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”
Large-scale vibroacoustic simulations using parallel direct solvers for high-performance clusters - Dataset
<p>The dataset contains files to retreive the matrices used as benchmark for the contribution - Sreekumar, H. K. et. al. "Large-scale vibroacoustic simulations using parallel direct solvers for high-performance clusters", DAGA Conference 2021, Vienna, 2021.</p>
Archive of the iSALE simulation datasets for Jackson et al. 2022, "Impact generation of holes in the early lunar crust I: scaling relations"
<p>Archive of the iSALE simulation datasets used in Jackson et al. 2022, "Impact generation of holes in the early lunar crust I: scaling relations".</p> <p>This archive contains all necessary information to re-run any of the simulations in the dataset along with plots that can be used to check the outcomes of each simulation. In addition, it also contains a full compilation of all of the derived data used in Jackson et al. 2022 as well as the analysis scripts used to extract that data from the simulations and the scripts that use the derived data to generate the figures in the manuscript.</p> <p>The archive is provided in a standard compressed (.zip) format.</p> <p> </p> <p><strong>Detailed description of the contents</strong></p> <p>Note that this description can also be found in the ReadMe file provided with the archive</p> <p> </p> <p><strong>1) Simulation folders</strong></p> <p>The folder '<em>simsmain</em>' contains files for the iSALE2D simulations that comprise the primary simulation database.</p> <p>For each simulation we provide:</p> <ul> <li>asteroid.inp - the initialisation file for the simulation that would allow it to be re-run</li> <li>[].dat - a text file containing data derived from the final simulation output. These values from all simulations are compiled in alldata.csv. A description of the columns is provided below for alldata.csv</li> <li>[]-Crthickprof.pdf - a figure showing the radial crust thickness profile at the end of the simulation. Light pink shows the cell-by-cell profile, solid red a moving average.</li> <li>[]-Damprof.pdf - a figure showing the radial profile of the mean damage factor (averaged over the depth of the crust at each radial cell) at the end of the simulation. Light grey shows the cell-by-cell profile, solid black a moving average.</li> <li>[]-TmpDam.dpdf - a figure showing the temperature and damage structure of the entire high-resolution zone at the end of the simulation at 1:1 aspect ratio (i.e. square cells are square in the image). The boundary of the crust is outlined in black.</li> <li>[]-TmpDam-zoom.pdf - as above, but zoomed in on the central region of interest in the simulation. This image is stretched to highlight the region of interest (i.e. square cells are not square in the image).</li> <li>[]-temporal.dat - a text file containing data derived from the full simulation time series. These values from all simulations are compiled in alldata_temporal.csv. A description of the columns is provided below for alldata_temporal.csv</li> <li>[]-crdepthtime.png - a figure showing the change in the maximum depression of the upper surface of the simulation over time. This records the excavation and collapse of the initial transient cavity.</li> <li>[]-holeradtime.png - a figure recording the radius of the hole (as measured by the locations at which the crust thickness passes 90 and 95 percent of the initial value) over time. A final plateau is indicative of the simulation having settled.</li> <li>[]-holevoltime.png - a figure recording the volume of the hole within the 95% crust thickness radius over time.</li> <li>[]-trcavtime.png - a figure recording the volume of the surface depression over time. This can be quite sensitive to small fluctuations at the edges of the surface depression and does not always plateau.</li> </ul> <p>Within <em>simsmain</em> each sub-folder is named in the format dW_vX_cY_gZ, where d is the diameter of the impactor (in km), v is the impact velocity (in m/s), c is the crust thickness (in km) and g is the grid resolution of the high-resolution zone (in km). W, X, Y and Z are all in exponential notation with two significant figures (i.e. N.NE+NN).</p> <p>The folder '<em>simsres</em>' contains files for the additional iSALE2D simulations run for resolution testing purposes. These files have the same formats and naming conventions as <em>simsmain</em>.</p> <p>The folder '<em>material</em>' contains the file material.inp, the initialisation file for the material properties which is common to all of the simulations.</p> <p> </p> <p><strong>2) Analysis scripts</strong></p> <p>The folder '<em>analysisscripts</em>' contains the Python scripts that were used to generate the files in <em>simsmain</em> and <em>simsres</em>. There are two scripts, iSALEanalysis_batch.py which generates the end-of-simulation plots and data, and iSALEanalysis_batch_temporal.py which generates the time series plots and data. The temporal script typically takes around 400 times longer to run than the end-of-simulation script since the former must examine all (usually 800) timesteps, whereas the end-of-simulation script examines only the first and last timestep.</p> <p> </p> <p><strong>3) Figures</strong></p> <p>The folder 'figures' contains CSV files summarising all of the data derived from the iSALE2D simulations along with plotting scripts that use this derived data to generate the plots in Jackson et al. 2022.</p> <p>-----------<br> alldata.csv is a compilation of the data in the [].dat files for all 252 simulations in the primary simulation database. There are 14 columns which in order are:</p> <ol> <li>dimp - impactor diameter in km</li> <li>vimp - impact velocity in km/s</li> <li>crd - initial crust thickness in km</li> <li>tdat - simulation time (in seconds) at which the data was taken</li> <li>gres - grid resolution in the high-resolution zone in km</li> <li>rhires - radius of the high-resolution zone in km</li> <li>r_thincr0_95- radial distance (in km) at which the crust thickness exceeds 95% of the initial value</li> <li>r_thincr0_9 - radial distance (in km) at which the crust thickness exceeds 90% of the initial value</li> <li>r_magcr - radius (in km) within which there is direct exposure of magma to the surface (i.e. crust thickness is zero)</li> <li>r_frac09 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.9</li> <li>r_frac08 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.8</li> <li>r_frac05 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.5</li> <li>holevolume - volume (in km^3) of crust excavated from within r_thincr0_95</li> <li>comment2 - impact regime classification, 'partial'= partial penetration, 'complete' = complete penetration, 'cfd' = cratering with full-depth fracturing, 'classical' = classical crater.</li> </ol> <p>-----------<br> alldata_temporal.csv is a compilation of the data in the []-temporal.dat files for all 252 simulations in the primary simulation database. There are 20 columns which in order are:</p> <ol> <li>dimp - impactor diameter in km</li> <li>vimp - impact velocity in m/s (Note the difference from alldata.csv)</li> <li>crd - initial crust thickness in km</li> <li>tdat - simulation time (in seconds) at which the data was taken</li> <li>gres - grid resolution in the high-resolution zone in km</li> <li>rhires - radius of the high-resolution zone in km</li> <li>r_thincr0_95- radial distance (in km) at which the crust thickness exceeds 95% of the initial value</li> <li>r_thincr0_9 - radial distance (in km) at which the crust thickness exceeds 90% of the initial value</li> <li>r_magcr - radius (in km) within which there is direct exposure of magma to the surface (i.e. crust thickness is zero)</li> <li>r_frac09 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.9</li> <li>r_frac08 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.8</li> <li>r_frac05 - radial distance (in km) within which the vertically averaged damage factor in the crust exceeds 0.5</li> <li>holevolume - volume (in km^3) of crust excavated from within r_thincr0_95</li> <li>mcrd - maximum depth of the transient cavity in km</li> <li>t_mcrd - simulation time (in seconds) at which transient cavity reaches maximum depth</li> <li>trcav_mcrd - volume (in km^3) of the transient cavity at the time of maximum depth</li> <li>mtrcav - maximum volume of the transient cavity</li> <li>t_mtrcav - simulation time (in seconds) at which transient cavity reaches maximum volume</li> <li>crd_mtrcav - depth of the transient cavity (in km) at the time of maximum volume</li> <li>comment2 - impact regime classification, 'partial'= partial penetration, 'complete' = complete penetration, 'cfd' = cratering with full-depth fracturing, 'classical' = classical crater.</li> </ol> <p>-----------<br> restest-rthin.csv and restest-frac.csv are in the same format as alldata.csv and contain the data for the sets of simulations used to test the resolution dependence of r_thin and r_frac respectively.</p> <p>-----------</p> <p>There are 7 Python plotting scripts:</p> <ol> <li>plotter_main.py - the main plotting script, produces plots that don't fall under another heading</li> <li>plotter_KE.py - produces plots as a function of impact kinetic energy</li> <li>plotter_temporal.py - produces plots using time series data from alldata_temporal</li> <li>plotter_cox.py - produces plots comparing our results to those of Bray et al. 2014, Cox et al. 2008, Cox & Bauer 2015 and Miljkovic et al. 2015. Note that we do not include the CSV files containing the data from those papers since it is not ours to distribute, however they can be easily extracted from the relevant publications.</li> <li>plotter_res.py - produces resolution comparison plots</li> <li>plotter_pi.py - produces Pi-scaling plots</li> <li>kplot-sims.py - produces a plot of the 'k' factor (hole size/impactor mass) for comparison with Perera et al. 2018</li> </ol> <p>All of the plots and fitting summaries produced by the plotting scripts are also included</p>
Rapid review simulation dataset and code
<p>Dataset and statistical code for rapid review simulation study</p>
Dataset of NAME simulations of ash aggregation during the Eyjafjallajokull 2010 eruption
<p>This data set includes supporting data for the manuscript 'Modelling the size distribution of aggregated volcanic ash and implications for operational atmospheric dispersion modelling' submitted to <em>Atmospheric Chemistry and Physics</em>.</p> <p>The data set consists of the following folders:</p> <p><br> 1. 'SensitivityStudy_Uniform' includes input files and data output from the sensitivity study presented in Section 3 of the paper.</p> <p>2. Folders with the prefix 'Scaling' include the data generated from the scaling analysis presented in Section 4.</p> <p>3. 'NAMEEyjaAggRuns' includes the input files and output data from the NAME simulations of the Eyjafjallajokull 2010 ash cloud presented in Section 5. </p>
A dataset simulated by WRF-CM-BEM
<p>A dataset simulated by a regional climate model coupled with an urban canopy model and building energy model (WRF–CM–BEM). The study area was the Tokyo Metropolitan Area, Japan and the simulation period was summertime in 2017 (from 00UTC 25 June to 00UTC 01 September). This dataset includes model output (electricity consumption (EC) and near-surface air temperature) and model grid information (e.g., longitude, latitude, and land-use index) as netCDF files. There are two files: an improvement case (CASEnew.nc) and another case without improvements (CASEold.nc).</p>
Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere"
<p>Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux1 Transfer Events on the Ionosphere"</p> <p>Author: Arghyadeep Paul, Antoine Strugarek and Bhargav Vaidya<br> Date: 21 May, 2023</p> <p><br> Figure 1 has been plotted from two data files named C0_320.vtk and c1_320.vtk using the visualisation toolkit VisIt. Visit can be freely downloaded from https://wci.llnl.gov/simulation/computer-codes/visit</p> <p>Figure 2 has been plotted using the ipython notebook named "figure_2.ipynb"</p> <p><br> Figure 3 has been plotted using the data file named "t_4964.vtk" and the visualisation toolkit VisIt.</p> <p><br> Figure 4 has been plotted using the ipython notebook named "figure_4.ipynb"</p> <p><br> Figure 5 has been plotted using the ipython notebook named "figure_5.ipynb"</p> <p><br> Figure 6 has been plotted using the ipython notebook named "figure_6.ipynb"</p> <p><br> Figure 7 has been plotted using the ipython notebook named "figure_7.ipynb"</p> <p><br> Figure 8 has been plotted using the ipython notebook named "figure_8.ipynb"</p> <p><br> Figure 9 has been plotted using the ipython notebook named "figure_9.ipynb"</p> <p><br> All the associated data files are uploaded with the ipython notebooks</p>
Dataset on simulations of Kinetic of Fe(II) oxidation
<p>This upload contains simulation and fitting data matching figures in the publication "Kinetic constraints for the formation of microniches for microaerophilic Fe(II) oxidation”, Peiffer et al, submitted to GCA.</p> <p>It further contains the matlab code used for the simulations (<a href="https://zenodo.org/api/files/1b6a7c05-0f90-46b0-b70b-335060beac6a/micro_n_iteration_v1.m?versionId=983ccebf-337b-41b9-929c-dcddd1b8f59e">micro_n_iteration_v1.m) </a><br> </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.