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1,028 results for “simulation model”
Random models for CASCADE 2.0 topology and Drabme simulation results
<p>For the investigation/analysis based on this dataset, see: <a href="https://druglogics.github.io/gitsbe-model-analysis/cascade/random-model-ss/main.html">https://druglogics.github.io/gitsbe-model-analysis/cascade/random-model-ss/main.html</a></p>
Simulating and Evaluating the Global Aerosol Distributions with the Online Aerosol Coupled CAS-FGOALS Model
<p>We implement an existing aerosol module named Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) in the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System (CAS-FGOALS) model and simulate the global aerosol properties over 2002-2014. The CAS FGOALS modeling outputs associated with the work are stored here.</p>
Simulations of DPPC/DOPC/CHOL 2/1/1 mixture using the model by Carpenter et al., Part 3
<p>Simulations of 2/1/1 mixture of DPPC/DOPC/CHOL with the refined Martini model by Carpenter et al. </p> <p>Simulations are performed using a time step of 35 fs for 15 µs. The files are named 211_XX.YYY, where XX is either "sep" for simulations in which all lipid types are coupled separately to the thermostat, or "lincs", where different values for lincs_order (8 instead of standard 4) and lincs_iter (2 intead of standard 1) are used. YYY are GROMACS-compatible file formats: xtc for trajectory, edr for energy outputs, cpt for checkpoint file, tpr for run input file, and gro for final structure at the end of simulation. Additionally, files with _trr are included, which are 100 ns-long extensions to the aforementioned files. For them, a trr trajectory with velocities and coordinates is provided.</p> <p>The topology (topol.top) and index file (index.ndx) are common for all simulations, and the TOP.tar contains all the force field parameters (itp). The run input parameters (mdp) are provided for each system.</p>
Simulations of DPPC/DOPC/CHOL 3/3/2 mixture using the model by Carpenter et al., Part 1
<p>Simulations of 3/3/2 mixture of DPPC/DOPC/CHOL with the refined Martini model by Carpenter et al. </p> <p>Simulations are performed using time steps ranging from 10 to 40 fs for 15 µs (30 µs for 35 fs time step). Additional 100 ns-long simulations, during which velocities are stored in the trr trajectory are available in XXX. The files are named 332_XX.YYY, where XX is the time step in fs and YYY are GROMACS-compatible file formats: xtc for trajectory, edr for energy outputs, tpr for run input file, and gro for final structure at the end of simulation.</p> <p>The topology (topol.top) and index file (index.ndx) are common for all simulations, and the TOP.tar contains all the force field parameters (itp). The run input parameters (mdp) are provided for each system as mdXXfs.mdp, where XX is the time step.</p>
Simulations of DPPC/DOPC/CHOL 3/3/2 mixture using the model by Carpenter et al., Part 2
<p>Simulations of 3/3/2 mixture of DPPC/DOPC/CHOL with the refined Martini model by Carpenter et al. </p> <p>Simulations are performed using time steps ranging from 10 to 40 fs for 100 ns, during which velocities are stored in the trr trajectory. These simulations are initiated from the final structures of the 15 µs simulations available here:XXX. The files are named 332_XX_trr.YYY, where XX is the time step in fs and YYY are GROMACS-compatible file formats: trr for trajectory, edr for energy outputs, cpt for checkpoint file, tpr for run input file, and gro for final structure at the end of simulation.</p> <p>The topology (topol.top) and index file (index.ndx) are common for all simulations, and the TOP.tar contains all the force field parameters (itp). The run input parameters (mdp) are provided for each system as mdXXfs_trr.mdp, where XX is the time step.</p>
Simulations of DPPC/DOPC/CHOL 3/3/2 mixture using the model by Carpenter et al., Part 2
<p>Simulations of 2/2/1 mixture of DPPC/DOPC/CHOL with the refined Martini model by Carpenter et al. </p> <p>Simulations are performed using time steps ranging from 10 to 40 fs for 100 ns, during which velocities are stored in the trr trajectory. These simulations are initiated from the final structures of the 15 µs simulations available here:XXX. The files are named 211_XX_trr.YYY, where XX is the time step in fs and YYY are GROMACS-compatible file formats: trr for trajectory, edr for energy outputs, cpt for checkpoint file, tpr for run input file, and gro for final structure at the end of simulation.</p> <p>The topology (topol.top) and index file (index.ndx) are common for all simulations, and the TOP.tar contains all the force field parameters (itp). The run input parameters (mdp) are provided for each system as mdXXfs_trr.mdp, where XX is the time step.</p>
Simulations of DPPC/DOPC/CHOL 3/3/2 mixture using the model by Carpenter et al., Part 3
<p>Simulations of 3/3/2 mixture of DPPC/DOPC/CHOL with the refined Martini model by Carpenter et al. </p> <p>Simulations are performed using a time step of 35 fs for 15 µs. The files are named 332_XX.YYY, where XX is either "sep" for simulations in which all lipid types are coupled separately to the thermostat, or "lincs", where different values for lincs_order (8 instead of standard 4) and lincs_iter (2 intead of standard 1) are used. YYY are GROMACS-compatible file formats: xtc for trajectory, edr for energy outputs, cpt for checkpoint file, tpr for run input file, and gro for final structure at the end of simulation. Additionally, files with _trr are included, which are 100 ns-long extensions to the aforementioned files. For them, a trr trajectory with velocities and coordinates is provided.</p> <p>The topology (topol.top) and index file (index.ndx) are common for all simulations, and the TOP.tar contains all the force field parameters (itp). The run input parameters (mdp) are provided for each system.</p>
Simulations of DPPC/DOPC/CHOL 2/1/1 mixture using the model by Carpenter et al., Part 1
<p>Simulations of 2/1/1 mixture of DPPC/DOPC/CHOL with the refined Martini model by Carpenter et al. </p> <p>Simulations are performed using time steps ranging from 10 to 40 fs for 15 µs (30 µs for 35 fs time step). Additional 100 ns-long simulations, during which velocities are stored in the trr trajectory are available in XXX. The files are named 332_XX.YYY, where XX is the time step in fs and YYY are GROMACS-compatible file formats: xtc for trajectory, edr for energy outputs, tpr for run input file, and gro for final structure at the end of simulation.</p> <p>The topology (topol.top) and index file (index.ndx) are common for all simulations, and the TOP.tar contains all the force field parameters (itp). The run input parameters (mdp) are provided for each system as mdXXfs.mdp, where XX is the time step.</p>
Supporting datasets used in the paper entitled "Black carbon absorption efficiency under preindustrial and present-day conditions simulated by a size- and mixing-state-resolved global aerosol model"
<p>This archive contains datasets used in the paper entitled "Black carbon absorption efficiency under preindustrial and present-day conditions simulated by a size- and mixing-state-resolved global aerosol model".</p>
Pore-scale electrical numerical simulation and new saturation model of fractured tight sandstone
<p>Fractured tight sandstone reservoirs are characterized by low matrix porosity, low matrix permeability, and strong heterogeneity. Fractures developed in rocks improves reservoir permeability, but it brings new challenges to formation evaluation from log interpretation. The conventional Archie’s equation is not applicable. Meanwhile, the fragility of fractured rocks limits the application of petrophysical experiment method which is expensive and time-consuming. Recently, the development of digital rock technology provides a new means for studying fractured rocks. In this work, a set of fractured digital rocks with different fracture openings and dips are constructed to study electrical properties of fractured rocks using finite element method (FEM). The formation factor <em>F</em> is calculated by the resistivity simulation results of digital rocks with different porosity and an equation of cementation exponent versus fracture opening and dip is established. Then the resistivity of partially saturated rocks is simulated by FEM and the resistivity index is calculated to build the cross-plot of resistivity index (<em>RI</em>) and water saturation (<em>S</em><sub>w</sub>). The relationship between the <em>RI</em> and <em>S</em><sub>w</sub> for fractured rocks is exponential. Subsequently, a new saturation model for fractured tight rock reservoirs is proposed. Taken the deep Cretaceous fractured tight sandstone gas reservoir in Kuqa as an example, the model is proved more effective and correct. This study is of great significance in the theoretical analysis and saturation evaluation of fractured tight rock reservoirs.</p>
DATA for Land Surface Model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v.3.9 model over North America
<p>Code and daily temperature and precipitation outputs used to estimate climate extreme indices in García-García et al. 2020.</p> <p> </p> <p>García-García A., Cuesta-Valero F.J., Beltrami H., González-Rouco J.F., García-Bustamante E., and Finnis J. "Land Surface Model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v.3.9 model over North America" submitted to Geocientific Model Development. 2020. </p>
Integration of Activity Specification into DEVS Modeling & Simulation Development Environment
<p>We propose an integrative environment for the modeling and simulation of activity specification. The devised approach relies on the DEVS (Discrete Event System Specification) formalism for the foundational semantics of the essential activity elements. The code generation takes place afterward, targeting specific DEVS-compliant modeling and simulation (M&S) environments such as DEVS-Suite and MS4 Me. The modelers can set parameters or modify the code to satisfy specific needs. The simulation can then be conducted with behavior monitoring and visualization. We demonstrate the approach with observations about performance evaluation and tracking. Such environments have the potential to facilitate computational model development for System of Systems via full-scale simulation support.</p>
Configurations and scripts to reproduce the numerical simulations of "A two-fluid model for immersed granular avalanches with dilatancy effects" article
<p>This directory contains the main data to reproduce the results presented in the article "A two-fluid model for immersed granular avalanches with dilatancy effects" by Eduard Puig Montellà, Julien Chauchat, Bruno Chareyre, Cyrille Bonamy and Tian-Jian Hsu.<br> <br> The numerical results and experimental data extracted from Pailha et ad (2008) can be found inside "NumericalData" and "ExperimentalData" folders respectively. The script "PressureVelocityPlot.py" displays the evolution of the surface particle velocity and the excess of pore pressure with time for cases ranging from loose to dense granular avalanches. </p> <p>The input files needed to reproduce a dense granular avalanche (phi=0.592) in 1D and 2D are found in the following folders: "1D_DenseCase" and "2D_DenseCase". To accelerate the simulation, the files are given after 200 seconds of sedimentation in order to reach an equilibrium state. Please read the corresponding README.txt files to launch a 1D and/or a 2D simulation. Additionally, python scripts in each configuration are provided to evaluate the evolution of the main parameters during the avalanche. </p>
COAWST model simulations of warm and cool nearshore rip-current plumes
<p>This archive contains COAWST model input, grids and initial conditions, and output used to produce the results in a submitted manuscript. The files are:</p> <p>model_input.zip: input files for simulations presented in this paper<br> ocean_rip_current.in: ROMS ocean model input file<br> swan_rip_current.in: SWAN wave model input file (example with Hs=1m)<br> coupling_rip_current.in: model coupling file<br> rip_current.h: model header file<br> <br> model_grids_forcing.zip: bathymetry and initial condition files<br> hbeach_grid_isbathy_2m.nc: ROMS bathymetry input file<br> hbeach_grid_isbathy_2m.bot: SWAN bathymetry input file<br> hbeach_grid_isbathy_2m.grd: SWAN grid input file<br> hbeach_init_isbathy_14_18_17.nc: Initial temperature, cool surf zone dT=-1C case<br> hbeach_init_isbathy_14_18_19.nc: Initial temperature, warm surf zone dT=+1C case<br> hbeach_init_isbathy_14_18_16.nc: Initial temperature, cool surf zone dT=-2C case<br> hbeach_init_isbathy_14_18_20.nc: Initial temperature, warm surf zone dT=+2C case<br> hbeach_init_isbathy_14_18_17p5.nc: Initial temperature, cool surf zone dT=-0.5C case<br> hbeach_init_isbathy_14_18_18p5.nc: Initial temperature, warm surf zone dT=+0.5C case</p> <p>model_output files: model output used to produce the figures<br> netcdf files, zipped<br> variables included:<br> x_rho (cross-shore coordinate, m)<br> y_rho (alongshore coordinate, m)<br> z_rho (vertical coordinate, m)<br> ocean_time (time since initialization, s, output every 5 mins)<br> h (bathymetry, m)<br> temp (temperature, Celsius)<br> dye_02 (surfzone-released dye)<br> Hwave (wave height, m)<br> Dissip_break (wave dissipation W/m2) <br> ubar (cross-shore depth-average velocity, m/s, interpolated to rho-points)<br> Case_141817.nc: cool surf zone dT=-1C Hs=1m<br> Case_141819.nc: warm surf zone dT=+1C Hs=1m<br> Case_141816.nc: cool surf zone dT=-2C Hs=1m<br> Case_141820.nc: warm surf zone dT=-2C Hs=1m<br> Case_141817p5.nc: cool surf zone dT=-0.5C Hs=1m<br> Case_141818p5.nc: warm surf zone dT=+0.5C Hs=1m<br> Case_141817_Hp5.nc: cool surf zone dT=-1C Hs=0.5m<br> Case_141819_Hp5.nc: warm surf zone dT=+1C Hs=0.5m<br> Case_141817_Hp75.nc: cool surf zone dT=-1C Hs=0.75m<br> Case_141819_Hp75.nc: warm surf zone dT=+1C Hs=0.75m</p> <p>COAWST is an open source code and can be download at https://coawstmodel-trac.sourcerepo.com/coawstmodel_COAWST/. Descriptions of the input and output files can be found in the manual distributed with the model code and in the glossary at the end of the ocean.in file.</p> <p>Corresponding author: Melissa Moulton, mmoulton@uw.edu</p>
Monte-Carlo simulation of dike stability based on a coupled hydro-stability model
<p>With a large network of dikes that in the future will protect up to 15% of the worlds population from flooding, more extreme river discharges that result from climate change will dramatically increase the flood risk of these protected societies. Precise calculations of dike stability under adverse loading conditions will become increasingly important, though the hydrological impacts on dike stability, particularly the effects of groundwater flow, are often oversimplified in stability calculations. In order to better take account of groundwater flow processes, we use a coupled hydro-stability model to indicate relations between the geometry, subsurface materials, groundwater hydrology and stability of a dike. To calculate the stability, the most adverse drained loading conditions are applied to soil slip and basal sliding mechanisms. A database created by an extensive Monte Carlo analysis provides evidence for relations between geometry, material characteristics, hydrology and stability for three different failure processes. The database contains parameter combinations and the corresponding safety factor F. The database can be used to estimate failure probabilities for dike stretches that have not been assessed in detail, while including the uncertainties caused by a lack of in-situ data. </p>
Computational Modelling and Experimental Characterisation of the Fabrication of Tuneable Photonic Crystal pH Sensors: Supplementary Simulation Videos
<p>Simulation output videos to accompany the paper 'Computational Modelling and Experimental Characterisation of the Fabrication of Tuneable Photonic Crystal pH Sensors'.</p> <ul> <li>Film writing, with exported film image</li> <li>Film reading, at various expansions and pulse wavelengths (selected as the diffraction peak wavelengths identified in the paper)</li> </ul>
Model data for Sterzinger and Igel (2023) "Simulated Idealized Arctic Cloud Sensitivity to Above Cloud CCN Concentrations"
<p>Data for Sterzinger and Igel (2023) "Simulated Idealized Arctic Cloud Sensitivity to Above Cloud CCN Concentrations"</p> <p> </p> <p>all_data.tar.gz contains the processed horizontally-averaged data used to plot the figures in the paper.</p> <p> </p> <p>data_processing.tar.gz contains the scripts to process the 4-D data output from the model and namelists in https://doi.org/10.5281/zenodo.7991355</p>
The simulation data for the paper: Modeling the inner part of the jet in M87: confronting jet morphology with theory
<p>"mad98.prim.02740.athdf" is the simulation data of the fiducial model MAD98, "mad98low.prim.03900.athdf" is the simulation of the low resolution of MAD98.</p>
Data of "An enhanced lattice beam element model for the numerical simulation of rate-dependent self-healing in cementitious materials"
Open the record for dataset details and reuse information.
Supplementary Data: Measured values of 13 flood events in the upper watershed of Qingshan Hydrological Station and their simulation results by two models
<p>The files in this record contain measured data of 13 flood events and simulated flood processes from the XAJ and CM-XAJ models considered for publication in Water Resources Research.</p><p> </p><p>The files consist of:</p><p> </p><p>Measured values for 13 flood events of Qingshan Hydrological Station;</p><p>Source code (incomplete) and results of the XAJ Model;</p><p>Source code (incomplete) and results of the CM-XAJ Model;</p><p>Source code of Genetic Algorithm with recourse 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.