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.
567
datasets available to search
ShareScore release 0.9.0
Dataset results
567 results for “Simulated datasets”
Simulated datasets used in SCIPAC analysis
Open the record for dataset details and reuse information.
Dataset for: Cure or Curse? Simulation Indicates that Microbes Proliferate under Disinfection Measures in the Space Station
Open the record for dataset details and reuse information.
Example simulated dataset for Strainy
<p>An example simulated dataset for Strainy tutorial (https://github.com/katerinakazantseva/strainy)</p>
Simulated datasets from cosimu
Open the record for dataset details and reuse information.
A dataset of global lake-level simulations and reconstructions since the Last Glacial Maximum
Open the record for dataset details and reuse information.
Simulated streamflow datasets under different future scenarios for near-future (NF, 2031-2060) and far-future (FF, 2071-2100) periods.
Open the record for dataset details and reuse information.
Dataset for "Quantifying the Role of Electron Plateau Distribution on the Chorus Gap Formation Using One-dimension PIC Simulations" [Dataset]
<p>Dataset for figures in the paper</p>
Dataset for "Equatorial evolution of the fast magnetosonic mode in the source region: Observation-simulation comparison of the preferential propagation direction"
<p>Includes 2D PIC simulation results presented in the paper.</p>
Dataset for network simulator comparison
<p>Dataset for network simulator comparison</p>
Dataset for molecular dynamics simulations of coalescence of Pd and AuPd clusters
Open the record for dataset details and reuse information.
Experiment dataset of people driving through simulated wildfire smoke using a driving simulator
<p>More information will be added soon.</p>
Dataset 1:Coarse-grained simulations of adeno-associated virus and its receptor reveal influences on membrane lipid organisation and curvature.
Open the record for dataset details and reuse information.
Dataset 2:Coarse-grained simulations of adeno-associated virus and its receptor reveal influences on membrane lipid organisation and curvature.
Open the record for dataset details and reuse information.
Datasets and Codes - Toward improved representation of reservoir operation at the larger scales: The role of calibration, the trade-off between reservoir release and storage, and the issue of error accumulation in long-term simulations
Open the record for dataset details and reuse information.
Emulator of PR-DNS: Part II, dataset for training the emulator of thermodynamics and cloud droplet fields in Particle-Resolved Direct Numerical Simulation
Open the record for dataset details and reuse information.
Codes and datasets associated with the paper "Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework"
<p>Here, you will find some of the codes, images, and datasets utilized in the article: </p> <p>Basu (2017). "Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework", Optical Engineering, 56(7), 071505 (https://doi.org/10.1117/1.OE.56.7.071505)</p> <p>WRF codes: namelist.wps, namelist.input, myoutfields.txt</p> <p>NCL codes: d02_terrain.ncl, wrf_SurfaceASTD_d02.ncl</p> <p>RADAR loop: KGRR.gif</p> <p>MATLAB codes: Plot_Buoy.m</p> <p>Note: buoy datasets are available publicly from https://www.ndbc.noaa.gov/ </p>
Materials Science Optimization Benchmark Dataset for Multi-fidelity Hard-sphere Packing Simulations
Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a "Turing test" of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 438371 random hard-sphere packing simulations representing 279 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets can be used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.
Codes and dataset for the green-beard gene simulation
<p>Codes and dataset for the green-beard gene simulation.</p>
Shallow Convection Datasets Simulated by Different Large Eddy Models
<p>The datasets contain four shallow convection cases (RICO, BOMEX, ATEX, and ARM-SGP) simulated by three large eddy models (SAM, WRF, UCLA-LES). For each convection case, there are nineteen two-dimensional variables, including winds, temperature, and humidity. Three types of plumes are provided, i.e., convective core refers to rising plumes that have positive buoyancy and contain condensed water, and convective updraft defined as plumes containing liquid water and upward vertical velocity, and cloud defined as grid points containing liquid water. Conditionally sampled variables such as in-cloud temperature, moisture and vertical velocity are also provided, which are indispensable for calculating entrainment rate. These datasets will be used as a reference to help users verify and improve parameterization schemes of shallow convection.</p> <p>Table 2 List of the LES output variables</p> <table align="center"> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Long name</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p>Zonal wind</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>V</p> </td> <td> <p>Meridional wind</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>W</p> </td> <td> <p>Vertical velocity</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>W2</p> </td> <td> <p>Variance of vertical velocity</p> </td> <td> <p>m<sup>2</sup>/s<sup>2</sup></p> </td> </tr> <tr> <td> <p>QT</p> </td> <td> <p>Total water</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>QV</p> </td> <td> <p>Water vapor</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>QC</p> </td> <td> <p>Cloud condensate</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>P</p> </td> <td> <p>Pressure</p> </td> <td> <p>hPa</p> </td> </tr> <tr> <td> <p>Z</p> </td> <td> <p>Height</p> </td> <td> <p>m</p> </td> </tr> <tr> <td> <p>A_cor</p> </td> <td> <p>Core fraction</p> </td> <td> <p>100%</p> </td> </tr> <tr> <td> <p>A_upd</p> </td> <td> <p>Updraft fraction</p> </td> <td> <p>100%</p> </td> </tr> <tr> <td> <p>A_cld</p> </td> <td> <p>Cloud fraction</p> </td> <td> <p>100%</p> </td> </tr> <tr> <td> <p>QT_cor</p> </td> <td> <p>Mean qt in core</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>QT_upd</p> </td> <td> <p>Mean qt in updraft</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>QT_cld</p> </td> <td> <p>Mean qt in cloud</p> </td> <td> <p>g/kg</p> </td> </tr> <tr> <td> <p>W_cor</p> </td> <td> <p>Mean w in core</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>W_upd</p> </td> <td> <p>Mean w in updraft</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>W_cld</p> </td> <td> <p>Mean w in cloud</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>QTFLUX</p> </td> <td> <p>Total water flux</p> </td> <td> <p>W/m<sup>2</sup></p> </td> </tr> </tbody> </table>
Dataset I related to publication: Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations
<p>Dataset related to publication:<em> </em></p> <p><em>Leini, R., Kapp, J., Kopra, K. et al. Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations. Sci Rep <strong>15</strong>, 24843 (2025). https://doi.org/10.1038/s41598-025-07532-2</em></p> <ul> <li>.zip-files contain the raw Desmond trajectories of individual replica simulations of GDC-6036.</li> <li>WaterMap-output file for the representative conformation of GDC-6036.</li> </ul>
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.