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1,028 results for “simulation model”
Simulation Data from STORMI and SWMF Models for the May 2024 Geomagnetic Storm Analysis
<p>Simulation Data from STORMI and SWMF Models for the May 2024 Geomagnetic Storm Analysis.</p>
Data from: Numerical modelling of bridges in 2D shallow water flow simulations
<p>This repository includes the experimental dataset colleted in the Hydraulics Laboratory of the University of Zaragoza in 2014.</p> <p>The experiments were carried out with different bridge configurations in a straight flume for both steady and transient flow regimes.</p> <p>The data were published originally in:</p> <p>Ratia, H., Murillo, J. and García-Navarro, P. (2014), Numerical modelling of bridges in 2D shallow water flow simulations. Int. J. Numer. Meth. Fluids 75, pp. 250-272. <a href="https://doi.org/10.1002/fld.3892">https://doi.org/10.1002/fld.3892</a></p> <p>This dataset has been made available online to the research community thanks to the support of the project PID2022-137334NB-I00 funded by MCIN/AEI/10.13039/<br>501100011033 and by “ERDF/EU”.</p>
Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean
<p>Output files for Variable-Resolution Community Earth System Model (VR-CESM) simulations with highest resolutions over the Euro-Mediterranean and notebooks created for analyses and visualization.</p> <p>Configuration names:</p> <ul> <li>ne30_n</li> <li>ne30x4_n</li> <li>ne30x4_t</li> <li>ne30x8_t</li> </ul> <p>Variables at single level: PHIS,PRECC,PRECL,PS,TREFHT,LHFLX,SWCF,LWCF,TMQ,SNOWHLND</p> <p>Variables at pressure levels:U,V,OMEGA,RELHUM,Z3,Q</p>
Processed files used for OQ based tsunami loss modelling using HPC based inundation and emulators(Part-IV OQ Simulation and Emulation Hazard Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed OpenQuake files covering tsunami inundation depth hazard using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <div> </div> <div>The risk calculation and procedure is available in the main repo: <a href="https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk">https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk </a></div> <div> </div> <br> <div>The processed files for the test locations of Catania(CT) are provided in compressed gzip files(.gz):</div> <br> <div>Processed hazard information used to prepare and run OQ event based risk analysis are as below,</div> <div> -<strong>hazard.tar.gz </strong>- preliminary numpy files with sitcol, eventid and hazard magnitude info</div> <div> -<strong>loss.tar.gz</strong> -final hdf5 files with both event hazard and site info</div> <br> <div>The filename follows the nomenclature with:</div> </div> <div>ML4SicilyTsunami/risk/loss/tsunami_prob_892.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_1658.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_3454.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_7071.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_true.hdf5</div> <div><br> <div> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk</strong></div> <div><strong>https://github.com/naveenragur/OQ-Tsunami</strong></div> </div> </div>
Simulations for convection during the intensive observation period of the TWP-ICE field campaign: results from cloud-resolving model and convective parameterization schemes
<p>Simulated convections from cloud-resolving model and convective parameterization schemes during the intensive observation period of the TWP-ICE field campaign, which are used to investigate the scale-awareness problem of convective parameterization schemes. The corresponded observations are also included in this dataset.</p>
Paper dataset of "ARAMIS: a Martian radiative environment model built from GEANT4 simulations"
<p>Supplementary materila dataset concerning the article : <a href="https://doi.org/10.1051/swsc/2024032">https://doi.org/10.1051/swsc/2024032 .</a></p> <p>In the version v1, the proton flux where not giving for both cone and total view which is corrected here. Beside there was a wrong upload file for proton_aramis_2015_2016.dat, which has been corrected as well.</p>
Data assimilation products by using multiple climate model simulations and different combinations of proxies
<p>This dataset of the climate reconstruction by data assimilation using isotope ratios provides annual surface air temperature, precipitation amount, and other climate variables during 850–2000.</p> <p>Two isotopes-incorporated atmospheric general circulation models and 129 isotopic proxy data (65 corals, 43 ice cores, and 21 tree-ring cellulose) were used in this study. There are nine experiments using three type of simulations and three combinations of proxies.</p> <p>The associated publication: Shoji, S., Okazaki, A., & Yoshimura, K. (2020). Impact of proxies and prior estimates on data assimilation using isotope ratios for the climate reconstruction of the last millennium. (submitted to Earth and Space Science)</p> <p>[Data structure]<br> X(lon) x Y(lat) x Z(2) x Variables(8) x Year(1151)<br> Z(1): analyses<br> Z(2): priors</p>
Model simulations using a parameterization of convective organization effects
<p>We propose a parameterization scheme of convective organization effects based on a moisture-distribution approach. We implement it into a regional climate model and evaluate its performance against a convection-permitting model simulation. The related model simulations are included in this dataset.</p>
The prediction data analyzed in the article: "An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018"
<p>The outputs of seasonal predictions with the Coupled Arctic Prediction System version 1 analyzed in the article, "An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018", including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Ice mass budget diagnostics</p> <p>Accumulated downward shortwave radiation at the surface (ASWDN)</p> <p>Accumulated downward longwave radiation at the surface (ALWDN)</p> <p>Near surface air temperature (T2) </p> <p>Temperature and salinity profile of the upper ocean under sea ice </p>
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</p>
Dataset for XBeach model calibration and simulation of sample storms in an intermediate-to-dissipative, microtidal coast (Sabaudia, Italy)
<p>The archive contains XBeach input data and datasets used for the calibration of the hydrodynamic model XBeach and subsequent simulation of six sample storms at different tidal levels at the intermediate-to-dissipative, microtidal coast of Sabaudia (Tyrrhenian Sea, Italy).</p> <p>VERSION v2 (January 24, 2022): uploaded a new version of the dataset to support the revised version of the manuscript.</p>
Dataset: Large-eddy simulation of the ice shelf-ocean boundary layer model output
<p>This repository contains large-eddy simulation output from the CFD model <em>Diablo</em>. The simulations are of the boundary layer beneath a melting ice shelf. This model output underpins the submitted manuscript <em>Regimes and transitions in the basal melting of Antarctic ice shelves</em> submitted to the <em>Journal of Physical Oceanography</em> (December 2021).</p>
Source molecular simulation data for calculating energy and friction profiles and permeability coefficients through model lipid membranes
<p>Energy files from GROMACS molecular dynamics simulations with enhanced free energy sampling contain time-dependent evolution of the free energy profiles and friction profiles (and other energies and simulation properties) that were used for calculating permeability coefficients in the publication https://www.biorxiv.org/content/10.1101/2021.07.16.452599v1</p> <p>Simulation system contains a lipid POPC or DPPC bilayer with a varying amount of cholesterol (specified as mol% in the file name). Hydrophobic level of the permeating particle is specified as "level-I", "level-II" etc. When unspecified in the file name, the particle is hydrophobic level "III". Lipids D-C14-PC denote PC lipids with both tails monounsaturated of length 14 carbon atoms. DOPC is equivalent to D-C18-PC. (Detailed description in the publication)</p> <p>Adaptive Weighted Histogram (AWH) method was used to sample the free energy profile of translocating small molecule through the lipid bilayer.</p> <p>GROMACS tool `gmx awh` reads the files and provides the described profiles.</p> <p>Files were generated by GROMACS `mdrun` simulation engine version 2019.3.</p> <p> </p> <p>Coarse-grained MARTINI 3.0 model was used for modeling the biomolecular interactions.</p> <p>Scripts to perform the simulations and the files with initial configurations and simulation settings are stored in a public GitHub repository depozited on Zenodo.org: <a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p> <p> </p> <p>Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).</p> <p>Lindahl, V., Lidmar, J. & Hess, B. Accelerated weight histogram method for exploring free energy landscapes. J. Chem. Phys. 141, 044110 (2014).</p> <p>Souza, P. C. T. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18, 382–388 (2021).</p> <p>Melcr, J. Git repository with analysis scripts for MD simulations of permeability through lipid membranes. (2021) doi:<a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p>
KMC Models and CFD Simulations for Catalytic CO Oxidation over RuO2
<p>The large tar.gz file (~15GB) has the CFD Data Set for CFD Simulations for Catalytic CO Oxidation over RuO2 associated with the publication: doi: 10.1021/acscatal.8b00713 , https://pubs.acs.org/doi/abs/10.1021/acscatal.8b00713</p> <p>The smaller zip file(s) have the KMC model creation files, which are actually from this publication: https://doi.org/10.1088/1361-648X/aacb6d https://iopscience.iop.org/article/10.1088/1361-648X/aacb6d</p> <p>For the 110 KMC file, it must be compiled using the lat_int backend of kmos. The xml file generated takes less than an hour, though the exporting (compiling) may take 5 to 10 hours. With lateral interactions, the 110 xml file is >25MB. Without lateral interactions, the xml file is very small ( << 1 MB). Setting the "upToDistance" to 0 for each reaction in the python file called CO_O_RuO2_110.py will create a 110 model without lateral interactions (currently the "upToDistance" is set at 1 for each reaction, and thus includes nearest neighbor interactions). How to compile the model requires reading the documentation for the KMC software. The final syntax is " kmos export -b lat_int CO_O_RuO2_110.xml "</p> <p>For the 111 KMC file, it should be compiled with the default ( local_smart ) backend of kmos. The lateral interactions are already included and cannot be easily turned off. The resulting files are small. How to compile the model requires reading the documentation for the KMC software. The final syntax is " kmos export -b local_smart CO_O_RuO2_111.xml "</p> <p>Primitive example KMC runfiles are included, which would need to be run in the compiled KMC model's directories, though current conventions of kmos simulations use more advanced runfiles.</p> <p> </p>
uDALES 1.0: a large-eddy-simulation model for urban environments
<p>Data accompanying the GMD article 'uDALES 1.0: a large-eddy-simulation model for urban environments'</p> <p>https://doi.org/10.5194/gmd-2021-255</p> <p>1) Figures shown in the manuscript</p> <p>2) Input to rerun the presented cases in uDALES</p> <p>3) Model output and Matlab scripts to recreate the figures</p>
Glacier model simulations of moraine building forced by interannual variability in climate
<p>A set of 2,000-year simulations of moraine building by a glacier flowing through a synthetic alpine landscape forced by interannual variability in weather imposed on an otherwise stable climate. Moraine relief is shown for a standard deviation in mean annual air temperature (dT) of 0.5°C, 1.5°C, and 3.0°C around a long-term mean of 7.0°C. Simulations were made using the ice-flow model iSOSIA (Egholm et al., 2011, <em>Geomorphology</em>).</p>
A dynamic ancestral graph model and GPU-based simulation of a community based on metagenomic sampling
<p>In this paper we present an ancestral graph model of the evolution of a guild in an ecological community. The model is based on a metagenomic sampling design in that a random sample is taken at the community, as opposed the taxon, level and species are discovered by genetic sequencing. The specific implementation of the model envisions an ecological guild that was founded by colonization at some point in the past that then potentially undergoes diversification by natural selection. Within the graph, species emerge and evolve through the diversification process and their densities in the graph are dynamic and governed by both ecological drift and random genetic drift, as well as differential viability. We employ the 3% sequence divergence rule at a marker locus to identify Operational Taxonomic Units. We then explore approaches to see if there are indirect signals of the diversification process, including population genetic and ecological approaches. In terms of population genetics, we study the joint site frequency spectrum of OTUs, as well its associated statistics. In terms of ecology, we study the species (or OTU) abundance distribution. For both we observe deviations from neutrality, which indicates that there may be signals of diversifying selection in metagenomic studies under certain conditions. The model is available as a GPU-based computer program in C/C++ and using OpenCL, with the long-term goal of adding functionality iteratively to model large-scale eco-evolutionary processes for metagenomic data.</p>
In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation
<p><strong>Background</strong><br> <br> The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted). It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset.</p> <p><strong>General description / Dataset Structure</strong></p> <p>Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.</p> <p>The different stenosis degrees for each degree are:<br> ScenarioI: 100 % Area fraction (no stenosis)<br> ScenarioII: 37,5 % Area fraction<br> ScenarioIII: 23,4 % Area fraction<br> ScenarioIV: 6,56 % Area fraction</p> <p><strong>Data fields for each file</strong></p> <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table> <p> </p> <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor, unit mmHg for type 'p' and ml/s for type 'q' </td> </tr> <tr> <td>anatomicalPosition</td> <td> <p>name of the corresponding anatomical position</p> </td> </tr> </tbody> </table>
Supporting information for Hausfather et al 2022, Climate simulations: recognize the 'hot model' problem, comment in Nature
<p>This is the supporting information for the figure in Hausfather et al 2022, Climate simulations: recognize the ‘hot model’ problem, <em>Nature</em>. It includes CMIP6 ECS and TCR values, the screening used in our TCR screened assessment, as well annual global mean surface temperature anomalies relative to preindustrial (1850-1899) for the AR6 assessed warming, CMIP6 multimodel mean, and TCR screened subset shown in Figure 1 in our comment. </p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models
<p>Trained machine learning models and scaling values used in the paper "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model."</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.