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.
1,028
datasets available to search
ShareScore release 0.7.1
Dataset results
1,028 results for “modelling & simulation”
Simulation results for "COVID-19 vaccination in Sindh Province, Pakistan: a modelling study of health impact and cost-effectiveness"
<p>The high-performance computing results for raw epidemiological simulations and quantiled scenarios associated with https://doi.org/10.1101/2021.02.24.21252338.</p> <p>All results stored as compressed rds files of data.table objects (use-able as data.frame objects) for use with R programming language.</p>
Data and simulations files for the article "Accurate modeling and characterization of photothermal forces in optomechanics"
<p>Data and simulations files for the article "Accurate modeling and characterization of photothermal forces in optomechanics".</p>
FESOM output supporting: Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?
<p>AWI-CM1 and FESOM1.4 simulation results used in the manuscript "Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?".</p>
Comparing simulations and experiments of positive streamers in air: steps toward model validation
<p>This dataset consists of files used to produce the data presented in the article "Comparing simulations and experiments of positive streamers in air: steps toward model validation". This dataset includes 1) source code for the simulations; 2) transport data files, which contain a list of included reactions, their reaction rate coefficients, and transport coefficients; 3) configuration files for running the simulations; 4) outputted log files of the simulations and 5) original experimental images.</p> <p>This dataset is for the revised paper.</p>
Data supplement for: Agreement of analytical and simulation-based estimates of the required land depth in climate models
<p>Many current-generation climate models have land components that are too shallow. Under climate change conditions, the long-term warming trend at the surface propagates deeper into the ground than the commonly used 3-10m. Shallow models alter the terrestrial heat storage and distribution of temperatures in the subsurface, influencing the simulated land-atmosphere interactions. Previous studies focusing on annual timescales suggest that deeper models are required to match subsurface-temperature observations and the classic analytical heat conduction solution. However, for a systematic investigation of land-model deepening in the frame of anthropogenic climate change, the classic analytical solution is inaccurate because it does not mimic the timescale and amplitude of the simulated warming trend. This study intends to bridge the gap between analytical and simulation-based estimates of the subsurface thermodynamic state by adapting the classic analytical framework to mimic long-term anthropogenic warming. The analysis shows that a land-model depth of at least 170m is recommended for a proper simulation of the post-1850 ground climate, which differs up to 30% from the estimate of the classic approach. Compared to previous studies, this provides an accurate estimate of the required land model depth for long-term climate-change simulations and indicates the relative bias in insufficiently deep land models.</p>
The configurations, inputs and outputs of the EFDC model for all simulated episodes
<p>TAIHU EFDC.zip includes two file folders named 2015 and 2018. The 2015 file folder is the configurations, inputs and outputs of the EFDC model for the numerical experiment named EFDC of 2015. The 2018 file folder is the configurations, inputs and outputs of the EFDC model for the numerical experiment named EFDC of 2018.</p>
Lattice kinetic Monte Carlo model to simulate RNA polymerase II clusters
<p>This data set includes Python scripts (numerical simulation and analysis) and already generated simulation data for RNA polymerase II clusters. RNA polymerase II particles as single lattice sites and chromatin with regulatory region as connected polymer.</p>
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
<p><span>The datasets store both motion and observation information of a single fluorescent sub-diffraction limit-sized particle moving in a three-dimensional confined environment. The confined motion is following a nonlinear model driven by non-Gaussian noise, the observation is formed by engineered Double-helix (DH) point spread function (PSF) and captured by scientific complementary metal-oxide semiconductor (sCMOS) camera. Based on our prior computationally efficient application of Sequential Monte Carlo - Expectation Maximization (SMC-EM), we extended it to handle the DH-PSF for encoding the three-dimensional position of the particle in two-dimensional image plane of the camera. We focus on studying the datasets at low signal and low signal-to-background ratio (SBR). Based on the datasets across different SBR and confinement lengths, a quantitative comparison is conducted to show that in the low signal regime, the SMC-EM approach outperforms the other methods while at higher signal-to-background levels, SMC-EM and the MLE-based methods perform equally well and both are significantly better than fitting to the MSD. In addition, our results indicate that at smaller confinement lengths where the nonlinearities dominate the motion model, the SMC-EM approach is superior to the alternative approaches. </span></p>
Model output from hydrothermal simulations
<p>Files Fe_spreading.nc, Fe_inverse.nc and Fe_inverseR.nc represent dissolved iron fields (mmol/m3) and files ECP_spreading.nc, ECP_inverse.nc and ECP_inverseR.nc export production at 100m (mol/m2/s) from experiments of those names in Tagliabue et al., "Constraining the contribution of hydrothermal iron to the Southern Ocean biological carbon pump using deep ocean iron observations" Frontiers in Marine Science</p>
Simulated CO2 time series data based on Jena CO2 inversion and TM3 transport model, and MIROC-ACTM
<p>Each file contains simulated CO2 time series at each surface station. The model, simulation type, and station are specified in the file name. These simulations are driven by either varying winds alone (e.g., Jena_W) or varying winds and fluxes (e.g., Jena_WF). The only MIROC-ACTM run is named ACTM_W_MLO.</p>
Discrete Element Model Simulations for a Granular Bed Undergoing Shear Deformation
<p>Discrete Element Model (DEM) Archive for,</p> <p>"Shear Variation at the Ice-Till Interface Changes the Spatial Distribution of Till Porosity and Meltwater Drainage"</p> <p>by,<br> Indraneel Kasmalkar, Anders Damsgaard, Liran Goren, Jenny Suckale</p> <p><br> This dataset contains the output files of the DEM simulations for understanding how the porosity of a granular bed evolves with an imposed laterally varying shear. The DEM simulations are performed using Sphere (<a href="https://src.adamsgaard.dk/sphere/">https://src.adamsgaard.dk/sphere/</a>), developed by Anders Damsgaard.</p> <p>The output files are in the 'output' folder.<br> The porosity values are curated and stored in 'porosities' folder as 4D numpy arrays.<br> The results are generated as .pdf files in the 'Images' folder.<br> The scripts are stored in the 'python' folder.</p> <p><br> The main file in the 'python' folder is MyVisualization.py.</p> <p>To run the script you will need Python 3.6 or newer, with the following packages:</p> <p>numpy, scipy, matplotlib, colorbrewer, math, pickle</p> <p>In MyVisualization.py, go to line<br> if __name__ == '__main__'</p> <p>Before the line, you can change the variable plot_type to get the plot you need.<br> The options for plot_type are listed in a comment right before the variable.<br> You can limit the plotting to either simple shear ('unif') or laterally varying shear ('hl') within each if condition thereafter</p>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America : Dataset
<p>This dataset is linked to the paper “Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-16-1661-2023). It contains the installer of the model, its user guide, as well as all the input files (inventory, thinning, meteorology and soil horizons files for each stand used in the evaluation and calibration steps), the R scripts and associated data used to analyse the model outputs.</p>
CESM 1.2 climate model simulation output for: The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model 'Wind Stress Shaving' Experiments
<p>Westerly wind bursts (WWBs)—brief but strong westerly wind anomalies in the equatorial Pacific—are believed to play an important role in El Niño Southern Oscillation (ENSO) dynamics, but quantifying their effects is challenging. Here, we investigate the cumulative effects of WWBs on ENSO characteristics, including the occurrence of extreme El Niño events, via modified coupled model experiments within Community Earth System Model (CESM1) in which we progressively reduce the impacts of wind stress anomalies associated with model-generated WWBs. In these "wind stress shaving" experiments we limit momentum transfer from the atmosphere to the ocean above a preset threshold, thus "shaving off" wind bursts. To reduce the tropical Pacific mean state drift, both westerly and easterly wind bursts are removed, although the changes are dominated by WWB reduction. As we impose progressively stronger thresholds, both ENSO amplitude and the frequency of extreme El Niño decrease, and ENSO becomes less asymmetric. The warming center of El Niño shifts westward, indicating less frequent and weaker Eastern Pacific (EP) El Niño events. Removing most of wind bursts-related wind stress anomalies reduces ENSO amplitude by 22%. The essential role of WWBs in the development of extreme El Niño events is revealed in the suppressed eastward migration of the western Pacific warm pool and hence a weaker Bjerknes feedback under wind shaving. Overall, our results reaffirm the importance of WWBs in shaping the characteristics of ENSO and its extreme events and imply that WWB changes with global warming could influence future ENSO.</p>
Thermo-hydro-chemical simulation of mid-ocean ridge hydrothermal systems: Static 2D models and effects of paleo-seawater chemistry
<p>DePaolo et al. Gcubed 2022 data files</p> <p><strong>Thermo-hydro-chemical simulation of mid-ocean ridge hydrothermal systems: </strong></p> <p><strong>Static 2D models and effects of paleo-seawater chemistry </strong></p> <p> </p> <p>In this folder are input and output files for v3.68 of TOUGHREACT that contain all of the files illustrated in the manuscript plus many more. Also included is v3 TOUGHREACT reference manual, which gives more information on all of the input and output files.</p> <p>In each folder there are a sequence of run folders, each containing input files (flow.inp, solute.inp, chemical.inp, MESH, GENER, plus a thermodynamic database with filename like “tkslth06acp3isi9.dat.” Also included are raw tecplot files (flowvector.tec, flowdata.tec, rct_sfarea.tec, rctn_rate.tec, min_SI.tec, minerals.tec, aqconc.tec) and other output files (all “.out” files). In some cases the .tec files, which are combined files with output for both fractures and matrix, have been separated into separate fracture and matrix files with names like “flowvector_frc.tec,” “flowvector_mtx.tec,” aqconc_frc.tec,” “aqconc_mtx.tec” to allow plotting of fracture and matrix properties separately.</p> <p>Some folders also contain .tiff or .png files that are 2D color contour plots as shown in the manuscript. All of these plots were made with Paraview (<a href="https://www.paraview.org/">https://www.paraview.org</a>) which is open-source.</p> <p>Each folder labeled like “Modern SW fastcpx Sr8…” contains several subfolders each labeled with the model year at which the run ends, like 2000, 2600, 2700, 2800, … which correspond to the warmup steps described in the manuscript:</p> <p>The typical procedure used to achieve the results reported here is (with some minor variations):</p> <ol> <li>Run the simulation for 2000 model years with 50% of the final heating from below and minimal chemical reactions. RSA for primary minerals in both matrix and fractures are set to 10<sup>-6</sup> cm<sup>2</sup>/g and 2 x 10<sup>-6</sup>cm<sup>2</sup>/g for secondary minerals, which yields chemical reaction rates about 500 times slower than for a more realistic system.</li> <li>Run for an additional 600 model years with the full heating from below and RSA’s at 10<sup>-6</sup> cm<sup>2</sup>/g and 2 x 10<sup>-6</sup> cm<sup>2</sup>/g. This step yields a steady state temperature and flow field with the full heating from below. Less time is needed than for the first phase because the fluid flow velocities are higher with higher heating rates.</li> <li>Run an additional 100 years; RSA’s increased to 10<sup>-5</sup> cm<sup>2</sup>/g and 2 x 10<sup>-5</sup> cm<sup>2</sup>/g</li> <li>Run 100 years; RSA’s at 10<sup>-4</sup> cm<sup>2</sup>/g and 2 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 100 years; RSA’s at 2 x 10<sup>-4</sup> cm<sup>2</sup>/g and 4 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 50 years; RSA’s at 3 x 10<sup>-4</sup> cm<sup>2</sup>/g and 5 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 50 years; RSA’s at 4 x 10<sup>-4</sup> cm<sup>2</sup>/g and 8 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 100 additional years*</li> </ol> <p>After step 8 the system has been running for 3100 model years, but only 150 years with full reactions, which is long enough to get close to quasi-steady state fluid chemistry (there is no true steady state for chemistry because the rock mineralogy is changing with time). For each of the steps marked with an asterisk, an alternative procedure is to use high RSA’s for fracture minerals, up to 50 times higher. </p> <p>In some folders there are additional subfolders extending in model time up to 3400 years.</p>
Performance and limits of a shallow-water model for landslide-generated tsunamis: from laboratory experiments to simulations of flank collapses at Montagne Pelée (Martinique) - DATASETS
<p>Datasets of the 6 presented experiments with 4mm beads presented in the paper "Performance and limits of a shallow-water model for landslide-generated tsunamis: from laboratory experiments to simulations of flank collapses at Montagne Pelée (Martinique)".</p> <p>Each datasets (csv file) corresponds to the hand picked profile of either the water free surface or the granular material at 0.1 second of interval. </p> <p>The third dataset for each experiment corespond of the gauges records.</p>
Data from simulations of the thalamocortical loop model
<p>This zip file contains, separated into different folders, the data and metadata resulting from simulating different stimuli protocols with a thalamocortical spiking network model (https://github.com/dguarino/T2). The model was developed using the Mozaik framework (https://github.com/antolikjan/mozaik), which itself relies on PyNN (http://neuralensemble.org/PyNN/), NEST (https://www.nest-simulator.org/), and other libraries (see the Mozaik specs).</p> <p>Inside the zipped folder, there will be the following sub-folders, each containing the python pickled output recording of the recorded spikes (with Vm, and conductances for a subset of simulated neurons) in the Neo format (https://neo.readthedocs.io/en/stable/ ):</p> <p>ThalamoCorticalModel_data_contrast_closed_____<br> ThalamoCorticalModel_data_contrast_open_____<br> ThalamoCorticalModel_data_luminance_closed_____<br> ThalamoCorticalModel_data_luminance_open_____<br> ThalamoCorticalModel_data_orientation_closed_____<br> ThalamoCorticalModel_data_orientation_feedforward_____<br> ThalamoCorticalModel_data_orientation_open_____<br> ThalamoCorticalModel_data_size_closed_____<br> ThalamoCorticalModel_data_size_closed_____large<br> ThalamoCorticalModel_data_size_closed_cross-oriented_____<br> ThalamoCorticalModel_data_size_feedforward_____<br> ThalamoCorticalModel_data_size_feedforward_____large<br> ThalamoCorticalModel_data_size_feedforward_____old<br> ThalamoCorticalModel_data_size_LGNonly_____<br> ThalamoCorticalModel_data_size_nonoverlapping_____<br> ThalamoCorticalModel_data_size_open_____<br> ThalamoCorticalModel_data_size_overlapping_____<br> ThalamoCorticalModel_data_size_overlapping_____old<br> ThalamoCorticalModel_data_spatial_closed_____<br> ThalamoCorticalModel_data_spatial_Kimura_____<br> ThalamoCorticalModel_data_spatial_LGNonly_____<br> ThalamoCorticalModel_data_spatial_open_____</p>
SIMCor - Demo of TAVI deployment simulations using the virtual geometries and a high-fidelity model
<p>This video demonstrates the feasibility of performing finite element TAVI deployment simulations in the virtual patient geometries generated using the Virtual Cohort Generator. First, the TAVI device is positioned within the aortic root. Then, the device is crimped into the catheter sheath. Afterwards, the contacts between device and aorta are activated and the sheath is removed, resulting in deployment of the device within the aortic root. The principal stresses in the tissue resulting from the implantation are shown.</p>
Data for m-NLP inference models using simulation and regression techniques
<p>This file contains data for the manuscript ``m-NLP inference models using simulation and regression techniques''.</p> <p>The "needle probe data.xlsx" file contains the simulation results and the fits to the simulation data. It also contains the coefficients a, b, and c for which a synthetic solution library can be constructed. Examples of the synthetic solution library are also included, named "nndlt.dat" and "nndlv.dat". The "center.out" file contains the Radial Basis Function density inference model created from the synthetic solution library.</p> <p>The NorSat-1 data can be obtained from: http://tid.uio.no/plasma/norsat/norsat1.html</p>
Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study
<p>Data and R codes necessary to replicate the analyses presented in the paper entitled "Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study", published in Peer Community in Ecology (<a href="https://doi.org/10.24072/pcjournal.263">10.24072/pcjournal.263</a>).</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.