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328 results for “Simulation results”
MHD simulation results for the investigation of FTEs
<p>The unit of data:</p> <p>Length unit, like x, y z, is Re(the radius of the Earth).</p> <p>The unit of magnetic field data is nT.</p> <p>The unit of velocity is km/s.</p> <p>The unit of density is cm^-3.</p> <p>The unit of pressure is nPa.</p> <p><strong>1</strong>.'no2_bn_mp_betsuni_topo_halfopen_17_r0.dat ' contains the magnetic field and some plasma data on the surface of magnetopause in full time step(201 time steps) of case 2.</p> <p>This is a binary data, and you can read it as follows"</p> <p>read,ntht,nphi (ntht,nphi are integers)</p> <p>define xmp,ymp,zmp as the two dimension array (ntht,nphi)</p> <p>read,xmp,ymp,zmp (xmp,ymp,zmp is the coordinates of points, the unit is Re) </p> <p>define bn,bl,bm,p_1,b_mag,vx_1,d_1 (these are 2 dimensional arrays (ntht, nphi))</p> <p>begin the loops for i=0,200</p> <p>read,time(this is the time , you need to multiply 0.935 to get the physical time. For example, if the value of time you read is 680, then the physical time is 680*0.935)</p> <p>read, bn,bl,bm,p_1,b_mag,vx_1,d_1 (bn,bl,bm are the magnetic field data,p_1 is pressure, vx_1 is the magnitude of velocity,d_1 is the density)</p> <p>endfor</p> <p><strong> 2</strong>. 'S05D07V750B0013-13bt_cut_large_bipolar_bt119 ' , 'S05D07V750B0013-13bt_cut_large_bipolar_bt120' and 'S05D07V750B0013-13bt_cut_large_bipolar_bt121' are the data in 3d of case2 in 119th(2225.3s),120th(2244s) and 121th(2262.7s) time step separately.</p> <p>These are binary data, you can read as follows:</p> <p>read,m,n,l (m,n,l are integers)</p> <p>define, x,y,z as 1 dimensional array, x(m), y(n), z(l)</p> <p>read,,x,y,z</p> <p>define d,vx,vy,vz,bx,by,bz,p,tracer as 3 dimensional arrays, like d(m,n,l). d is density, vx is the velocity of x axis, vy is the velocity of y direction, vz is the velocity of z axis, bx, by and bz are the magnetic field in x, y, z, p is pressure. tracer is the percentage of solar wind particles. </p> <p>read,d,vx,vy,vz,bx,by,bz,p,tracer</p> <p><strong>3</strong>.S05D07V750B0013-13bt_cut_large_bipolar_bt121.silo is a 3 dimensional data which can be opened and plotted by Visit.</p> <p><strong>4</strong>.no1_bn_mp_betsuni_topo_half_r0_178.dat and no3_bn_mp_betsuni_topo_half_1120_34.dat contains the magnetic field and some plasma data on the surface of magnetopause in only one time step of case 1 and case3.</p> <p>The time step of case1' s data is 178th, that is 3328.6s.</p> <p>The time step of case3' s data is 34th, that is 635.8s.</p> <p>These are binary data, you can read the data as follows:</p> <p>read,ntht,nphi ( these are integers)</p> <p>define xmp,ymp,zmp as 2 dimensional arrays, like xmp(ntht,nphi)</p> <p>read, xmp,ymp,zmp</p> <p>read, time( if you want to get the physical time point, you should calculate 0.935*time)</p> <p>define bn,bl,bm,p_1,b_mag,vx_1,d_1 as 2 dimensional arrays, like bn(ntht,nphi)</p> <p>read,bn,bl,bm,p_1,b_mag,vx_1,d_1</p> <p><strong>5</strong>. S05D48V300B0033-33bt_cut_large_bipolar37 , S05D07V750B0013-13bt_cut_large_bipolar_bt37 and S05D03V1200B0008-08bt_cut_large_bipolar_bt37 are the data in 3 dimension of case1 ,2 and 3 in the 37th(691.9s) time step separately.</p> <p>These data are the binary data and you can read these as the steps shown in 2.</p>
Simulation Results for CAUSE Manuscript
<p>Simulation results for evaluating CAUSE, an MR method</p> <p>https://www.biorxiv.org/content/10.1101/682237v3</p> <p>https://jean997.github.io/cause/simulations.html</p>
Model source code, simulation results, and all scripts for the paper "A comprehensive estimate of the anthropogenic aerosol radiative effects using the GAMIL model with reduced complexity".
<p>FigureTable_Scripts.rar is NCL scripts used for figures and tables. <br> Model_Scripts.rar is the modified model code and scripts used for running model.<br> models.rar is the GAMIL model source code.<br> ModelResults.rar is the model results. Note that only the variables used for making plots.</p>
Results of 16000 simulations in "Voronoids: Fast and Ordered Swarm Coordinated Motion"
<p>The (CSV) table DataSet contains the aggregate results of the 16000 following simulation cases (one per line), defined by the first ten columns:</p> <p>A) ID: # case identifier</p> <p>B) n=10,20,40,80,160 # number of robots</p> <p>C) S2M=0,0.5 # Stall-to-Maximum, 0 for differential-drive and quadrotor-like robots, 0.5 for fUAV-like robots</p> <p>D) PF=0.25,0.5,0.75,1 # Prudence Factor, max. velocity is PF*r/s (+/- 10%), where r is the communication factor, and s is one second</p> <p>E) OF=1,2 # Overpopulation Factor</p> <p>F) T=0.25,0.5,0.75,1 # high-level control period (+/- 10%)</p> <p>G) FF=1 # Feed-Forward (True or False)</p> <p>H) CC=1,3 # Connectivity Control: 0=None,1=NN,2=MST,3=IMRG,4=Voronoi</p> <p>I) scn=1,2,3,4,5 # Scenario</p> <p>J) rands=0,1,2,3,4 # random seed</p> <p> </p> <p>Two auxiliary columns are added:</p> <p>K) r # communication radius (the arena axis are [-16,16,-9,9]) - physically, r=100 m, for instance, so this parameter defines the space scale of the simulation</p> <p>L) PFxT # Prudence Factor multiplied by control period, fraction of r that, at max. velocity, the robot advances without high-level control</p> <p> </p> <p>From each simulation case, the following aggreagate results, or indices, are obtained:</p> <p> </p> <p>The principal Indices of Quality, given in the following four columns:</p> <p>M) IQA # IQ Area coverage: time-average ratio of uncovered area</p> <p>N) IQN # IQ Nearest-neighbor distance uniformity: time-average of average difference of distances to nearest neighbor wrt. average distance, divided by r</p> <p>O) IQC # IQ collisions: ratio of robots lost by collision</p> <p>P) IQX # IQ connectedness: ratio of robots lost by disconnection</p> <p> </p> <p>Additional indices are given in the following four columns:</p> <p>Q) minD # minimum distance from any robot to anything else during the whole simulation, divided by r</p> <p>R) IQV # IQ Velocity: average advance velocity of robots, divided by max. velocity (approx. PF*r/s)</p> <p>S) SpenTime # time to complete the mission, including 3 s for initial deployment</p> <p>T) SimDuration # time to complete the simulation (in a single core of our cluster)</p>
Results for datasets with simulated error (derived from Datasets 1-5)
<p>Datasets w/ simulated error derived from Datasets 1-5.</p> <p>Version 2: Datasets were calculated with an Rstandard (R_std) value of 0.0036765 (<span>Skrzypek, G. and Dunn, P. J. H., Rapid Commun. Mass Spectrom., 34, e8890, <a href="https://doi.org/10.1002/rcm.8890">https://doi.org/10.1002/rcm.8890</a>, 2020</span>), which is more accurate than the atom percent value used in Version 1. Updating the R_std value had essentially no effect on the average values calculated using these datasets.</p> <p>There are 1000 datasets per combination of error level and skewness type.</p> <ul> <li>e1: Low error, no skew</li> <li>e2: Medium error, no skew</li> <li>e3: High error, no skew</li> <li>e4: Low error, left skew</li> <li>e5: Medium error, left skew</li> <li>e6: High error, left skew</li> <li>e7: Low error, right skew</li> <li>e8: Medium error, right skew</li> <li>e9: High error, right skew</li> </ul>
Megan results, emission input datasets for CMAQ, and simulation results
<p>Megan results, emission input datasets for CMAQ, and simulation results:</p> <p>Megan results: megan_results.zip</p> <p>Emission: emission.zip</p> <p>NO2 simulation: NO2_simulation.zip</p> <p>O3 simulation: O3&MDA8_simulation.zip</p> <p>NO2 and O3 simulation station results: simulation_and_observation_results.zip</p> <p>Process results: ozone_chemical_phyisc_contribution.zip</p> <p> </p> <p> </p> <p> </p>
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>
SOCOlv4.0 simulation results for 1980-2018
<p>Some simulation results used in the model validation paper in GMD. Yearly mean, zonal mean. For more data, contact the author.</p>
actuatorCylinderSimpleFoam-simulations-results
<p>Creator: Edgar Martinez-Ojeda</p> <p>Title: actuatorCylinderSimpleFoam-simulations-results</p> <p>Description: Data from two wind farms: the small-scale wind farm made of 4 turbines and the large-scale wind farm made of 8 turbines. These are log files with printed information from the solver as well as the power coefficients.</p> <p>Date of last of access: august 11, 2021.</p>
Simulation results
This is the result of a very expensive simulation. Here is some text describing the simulation in detail, how it was generated, and how to use the dataset.<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="https://github.com/rodluger/showyourwork-sandbox/tree/6b4dccf0c7e9a59e482c8ed46061a4f60ca35705">this GitHub repo</a> using the following command: <pre><code class="language-bash">cd src/figures && python run_simulation.py</code></pre>
Alpine climate and soils heterogeneity data and simulation results
<p>We developed metrics of climatic and edaphic heterogeneity, using principal components analyses and the shoelace algorithm, and added elevation range. We applied commonality analysis to partition the unique and shared explanation of the observed vascular plant species richness among selected metrics. A simulation was developed to separate the relative importance of area and heterogeneity at different extents and representations of spatial nestedness, and the heterogeneity – effective area tradeoff was evaluated by altering spatial discreteness.</p> <p>The simulations revealed that heterogeneity was consistently more important, but less so among smaller areas. This qualitative pattern was maintained regardless of whether and how nestedness was represented. The heterogeneity – effective area tradeoff occurred in a few simulations of more discrete habitats.</p>
Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems: Simulation Result Tables
<p>The dataset contains the result tables used for the paper "Influence of Adaptive Coupling Points on Coalition Formation in Multi-Energy Systems". </p> <p>Short description of the tables:</p> <ul> <li>all_in_one_tab.csv: contains all calculated metrics and attributes of the graph over time and adaptation rate</li> <li>event_tab.csv: contains all toggle events of the coupling points</li> <li>impact_dict.csv: contains the calculated impact values for every coupling point</li> <li>node_region.csv: contains region attributes for every node over adaptation rate</li> <li>static_component_properties.csv: contains static attributes </li> <li>static_node_component_properties.csv: contains static node attributes</li> </ul>
Molecular dynamics (MD) simulations methods and results on cyclooctene oxides in ACN/water hybrid electrolytes
<p><strong>Molecular dynamics (MD) simulations methods and extended results used for the <em>Perspective</em> article entitled</strong></p> <p>“Fine tuning of electrosynthesis pathways by modulation of electrolyte solvation structure”</p>
Simulation result of the electron and nuclear polarizations during a frequency swept pulse
<p>Simulation result of the electron and nuclear polarizations during a frequency swept pulse for 1 electron spin coupling to 5 nuclear spins, using SpinEvolution software.</p>
Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during January-March 2017
<p>Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during January-March 2017 including meteorology and air quality, which are in wrfout_YYYY-MM-DD_00_00_00 file on each day during January-March 2017. The file contains these variables:</p> <p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during October-December 2017
<p>Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during October-December 2017 including meteorology and air quality, which are in wrfout_YYYY-MM-DD_00_00_00 file on each day during October-December 2017. The file contains these variables:</p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.
Simulation results from WRF-Chem without aerosol feedbacks in eastern China for April-June 2017
<p>Simulation results from WRF-Chem without aerosol feedbacks in eastern China for April-June 2017 including meteorology and air quality,which are in wrfout_YYYY-MM-DD_00_00_00 file on each day for April-June 2017. The file contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during April-June 2017
<p>Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during April-June 2017 including meteorology and air quality, which are in wrfout_YYYY-MM-DD_00_00_00 file on each day during April-June 2017. The file contains these variables:</p> <p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during July-September 2017
<p>Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during July-September 2017 including meteorology and air quality, which are in wrfout_YYYY-MM-DD_00_00_00 file on each day during July-September 2017. The file contains these variables:</p> <p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-Chem without aerosol feedbacks in eastern China for January-March 2017
<p>Simulation results from WRF-Chem without aerosol feedbacks in eastern China for January-March 2017 including meteorology and air quality,which are in wrfout_YYYY-MM-DD_00_00_00 file on each day for January-March 2017. The file contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</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.