated code can be found at https://github.com/jomimc/FoldAsymCode.</p>
Supporting data for: "Data-driven discovery of cardiolipin-selective small molecules by computational active learning"
<p>This repository contains supporting data and code for the paper titled "Data-driven discovery of cardiolipin-selective small molecules by computational active learning" by Bernadette Mohr, Kirill Shmilovich, Isabel Kleinwächter, Dirk Schneider, Andrew L.Ferguson, and Tristan Bereau.</p>
Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in Caenorhabditis elegans
<p>This dataset contains all data and codes that are used in the manuscript entitled "Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in <em>Caenorhabditis elegans</em>".</p> <p>The data include raw videos and intermediate data for optogenetic experiments of all strains, experimental conditions (illumination duration, illuminated region, fluid viscosity and date). Within the parent folder 'Videos', each subfolder represents data of a group of experiments using the same strain under the same condition, as indicated explicitly by the subfolder name. Within each subfolder, there are raw videos of freely moving worms perturbed by transient optogenetic perturbations and intermediate data which include locomotory information and the corresponding figure plots (kymographs) that were generated by analysing the raw videos with the image analysis software (also in the dataset)</p> <p>The codes include scripts for image data analysis and model simulations. The image data analysis codes include scripts specifically for generating phase portrait graphs, phase response curves, head oscillation stability plots, phase isochron map and vector field. The model simulation codes include scripts for model oscillators implementation, paramter estimation/optimization and simulations of optogenetic inhibition.</p>
Supporting data (structural data, sample descriptions, microprobe data)
<p>Supporting data for the manuscript entitled "Tectonic evolution of the Nevado-Filábride complex (Sierra de los Filábres, Southeastern Spain): insights from new structural and geochronological data", submitted to Tectonics.</p>
Supporting dataset for the paper : " Hydro-geomorphic metrics for high resolution fluvial landscape analysis"
<p>This repository contains all the original data supporting the results of Bernard et al., 2021: "Consistent hydro-geomorphic indicators for high resolution topographic analysis".<br> The parameter used to perform hydraulic simulations are also available.<br> </p>
Supporting data for PRX Quantum 2, 040342 (2021)
<p>Data from the simulations presented in <a href="https://dx.doi.org/10.1103/PRXQuantum.2.040342">PRX Quantum 2, 040342 (2021)</a>, and generated with the code published in <a href="https://github.com/matteoacrossi/adapt_ic-povm">https://github.com/matteoacrossi/adapt_ic-povm</a></p> <p><strong>Instructions</strong></p> <p>The file data.tar contains the following files:</p> <ul> <li>hamiltonians_h_chain.pickle </li> <li>hamiltonians_params.json </li> <li>hamiltonians.pickle</li> <li>chemistry.feather</li> <li>h_chain.feather </li> <li>tomography_counts_data.jsonl</li> <li>fidelities_tomo.json</li> </ul> <p>The files `hamiltonians*` contain the Hamiltonians considered in the paper, as well as the corresponding exact ground states and example VQE ground states.</p> <p>The files `chemistry.feather` and `h_chain.feather` contain all the results for the molecules and hydrogen chain (respectively).</p> <p>The files `tomography_counts_data.jsonl` and `fidelities_tomo.json` contain (respectively) the raw data for tomographical reconstruction and the fidelities of the reconstructed density matrices used to produce Fig. 5.</p> <p>The files can be processed using the Figures.ipynb file in the linked repository. Please refer to the <a href="https://github.com/matteoacrossi/adapt_ic-povm#readme">README.md</a> file for more detail on how to use and reproduce this data.</p> <p>This new version fixes a mismatch between the states stored in hamiltonians.pickle and the ones used for simulating the measurements.</p>
Research Data Supporting "Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects"
<p>This repository contains the set of data shown in the paper "<strong>Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects</strong>", published on <strong>ACS Nano</strong> (DOI: 10.1021/acsnano.1c01398).</p> <p>The data stored in this section is organized as follow:</p> <p><strong>supervisedClustering/ :</strong> folder containing scripts for carrying out the supervised analysis as discussed in the main paper. Subfolders are divided in <strong><code>kmeans</code></strong><code><strong>/</strong></code> and <strong><code>spectral</code></strong><code>/</code> contain the different analysis as explained in the paper.</p> <p><strong>unbiasMD/ : </strong>folder containing the Gromacs input files for reproducing the unbias MD trajectories of the 3 types of fiber, as explained in the paper.</p> <p><strong>biasMD/</strong> <strong>:</strong> folder containing the PLUMED input files for reproducing enhanced sampling dynamics as discussed in the paper.</p> <p><strong>unsupervisedClustering/ : </strong>folder containing the directions and the original files to reproduce the unsupervised clustering results.</p>
Computational Data supporting "Porous covalent organic nanotubes and their assembly in loops and toroids"
<p>Computational research data supporting the article:<br> Kalipada Koner, Shayan Karak, Sharath Kandambeth, Suvendu Karak, Neethu Thomas, Luigi Leanza, Claudio Perego, Luca Pesce, Riccardo Capelli, Monika Moun, Monika Bhakar, Thalasseril G. Ajithkumar, Giovanni M. Pavan, and Rahul Banerjee, "Porous Covalent Organic Nanotubes and Toroids: A Carbon Nanotube Analogue" </p>
Supporting data for the AI education publication statistics in "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates"
<p>Supporting data for the AI education publication statistics presented in the paper "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates" to be published at the Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22). The data was used to plot the figure showing the cumulative number of publications from 1976 to 2020 relating to AI education.</p>
Processed data and models in support of manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion"
<p>Data and model files in original format used in the manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion". These files are accompanied by a set of python scripts to reproduce several of the figures in the Manuscript. Please refer to the Manuscript and the included files for further information on data origin and how to use the scripts. A link will be added upon acceptance.</p>
Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"
<p>Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019</p>
Stronger negative species interactions in the tropics supported by a global analysis of nest predation in songbirds
<p>Original data, phylogeny and list of studies from: "Stronger negative species interactions in the tropics supported by a global analysis of nest predation in songbirds"</p>
WGS pipeline supporting files
<p>These are supporting files used for our WGS analysis pipeline described in <a href="https://github.com/edg1983/WGS_pipeline">https://github.com/edg1983/WGS_pipeline</a></p> <p>Somalier files are derived from <a href="https://github.com/brentp/somalier">https://github.com/brentp/somalier</a></p> <p>Expansion Hunter files represent the variants catalog from Expansion Hunter v5.0.0 (<a href="https://github.com/Illumina/ExpansionHunter">https://github.com/Illumina/ExpansionHunter</a>)</p>
Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'
<p>Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'</p> <p>Contact: C. Scott Watson. c.s.watson@leeds.ac.uk</p> <p>DRR_greenspace<br> DRR_greenspace_polygon.shp - classified potential DRR greenspace (minimum 100 m2)<br> DRR_greenspace_zone_points.shp - classified potential DRR greenspace aggregated to zones<br> DRR_greenspace_zone_polygons_top10.shp - Top 10 maximum capacitated analysis of classified potential DRR greenspace aggregated to zones.<br> <br> Land_cover<br> rf_1986_mode_clipped.tif - 1986 land cover classification<br> rf_2020_mode_clipped.tif - 2020 land cover classification<br> landcover_classes.PNG - land cover classes<br> accuracy_assessment_points_1986 - 1986 land cover accuracy assessment points<br> accuracy_assessment_points_2020 - 2020 land cover accuracy assessment points<br> modified_urban_growth_scenario.shp - hazard-modified urban growth scenario</p> <p> </p>
Supporting data and software for: Low-temperature open-air synthesis of PVP-coated NaYF4:Yb,Er,Mn upconversion nanoparticles with strong red emission
<p>Upconversion nanoparticles (UCNPs) have unique photonic properties that make them ideally suited for many applications. They are excited by low-energy near-infrared photons and emit at higher energy (typically visible) wavebands. However, synthesis of UCNPs requires either high pressure reaction chambers or inert atmospheres. Combined with the requirements for high-temperatures (200 to 400 °C) and long reaction times (e.g. up to 24 hours), these place barriers to entry for UCNP research, in terms of both financial barriers and knowledge/"know how". These constraints may also limit the scale of UCNP production for end-user applications.</p> <p>We adapted and further developed a method for producing UCNPs with simple laboratory equipment, i.e. a hot-plate and beakers. No pressure vessel or inert atmosphere is required. The UCNPs produced have a<span> polyvinylpyrrolidone (PVP) polymer coating, with strong red emission due to Mn<sup>2+</sup> co-doping within the UCNP crystal lattice. It was found that UCNPs of composition NaYF<sub>4</sub>:Yb,Er,Mn (Yb = 20 mol %, Er = 2 mol%, Mn = 35 mol%) maximised the red emission whilst also minimising the diameter of the UCNPs to </span> 36 ± 15 nm. These combination of optical and physical properties should make these UCNPs ideal for further development and exploitation, particularly for biological applications where red emission can penetrate over a centimetre of tissue.</p> <p>This dataset and software accompanies the manuscript <em>'Low-temperature open-air synthesis of PVP-coated NaYF<sub>4:</sub>Yb,Er,Mn upconversion nanoparticles with strong red emission</em>', which was published in Royal Society Open Science on 19th January 2022. https://doi.org/10.1098/rsos.211508</p>
Supporting Data for Drift Phase Structure Implications for Radiation Belt Transport by T.P. O'Brien et al. submitted to J. Geophysical Res.
<p>Datasets used in Drift Phase Structure Implications for Radiation Belt Transport by T.P. O'Brien et al. submitted to J. Geophysical Res.</p>
Research data supporting for "Periclase deforms slower than bridgmanite under mantle conditions"
<p>This deposits contains the files related to the Dislocation Dynamics calculations of creep in MgO presented in the paper</p>
Data to support the publication "The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment" https://doi.org/10.3390/land10090964
<p>Underlying data of soil measurements and analysis by TUC team for the publication “The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment” <a href="https://doi.org/10.3390/land10090964">https://doi.org/10.3390/land10090964</a> from the SoilCare project study sites in Crete. </p> <p>Abstract:</p> <p>The risk of erosion is particularly high in Mediterranean areas, especially in areas that are subject to a not so effective agricultural management–or with some omissions–, land abandonment or wildfires. Soils on Crete are under imminent threat of desertification, characterized by loss of vegetation, water erosion, and subsequently, loss of soil. Several large-scale studies have estimated average soil erosion on the island between 6 and 8 Mg/ha/year, but more localized investigations assess soil losses one order of magnitude higher. An experiment initiated in 2017, under the framework of the SoilCare H2020 EU project, aimed to evaluate the effect of different management practices on the soil erosion. The experiment was set up in control versus treatment experimental design including different sets of treatments, targeting the most important cultivations on Crete (olive orchards, vineyards, fruit orchards). The minimum-to-no tillage practice was adopted as an erosion mitigation practice for the olive orchard study site, while for the vineyard site, the cover crop practice was used. For the fruit orchard field, the crop-type change procedure (orange to avocado) was used. The experiment demonstrated that soil-improving cropping techniques have an important impact on soil erosion, and as a result, on soil water conservation that is of primary importance, especially for the Mediterranean dry regions. The demonstration of the findings is of practical use to most stakeholders, especially those that live and work with the local land.</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.