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”
A simple framework for agent-based modeling with extracellular matrix: Simulation results
<h1>Data for "A simple framework for agent-based modeling with extracellular matrix"</h1> <p> </p> <div>Metzcar, J., Duggan, Ben S., Fischer, B., Murphy, M., Heiland, R., Macklin, P. A simple framework for agent-based modeling with extracellular matrix. bioRxiv. doi: 10.1101/2022.11.21.514608</div> <div> </div> <div><a href="https://www.biorxiv.org/content/10.1101/2022.11.21.514608">Link to preprint</a></div> <div> <p>This repository contains the data for each subfigure (and video) in the preprint cited and linked abvoe, as well as the original figures and videos themselves. We have tried to include original model file (<code>PhysiCell_settings.xml</code> or other <code>*.xml</code> file) used to generate each results with each file set as well as the code (see python scripts) to make images and videos that appear in the pre-print - both within the files and base modules and examples in a separate folder.</p> <p>The data can be regenerated using release 2.0, <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.1">2.1</a>, and <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.2.1">2.2.1</a>. Release 2.1 is recommended for reproducing more exactly the results for the fibrosis, invasive carcinoma, and series of leader-follower results as the results archived here were produced using a set of two random number generators (one from PhysiCell and one from BioFVM). 2.2.1, used to produce the invasive cellular front results, consolidates the use of random number generators to just one (the PhysiCell one). As such, in 2.2.1, the random number generator seed may need changed to produce stochastic replicates, even when multithreading.</p> </div> <p> </p> <div>The following file sets are in this download:</div> <ul> <li>Fig2_SM_3_simple_tests.zip <ul> <li>Has results for Figure 2 and SM Figure 3</li> </ul> </li> <li>Fig3_fibrosis.zip <ul> <li>Results from fibrosis simulation (originally Figure 3, now Figure 4)</li> </ul> </li> <li>Fig4_invasive_carcinoma.zip <ul> <li>Results from invasive carcinoma simulation (originally Figure 4, now Figure 5)</li> </ul> </li> <li>Fig5_collective_migration_initial_tests.zip <ul> <li>Results from initial leader follower model development (originally Figure 5, now Figure 6)</li> </ul> </li> <li>Fig6_instant_remodeling.zip <ul> <li>Results from instant remodeling leader follower model scenariods (originally Figure 6, now Figure 7)</li> </ul> </li> <li>Fig7b_leader_follower.zip <ul> <li>Results from leader-follower collective migration scenario (origianlly Figure 7b, now Figure 8b)</li> </ul> </li> <li>images_and_vidoes_for_paper.zip <ul> <li>has all the images, videos, and some figures from the main body of the paper and the supplmental material. Updated in this version.</li> </ul> </li> <li>Invasive_cellular_front.zip <ul> <li>Has results for each ECM scenario: random, parallel, and perpindicular orientations and mixed ECM conditions. New to this data repository.</li> </ul> </li> <li>python_imaging.zip <ul> <li>Base modules and examples for producing images (tested on Python 3.9). Updated in in this verison</li> </ul> </li> <li>SM_Fig_4b_leader_follower_decreased_remodeling.zip</li> <li>stochastic_replicates.zip <ul> <li>Contains stochastic replicates for the collective migration, fibrosis, and invasive carcinoma models and source code for all.</li> </ul> </li> <li>stochastic_replicates_invasive_cellular_front.zip <ul> <li>Contains stochastic replicates for the invasive cellular front scenarios. New to this data repository.</li> </ul> </li> </ul>
Simulation output of the reference setup in "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" contains the simulation output of the 20 reference runs. Additional data is available upon request from the corresponding author.</p>
Supporting Dataset for the study "Influence of Floodplains and Groundwater Dynamics on the Present-Day Climate simulated by the CNRM Model"
<p>This archive contains the data used in the study "Influence of Floodplains and Groundwater Dynamics on the Present-Day Climate simulated by the CNRM Model" submitted to <a href="https://www.earth-system-dynamics.net/">Earth System Dynamics</a>.</p> <p> </p>
Data for An Integrative Data-driven Model Simulating C. elegans Brain, Body and Environment Interactions
Open the record for dataset details and reuse information.
Data of 3D PPMLR-MHD model Simulation for manuscript "Formation and Evolution of Nightside Transpolar arc and Its Relationship with Energetic Plasma in the Magnetotail Lobe"
<p><span>Data of 3D PPMLR-MHD model Simulation for manuscript "Formation and Evolution of Nightside Transpolar arc and Its Relationship with Energetic Plasma in the Magnetotail Lobe"</span></p> <p><span>These data come from a fully run of a 3D MHD Simulation model that is named PPMLR-MHD model (detailed descriptions below).</span></p> <p><span>There are 2 types of data files:</span></p> <p><span>1) X15dXXXX.mat is saved simulation parameters. </span></p> <p><span>2) Xing15XXXX_heatflux.mat is saved heat flux from simulation parameters. </span></p> <p><span>XXXX is the number of files, and files with the same serial number correspond to the same time.</span></p> <p><span> </span></p> <p><span>The first type files of data include the following parameters:</span></p> <p><span>time, x, y, z, logrho, Vx, Vy, Vz, Bx, By, Bz, Pr, Jx, Jy, Jz</span></p> <p><span>Where, time is simulation time, which need to plus the start time to transfer them to universal time: time+16:00.</span></p> <p><span> (x,y,z) are the three components of the position of simulation point in GSM coordinates;</span></p> <p><span> logrho is the plasma density at the simulation point;</span></p> <p><span> (Vx, Vy,Vz) are the three components of plasma velocity at the simulation point in GSM coordinates;</span></p> <p><span> (Bx, By,Bz) are the three components of magnetic field at the simulation point in GSM coordinates;</span></p> <p><span> Pr is the plasma dynamic presure at the simulation point;</span></p> <p><span> (Jx, Jy,Jz) are the three components of plasma electric current at the simulation point in GSM coordinates;</span></p> <p><span> </span></p> <p><span>The second type file of data includes the simulated heat flux along the magnetic field lines at the simulation point in GSM coordinates. </span></p> <p><span>PPMLR-MHD model</span></p> <p><span>The PPMLR-MHD model is on the basis of an extension of the piecewise parabolic method (1) with a Lagrangian remap to magnetohydrodynamics (MHD) (2, 3). It is a three-dimensional MHD model, designed specially for the solar wind–magnetosphere–ionosphere system (4-6). The model possesses a high resolution in capturing MHD shocks and discontinuities and a low numerical dissipation in examining possible instabilities inherent in the system (4).</span></p> <p><span>The model uses a Cartesian coordinate system with the Earth’s center at the origin and X, Y, and Z axes pointing towards the Sun, the dawn-dusk direction, and the north, respectively. The size of the numerical box extends from 25 RE to –100 RE along the Sun-Earth line and from –50 RE to 50 RE in Y and Z directions, with 240×240×240 grid points and a minimum grid spacing of 0.2 RE. An inner boundary of radius 3 RE is set for the magnetosphere to avoid the complexities associated with the plasmasphere and large MHD characteristic velocity from the strong magnetic field (6). An electrostatic ionosphere shell with height-integrated conductance is imbedded, allowing an electrostatic coupling process introduced between the ionosphere and the magnetospheric inner boundary. The Earth’s magnetic field is approximated by a dipole field with a dipole moment of 8.06×1022 A/m in magnitude. The model is run to solve the whole system by inputting the real interplanetary conditions for the current event.</span></p>
Supporting Data for "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change"
<p>Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of the revised submission of Timothy M. Merlis, Ilai Guendelman, Kai-Yuan Cheng, Lucas Harris, Yan-Ting Chen, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, and Stephan Fueglistaler (2024): "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change".</p> <p> </p>
High quality figures of "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the figures for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations" in their original resolution, ensuring clarity the high-quality visual representations for readers.</p>
Air parcel trajectories data generated by MIMICA code and simulation results generated by a trajectory box model
<p>The dataset includes trajectories of air parcels extracted from the large-eddy (cloud-resolving model) simulations of the deep convective clouds from the Amazon based on the soundings retrieved on April 8, 2020, April 23, 2020, and April 27, 2020, over Manaus, Brazil, as well as the results of the chemical box model simulations quantifying the transport of some atmospheric trace gases abundant in the Amazon.</p>
Datasets for the comparison between POC estimated from BGC-Argo floats and PISCES model simulations
<p>This dataset is composed of two main folders.</p> <p><strong>clim_3D</strong>: contains 4 files in 3 directories:</p> <ul> <li>BGC-Argo/bbp700_nemo_clim.nc: global monthly climatology of BGC-Argo b<sub>bp700</sub> measurements on the ORCA2_L31 NEMO grid.</li> <li>BGC-Argo/bbp700_nemo_climseas.nc: seasonal climatology (JFM, ...OND) of BGC-Argo b<sub>bp700</sub> measurements on the ORCA2_L31 NEMO grid.</li> <li>biomes/nemobiomes.nc: biomes of Fay and McKinley 2014 (ESSD) reprojected onto the ORCA2_L31 NEMO grid.</li> <li>PISCES/PISCES_1m_19600101_19601231_ptrc_T.nc: PISCES tracers (living organisms and detrial organic carbon), monthly climatology based on pre-industrial simulation described by Aumont et al. 2017 (Biogeosciences).</li> </ul> <p><strong>CATS_1D:</strong> contains 66 folders. The folders are named as <fwmo>_<yyyy>_<orca1cell>, where</p> <ul> <li>fwmo = World Meteorological Organization (WMO) float number</li> <li>yyyy = year</li> <li>orca1cell = number of the NEMO model horizontal grid cell (ORCA1 grid) used to run the PISCES 1D offline simulation.</li> </ul> <p>Each folder contains:</p> <ul> <li>BGC-Argo observations for a given Argo float and year, bined onto the model vertical grid and 5-day peiods: 'Mprof*.nc'</li> <li>Dynamical fields used to run PISCES 1D offline simulations: 'dyna_grid_T.nc'</li> <li>Output of the simulations: PISCES tracers at 5-day resolution: 'PISCES_5d_*_ptrc_T.nc'</li> <li>List of horizontal model grid cells that were visited by the Argo float on a given year: 'xymatch_<fwmo>.csv'. If the list is queried for the grid cell number <orca1cell>, one can obtain the corresponding longitude (xmap), latitude (ymap), and the x and y indices of the grid (plus/minus 1). These indices correspond to the 3x3 horizontal grid of the dynamical fields and the PISCES 1D output.</li> </ul> <p> </p> <ul> </ul>
Identifying contributors to PM2.5 simulation biases of chemical transport model using fully connected neural networks
<p>The processed data and codes in the study are included. </p> <p><strong>Source data:</strong></p> <p>The training and testing dataset is composed of observed and simulated data of pollutants and meteorology in the BTH and YRD regions in the whole year of 2015. The processed datasets used for training are named as "dataset_BTH" and "dataset_YRD" in the folder.</p> <ul> <li><em>The hourly observed pollution data</em> are from China National Urban Air Quality Real-time Release Platform of the National Environmental Monitoring Station</li> <li><em>The hourly simulated pollutants data</em> comes from the output of WRF-CMAQv5.2 (spatial resolution of 27 km).</li> <li><em>Meteorological observation data</em> is provided by China Meteorological Data Service Centre</li> <li><em>The meteorological simulation data</em> comes from the simulation results of the WRF model</li> </ul> <p><strong>Codes:</strong></p> <ul> <li>preprocessing of raw CMAQ data, observed pollution data and meteorological data</li> <li>bulid and train process of fully connected neural networks</li> <li>calculation of correlation between variables</li> <li>feature selection method</li> <li>contribution analysis</li> </ul>
Simulation results for "Modeling the joint effects of vegetation characteristics and soil properties on ecosystem dynamics in a Panama tropical forest"
<p>This dataset is the ELM-FATES simulation outputs for the paper entitled "Modeling the joint effects of vegetation characteristics and soil properties on ecosystem dynamics in a Panama tropical forest". </p>
In Vitro Model for Simulating Drug Delivery during Balloon-Occluded Transarterial Chemoembolization - Supplementary Material
<p>Suplementary Material related with the under-review paper titled: In Vitro Model for Simulating Drug Delivery during Balloon-Occluded Transarterial Chemoembolization.</p>
ED2-hydro model output from simulations at Barro Colorado Island
<p>Monthly model output from the Ecosystem Demography model version 2 with hydrodynamics (ED2; Medvigy et al., 2009; Powell et al., 2018) run at Barro Colorado Island (BCI). ED2 was initialized from bare ground at BCI and run with recycled 2008-2014 observed meteorology (i.e. “BASE”) until predictions of above ground biomass (AGB) reached dynamic equilibrium after 700 years (Powell et al., 2018; referred to as simulation year 0 in the simulations presented here). The four data sets provided here all start after the spin up period and each provide a 50 year (monthly time step) time series of selected ED2 history variables under a particular precipitation scenario: 1) a synthetic El Niño time series (Powell <em>et al.</em>, 2017) which produces two exceptionally strong El Niño events within 30 years (the “ENSO” scenario), 2) a “WET” scenario where precipitation increased 30% compared to BASE precipitation, and 3) a “DRY-DS” scenario where dry season (January–April) precipitation was reduced 75% compared to BASE and 4) a continuation of BASE meteorology.</p> <p><strong><strong>Medvigy D</strong>, <strong>Wofsy SC</strong>, <strong>Munger JW</strong>, <strong>Hollinger DY</strong>, <strong>Moorcroft PR</strong></strong>. <strong>2009</strong>. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. <em>Journal of Geophysical Research: Biogeosciences</em> <strong>114</strong>: 1–21.</p> <p><strong><strong><strong>Powell TL</strong>, <strong>Koven CD</strong>, <strong>Johnson DJ</strong>, <strong>Faybishenko B</strong>, <strong>Fisher RA</strong>, <strong>Knox RG</strong>, <strong>McDowell NG</strong>, <strong>Condit R</strong>, <strong>Hubbell SP</strong>, <strong>Wright SJ</strong>, <em>et al.</em></strong></strong> <strong>2018</strong>. Variation in hydroclimate sustains tropical forest biomass and promotes functional diversity. <em>New Phytologist</em> <strong>219</strong>: 932–946.</p> <p><strong><strong>Powell T</strong>, <strong>Kueppers L</strong>, <strong>Paton S</strong></strong>. <strong>2017</strong>. Seven years (2008-2014) of meteorological observations plus a synthetic El Nino drought for BCI Panama.</p> <p> </p> <p> </p>
Generative models for hadron shower simulation in fundamental physics
<p>This is a subset of data used in our NeurIPS 2021 submission paper.</p>
Physics‐Based Narrowband Optical Parameters for Snow Albedo Simulation in Climate Models
<p>This is a supplementary file for a submitted paper"Physics-based effective broadband optical parameters for snow albedo simulation in climate models".</p> <p>The authors derived a set of snow optical properties that effective in broadband snow radiative transfer simulation. These parameters are physically-based. </p>
Advanced Macroeconomics: An Easy Guide - Simulating a DSGE Model Video Tutorial
<p>Can Soylu talks you over Appendix C on how to estimate DSGE models.</p> <p>This video was produced to accompany <a href="https://doi.org/10.31389/lsepress.ame">Advanced Macro-Economics: an Easy Guide</a>, available open access from LSE Press. </p> <p>Macroeconomic concepts and theories are among the most valuable for policy makers. Yet up to now, there has been a wide gap between undergraduate courses and the professional level at which macroeconomic policy is practiced. In addition, PhD-level textbooks rarely address the needs of a policy audience. So advanced macroeconomics has not been easily accessible to current and aspiring practitioners.</p> <p>This rigorous yet accessible book fills that gap. It was born as a Masters course that each of the authors taught for many years at Harvard’s Kennedy School of Government. And it draws on the authors’ own extensive practical experience as macroeconomic policymakers. It introduces the tools of dynamic optimization in the context of economic growth, and then applies them to policy questions ranging from pensions, consumption, investment and finance, to the most recent developments in fiscal and monetary policy.</p> <p>Written with a light touch, yet thoroughly explaining current theory and its application to policy-making, <em>Advanced Macroeconomics: An Easy Guide</em> is an invaluable resource for graduate students, advanced undergraduate students, and practitioners.</p>
Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years
<p>Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years</p>
model output of Cumulus convection over land in cloud-resolving simulations with a coupled ray tracer
<p><strong>768_*</strong>: Output of cloud resolving simulations of a diurnal cycle (Augst 15th, 2016) with shallow cumulus clouds over Cabauw, the Netherlands. All simulations are performed with MicroHH, with radiative transfer computed either with a two-stream approximation or with ray tracing. </p> <ul> <li>2s_het: main simulation with two-stream solver</li> <li>2s_hom_sw: two-stream solver, horizontally averaged surface solar radiative fluxes</li> <li>2s_hom_hrsw: two-stream solver, horizontally averaged heating rates</li> <li>rt_het: main simulation with ray tracing, 256 samples per pixel </li> <li>rt_hom_sw: ray tracing, horizontally averaged surface solar radiative fluxes, 256 samples per pixel </li> <li>rt_hom_hrsw: ray tracing, horizontally averaged heating rates, 256 samples per pixel </li> <li>rt_het_128: ray tracing, 128 samples per pixel</li> <li>rt_het:_64 ray tracing, 64 samples per pixel</li> <li>rt_het:_32 ray tracing, 32 samples per pixel</li> </ul>
Dataset for Liu et al. (2022), "Convection and Clouds under Different Planetary Gravities Simulated by a Small-domain Cloud-resolving Model"
<p>Dataset for Liu et al. (2022), "Convection and Clouds under Different Planetary Gravities Simulated by a Small-domain Cloud-resolving Model".</p> <p> </p>
Data for: Temperature and hygrometry of amphibian agar models in behavioral simulation and operational temperature of two forested areas
<p>We investigated how thermoregulatory behaviors affect hydro-thermoregulation in anurans, using agar models as a sampling unit, simulating four behaviors related to behavioral fever and sickness behavior. We collected data in two forest environments (Wet forest and transitional forest) in the Parque Estadual Intervales (PEI), an Integral Conservation Unit of the Atlantic Forest (24°12' - 24°25' S; 48°03 - 48°30' W). The Wet forest is a mature Atlantic Forest, and the Transitional forest is a young secondary forest adjacent to open areas. We measured operational temperatures (temperatures of inanimate objects comparable to real frog species in size and shape) of agar models across 8 replicates in the two forest environments. We also measured agar models' temperature and water loss in different behavior simulations. In each transect, we used eight sampling unit (called tetrad) that was composed of two sets of four sensor-fit agar models. One of the sets was used to collect the operational temperature of the forest areas. These agar models were fitted with a 170 cm HOBO® Data Logger (U12-008) sensor programmed to record the temperature every 15 min. The second set of tetrads was used to collect the agar models' body temperature and water loss in different behavior simulations. The behavioral simulations were defined as: (a) strong behavioral fever (SBF); (b) apathy behavior (AB); (c) single thermoregulatory event (STE); and (d) control model (CO).</p> <p>After the temperature data were collected at 0600 h, three of the agar models (SBF, AB, and STE) were moved immediately, each one according to their corresponding protocol (SBF: Warmest Neighboring Site, AB: closest shelter, mainly small burrows, or accumulations of leaf litter; STE: Alternative Warmest Neighboring site). The selection of the places was made by using a FLIR TG165 Thermal Imaging Thermometer. One hour later, at about 0700 h, and hereafter hourly, a similar procedure was repeated, but only the SBF model required movement. We placed each model within 5 cm of another in this set to ensure similar initial thermal conditions. These tetrads were left undisturbed overnight, and no manipulation occurred after 2000 h. On the next day, at 0600 h, we measured the surface temperatures of the models and immediately applied the corresponding behavioral rule. This procedure was performed hourly until 2000 h. To analyze water loss, we recorded the mass of each model at 0600 h just after measuring temperature and repeated this every two hours. We used a portable balance (A&D Newton EJ-123, 0.01g accuracy) and calculated water loss rates from the difference between the initial model mass and mass measured at each subsequent 2-hour period. We express water loss as a percentage of maximum hydration.</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.