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1,028 results for “simulation model”
Kilometre-scale regional climate model simulations of two atmospheric river case studies in West Antarctica
<p>Regional climate model simulations produced using the MetUM, HCLIM and Polar-WRF models at 1 km horizontal grid spacing. The data span two case studies in which an atmospheric river made landfall over the Amundsen Sea Embayment and Thwaites / Pine Island ice shelves. The first is a winter case (23-30 June 2020) and the second a summer case (3-9 February 2020). </p> <p>Data are gridded, in native model coordinates, and saved as netcdf.</p> <p>Data produced by:</p> <p>HCLIM: José Abraham Torres</p> <p>MetUM: Ella Gilbert</p> <p>Polar-WRF: Denys Pishniak</p> <p>Data were produced to support the analysis presented in Gilbert et al. (2024) [preprint] . The research was funded by the PolarRES project, which is funded under the EU's Horizon 2020 programme call H2020-LC-CLA-2018-2019-2020 under grant agreement 101003590. MetUM simulations were performed on the ARCHER2 UK National Supercomputer. </p>
Model simulation output for New Configuration for Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model
<p>Simulation output from the new configuration model used in the manuscript Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model</p>
'Reconciling Surface Deflections From Simulations of Global Mantle Convection' : Numerical model dataset
<p><strong>Output data from TERRA simulations included in 'Reconciling Surface Deflections From Simulations of Global Mantle Convection'</strong></p> <p>Dataset includes:</p> <ul> <li>Full density field (`NC_visc_dens_037.tar.gz`)</li> <li>Radial stresses (directory `radial_stresses`)</li> <li>Spherical harmonic coefficients for density field (`density_sph.037`)</li> <li>Radial viscosity factors (`visc.dat`)</li> </ul> <p>Radial stresses are calculated at depths of:</p> <ul> <li>0 km (surface)</li> <li>45 km</li> <li>180 km</li> <li>270 km</li> </ul> <p>and with various amounts of shallow structure removed:</p> <ul> <li>NC_DT_rmir0 - No shallow structure removed</li> <li>NC_DT_rmir1 - 45 km removed</li> <li>NC_DT_rmir2 - 90 km removed</li> <li>NC_DT_rmir3 - 135 km removed</li> <li>NC_DT_rmir5 - 225 km removed</li> <li>NC_DT_rmir7 - 270 km removed</li> </ul>
WRF Model Output for a Simulation of Hurricane Laura (2020), 2.25km grid, 2020-08-26-00Z through 2020-08-26-10Z
<p>This is WRF model output for a simulation of Hurricane Laura (2020). The tar file contains gzipped netcdf files each with 6 model outputs at 20 minute intervals.</p>
WRF Model Output for a Simulation of Hurricane Laura (2020), 2.25 km grid, 2020-08-26-12Z to 2020-08-26-22Z
<p>This is WRF model output for a simulation of Hurricane Laura (2020). The tar file contains gzipped netcdf files each with 6 model outputs at 20 minute intervals.</p>
Simulation of a POPC bilayer at 298K, lipid model by Maciejewski and Rog
<p>Simulation of a POPC bilayer containing 128 lipids with 40 water molecules per lipid and in the absence of ions at 298 K.</p> <p>The lipid model by Maciejewski and Rog [1,2] was used. Topologies (.itp) were obtained from [2]. TIP3P water was used, and the ions were modelled using default OPLS ion parameters. The simulation is 200 ns long with trajectory saved every 100 ps. Simulations were performed with Gromacs 2016.3 [3]</p> <p>The trajectory (.xtc), energy file (.edr), checkpoint file (.cpt), run input file (.tpr), index file (.ndx), topology file (.top), final structure (gro), and the simulation parameter file (.mdp) are provided. </p> <p>[1] Maciejewski et al., J. Phys Chem. B 118, 2014, pp. 4571–4581, DOI: 10.1021/jp5016627 </p> <p>[2] Kulig et al., Data in Brief 5, 2015, pp. 333–336, DOI: 10.1016/j.dib.2015.09.013</p> <p>[3] Abraham et al., SoftwareX 1–2, 2015, pp. 19–25, DOI: 10.1016/j.softx.2015.06.001</p>
Test Model for GTT Computational Fluid Dynamics Simulation by TransAT
<p>This archive contains the model and associated code need to run the GTT benchmark test for TransAT.</p>
Simulation results of the agent-based model of urban insurgence with the effect of gathering sites and Koopman mode analysis
<p>The data set contains the simulation results of the agent-based model of urban insurgence with the effect of gathering sites and Koopman mode analysis. Some details on the agent-based model (without gathering sites) can be found in [Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham, Nonlinear Dynamics of Crime and Violence in Urban Settings, Journal of Artificial Societies and Social Simulation, 15(1), 2, http://jasss.soc.surrey.ac.uk/15/1/2.html, DOI: 10.18564/jasss.1921].</p> <p>Files in folder "0bar" are related to the case with 0 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "1bar" are related to the case with 1 preferential gathering site.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and average distance from the preferential gathering site. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "2bars" are related to the case with 2 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars2.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "3bars" are related to the case with 3 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars3.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "4bars" are related to the case with 4 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars4.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "5bars" are related to the case with 5 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "10bars" are related to the case with 10 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars10.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "20bars" are related to the case with 20 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars20.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "30bars" are related to the case with 30 preferential gathering sites.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars30.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob25" are related to the case with 5 preferential gathering sites and 25% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.25.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob50" are related to the case with 5 preferential gathering sites and 50% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.50.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "prob75" are related to the case with 5 preferential gathering sites and 75% probability of agents moving towards them.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. The last value is not used. These files are provided for lattice sizes from 100x100 to 600x600 and for different random seeds.</p> <p>Each file with name starting with Day for each non-intimidated agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship, total number of days of being active and -1. Last two values are not used. If an agent is LEO, then last four values are -1. These files are provided for each time step of the simulation, each lattice size and the corresponding random seed used. For example, file "Day_10000_100_111_bars5_prob0.75.txt" provides information on the lattice situation at time step 10000 with lattice size 100 and random seed 111.</p> <p>Files in folder "KMD" have detailed information for the case with 3 preferetial gathering sites and lattice size 200x200.</p> <p>File "2016_Actives_LD200_seed111_bars3.txt" has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens.</p> <p>Each file with name starting with Day for each active / intimidated (jailed) / non-intimidated (notjailed) agent has his/her x-coordinate, y-coordinate, agent's state, agent's risk aversion, agent's hardship. Each file with name starting with Day for each LEO (cop) has his/her x-coordinate, y-coordinate, agent's state and -1, -1, -1. Last 3 entrees are not used. These files are provided for each time step of the simulation.</p>
Space Weather Modeling Framework ensemble simulations
<p><strong>Space Weather Modeling Framework ensemble simulations</strong></p> <p>This archive contains folders for 41 simulations with the operational geospace configuration of the University of Michigan's Space Weather Modeling Framework[1]. The operational configuration uses the University of Michigan's BATS-R-US magnetohydrodynamics code[2], the Ridley ionospheric electrodynamics solver[3], and the Rice Convection Model[4] inner magnetosphere model. More details of the operational geospace configuration are given by [5].</p> <p>These simulations were performed for, and used in, the paper</p> <p>> "Perturbed Input Ensemble Modeling with the Space Weather Modeling Framework",<br> > S.K. Morley, D.T. Welling and J.R. Woodroffe,<br> > Space Weather, 2018. doi: <a href="https://doi.org/10.1029/2018SW002000">10.1029/2018SW002000</a></p> <p><em>Directory Structure</em><br> All numbered directories are members of a perturbed input ensemble. The directory labeled "orig" is the reference (unperturbed) simulation. Each directory is structured identically.</p> <p>Each run directory contains `PARAM.in`, `LAYOUT.in` and `magin_GEM.dat` files. This set of files consistutes the required inputs for each run that are invariant. That is, these files are identical between runs and control the setup of the model and the types of outputs generated. Each run directory also contains an `IMF.dat` file that sets the upstream boundary condition. This file differs between each simulation. The values in each ensemble member have been perturbed from the values given in the reference simulation using a block resampling of measurement errors between an L1 solar wind monitor and a near-Earth monitor.</p> <p>Each run directory also contains `GM` and `GM\IO2` subdirectories. The `GM\IO2` subdirectory contains simulation output from the global magnetosphere module. Three files are present for each simulation: `geoindex_e20100404-190000.log`, `magnetometers_e20100404-190000.mag`, and `log_e20100404-190000.log`. These are standard SWMF log files that can be parsed and analyzed using, for example, the `pybats` module in the SpacePy[6] software package[7]. The simulation ouput includes ground magnetic perturbations at a set of magnetic observatory locations, local K indices, an estimated Kp index, a 1 minute resolution Sym-H/Dst index equivalent and simulated auroral electrojet indices.</p> <p><br> <em>Basic Analysis</em><br> To derive the time derivative of the horizontal ground magnetic perturbation (dB/dt) the magnetometer log file can be loaded using SpacePy<br> </p> <pre><code class="language-python">>>> import spacepy.pybats.bats >>> magdata = spacepy.pybats.bats.MagFile('run_001/GM/IO2/magnetometers_e20100404-190000.mag') >>> magdata.calc_h() #calculates horizontal from North and East components >>> magdata.calc_dbdt() #calculates time derivatives</code></pre> <p>To then calculate binned maxima in the dB/dt time series, e.g., for the Yellowknife (YKC) station<br> </p> <pre><code class="language-python">>>> import datetime as dt >>> import numpy as np >>> import spacepy.toolbox as tb >>> dBdt_max20, bintimes = tb.windowMean(magdata['YKC'], time=subset['time'], winsize=dt.timedelta(minutes=20), overlap=dt.timedelta(0), st_time=dt.datetime(2010,4,5), op=np.max)</code></pre> <p><br> and to turn this into a binary event series indicating a threshold crossing<br> </p> <pre><code class="language-python">>>> threshold = 1.1 #nT/s >>> predicted_event = np.asarray(dBdt_max20) >= threshold</code></pre> <p><br> Assuming that the observational data are obtained from NASA's CCMC and similarly processed, the event validation statistics can be calculated and displayed using the PyForecastTools package[8].<br> </p> <pre><code class="language-python">>>> import verify >>> c_table = verify.Contingency2x2.fromBoolean(predicted_event, observed_event) >>> ctable.summary(ci='bootstrap', verbose=True)</code></pre> <p> </p> <p><em>Footnotes</em><br> [1] Tóth, G., I. V. Sokolov, T. I. Gombosi, D. R. Chesney, C. R. Clauer, D. L. D. Zeeuw, K. C. Hansen, K. J. Kane, W. B. Manchester, R. C. Oehmke, K. G. Powell, A. J. Ridley, I. I. Roussev, Q. F. Stout, O. Volberg, R. A. Wolf, S. Sazykin, A. Chan, B. Yu, and J. KÃşta (2005), Space weather modeling framework: A new tool for the space science community, Journal of Geophysical Research: Space Physics, 110(A12), doi:10.1029/2005JA011126.</p> <p>[2] de Zeeuw, D. L., T. I. Gombosi, C. P. T. Groth, K. G. Powell, and Q. F. Stout (2000), An adaptive MHD method for global space weather simulations, IEEE Transactions on Plasma Science, 28(6), 1956–1965, doi:10.1109/27.902224.</p> <p>[3] Ridley, A. J., T. I. Gombosi, and D. L. DeZeeuw (2004), Ionospheric control of the magnetosphere: conductance, Annales Geophysicae, 22(2), 567–584, doi:10.5194/angeo-22-567-2004.</p> <p>[4] Toffoletto, F., S. Sazykin, R. Spiro, and R. Wolf (2003), Inner magnetospheric modeling with the Rice convection model, Space Science Reviews, 107(1), 175–196, doi: 10.1023/A:1025532008047.</p> <p>[5] Haiducek, J. D., D. T. Welling, N. Y. Ganushkina, S. K. Morley, and D. S. Ozturk (2017), SWMF global magnetosphere simulations of January 2005: Geomagnetic indices and cross-polar cap potential, Space Weather, 15(12), 1567–1587, doi: 10.1002/2017SW001695.</p> <p>[6] Morley, S. K., J. Koller, D. T. Welling, B. A. Larsen, M. G. Henderson, and J. T. Niehof (2011), Spacepy - a Python-based library of tools for the space sciences, in Proceedings of the 9th Python in science conference (SciPy 2010), Austin, TX.</p> <p>[7] SpacePy is packaged on PyPI, with the official git repository on SourceForge and an unofficial mirror on github.</p> <p>[8] PyForecastTools is packaged on PyPI and the repository is on github. The latest release is archived on Zenodo with doi: 10.5281/zenodo.1256921. The citation for v1.0.1 is Steve Morley. (2018, June 28). drsteve/PyForecastTools: PyForecastTools: Version 1.0.1 (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.1299389</p> <p> </p>
jEPlus project files and results of simulation of apartment room model with NURBS lined shading
<p>This archive consists of two sets of jEPlus project files for simulation of apartment room model, based on PNNL high-rise apartment building, with a window shading lined by NURBS curves. The difference between the two sets of project files is that in one control points of NURBS can be set independently of each other, while in the other control points are divided in six predetermined groups with the same depth within each group.</p> <p>The archive also contains results of genetic optimisation with jEPlus+EA for locations representing main USA climate zones. For each climate zone there is an obj file, containing generated population of shading variants, and a csv file, containing values of shading parameters and the respective heating and cooling loads.</p> <p>Further, the archive contains two directories, one with Java code and another with gnuplot code, that had been used to process simulations results and create diagrams for the manuscript.</p>
Simulation data output used in Griffiths and Phelps lightning initiation model, revisited
<p>Simulation data output used in the paper titled "Griffiths and Phelps Lightning Initiation Model, Revisited" submitted for publication in JGR by A. Attanasio, P. R. Krehbiel, and C. L. da Silva.</p> <p>The model simulates the collective dynamics of a system of positive streamers using the framework first proposed by Griffiths and Phelps [1976]. The README.txt file contains details about the data structure.</p> <p>C. L. da Silva, Jan/29/2019</p>
Simulation results of two agent-based models of logistics systems
<p>The data set contains the simulation results of the two different agent-based models of logistics systems: <br> 1. A medical treatment facility (MTF) model, consisting of agents representing wounded soldiers and fixed sites representing medical facilities.<br> 2. A ship fueling (SF) simulation, consisting of agents representing fuel transport ships and fixed sites representing fuel-using bases.</p> <p>Folders in folder "MTF" are related to the MTF model.</p> <p>Files in folder "casualty_rateX" are related to the case with casualty rate of X new casualties per time step, where X = 30, 50, 70, . . . , 330.</p> <p>Each file "RunN_casualtyX_fullness.txt" has the "fullness" (defined as the ratio of the number of patients at a site to the total patient capacity of that site) of each site for each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Each file "RunN_casualtyX_dow.txt" has the total number of Dead Of Wounds that occur in all sites in each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Folders in folder "SF" are related to the SF model.</p> <p>Files in folder "siteMaxX" are related to the case with an initial (and maximum) site fuel value of X units, where X = 25, 50, 75, . . . , 200.</p> <p>Each file "assetTowedFuelHistory_siteMaxX_N.txt" has the number of towed fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "assetUseFuelHistory_siteMaxX_N.txt" has the number of onboard fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "siteHistory_siteMaxX_N.txt" has the number of fuel units at each site for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p>
Supplemental material to 'Evaluation of force-based and displacement-based out-of-plane seismic assessment methods for unreinforced masonry walls through refined model simulations'
<p>This repository contains the results of discrete element simulations used to study the response of vertically-spanning unreinforced masonry walls under different wall configurations. The simulations are run using the software package UDEC 6.0 (<a href="https://www.itascacg.com/">Itasca</a>). The dataset has served as basis for the study conducted in the following paper:</p> <blockquote> <p>Godio M, Beyer K. Evaluation of force-based and displacement-based out-of-plane seismic assessmentmethods for unreinforced masonrywalls through refined model simulations. Earthquake Engng Struct Dyn. 2019;48:454-475. <a href="http://doi.org/10.1002/eqe.3144">DOI:10.1002/eqe.3144</a></p> </blockquote> <p>Version history:<br> V1: datasets '01_UDEC_VALIDATION' and '02_UDEC_RESULTS are added<br> V2: minor modifications to the datasets: deletion of unwanted temporary files from the datasets<br> V3: '00_CONTENT' PDF-file is added </p>
CESM1.2 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"
<p>CESM1.2-CAM5 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"</p> <p><strong>Citation: </strong>Zhu, J., & Poulsen, C. J. (2019). Quantifying the cloud particle-size feedback in an Earth system model. <em>Geophysical Research Letters</em>, <em>46</em>, 10910–10917. <a href="https://doi.org/10.1029/2019GL083829">https://doi.org/10.1029/2019GL083829</a></p> <p>Data include:</p> <p>(1) cloud liquid particle size for liquid (AREL) and ice (AREI), grid box averaged cloud liquid (CLDLIQ) and ice (CLDICE), fractional occurrence of liquid (FREQL) and ice (FREQI), and surface temperature (TS) in the preindustrial and 2xCO2 experiments; and<br> (2) the cloud feedback (lam_CLDTOT) and cloud particle-size feedback (lam_CLDEFR3L) from our PRP-based method.</p>
All figures for "Application of a new method to simulate post-seismic deformations in a realistic Earth model"
<p>It is a zip file for the figures used in paper "Application of a new method to simulate post-seismic deformations in a realistic Earth model".</p>
DTU 10MW reference turbine HAWC2 simulations for Model-free estimation of available power with deep learning training
<p>The time series of DTU 10MW HAWC2 model simulations of two channels: hub-height wind speed and produced power. They are generated to train model-free estimation of available power approach, using wind speed and its moving standard deviation as inputs. They include 3-hour length 100Hz simulations of 3 mean wind speeds (7 m/s, 9m/s and 11m/s) as well as 3 levels of turbulence intensity (TI = 7%, 10% and 20%). </p> <p>The dataset and the training algorithm can also be found here: <a href="https://gitlab.windenergy.dtu.dk/tuhf/deep-learning-for-available-power-estimation/tree/master">https://gitlab.windenergy.dtu.dk/tuhf/deep-learning-for-available-power-estimation/tree/master</a></p>
Finite element analysis results from simulation of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer
<p>Temperature profile data from a finite element analysis of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The mesh used for the analysis is available as a separate dataset:</p> <p><a href="https://doi.org/10.5281/zenodo.3522319">https://doi.org/10.5281/zenodo.3522319</a></p> <p>This data was used originally for the following publications (please cite if re-using the data):</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. <a href="https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf">https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf</a></p>
Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0
<p>This repository contains the data and software used for the study "Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0." The data and model were used to simulate an extreme low-water event in the River Rhine Basin.</p> <p>This archive contains the following:</p> <p><strong>wrf_hydro_nwm_public-5.2.0.tar.gz</strong>: Model code of the hydrological model WRF-Hydro. (Source: https://ral.ucar.edu/projects/wrf_hydro/model-code)</p> <p><strong>ERA5_Dataset_2016_2018.zip</strong>: ERA5 Reanalysis data and modified for the domain of the project, it includes all the mandatory variables necessary to run the model (Source:https://doi.org/10.24381/cds.bd0915c6)</p> <p><strong>GRDC_HydrologicalData_Rhine_Basin.zip</strong>: Daily stremaflow from gauges in the Rhine River from the Global Runoff Dataset Center (GRDC) (Source: https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser#dataDownload/Stations)</p> <p><strong>RegriddedFormatData.sh: </strong>Bash file to create the the input data for the regridding scripts using the ERA5 data. (Source: Campoverde, A.)</p> <p><strong>ESMFregrid_NLDAS.tar.gz</strong>: Earth System Modeling Framework (ESMF) Regridding Scripts (Source: https://ral.ucar.edu/projects/wrf_hydro/pre-processing-tools#regridding2)</p> <p><strong>wrf_hydro_arcgis_preprocessor-5.2.0.tar.gz</strong>: ArcGIS tools for preparing WRF-Hydro Routing Grids. (Source: https://ral.ucar.edu/projects/wrf_hydro/pre-processing-tools#preprocessing1)</p> <p><strong>eu_dem_3s.zip</strong>: Digital Elevation Model for Europe from HydroSHEDS - 3" (~90m) (Source: https://www.hydrosheds.org/hydrosheds-core-downloads)</p> <p><strong>S2GLC_Europe_2017_v1.2_grey.zip</strong>: Land Cover data used to create spatially distributed hydrological parameters from WRF-Hydro (RETDEPRTFAC, REFKDT, SLOPE) (Source: https://s2glc.cbk.waw.pl/extension)</p> <p><strong>WRF_Hydro_Setup_withLakeScheme.zip</strong>: Necesary files for WRF-Hydro model when considering the Lake Scheme. (Source: Campoverde, A.)</p> <p><strong>WRF_Hydro_Setup_withoutLakeScheme.zip</strong>: Necessary files for the WRF-Hydro model when <strong>not</strong> considering the Lake Scheme. (Source: Campoverde, A.)</p> <p><strong>ERA5_Land_Soil_Moisture_Dataset_2016_2018.zip</strong>: ERA5 Land data for the domain of the project includes the values of soil moisture for the period 2016-2018. (Source:https://doi.org/10.24381/cds.e2161bac)</p> <p>With this repository, we aim to provide to the comminity the necessary tools to replicate the experiments that led to the calibration of WRF-Hydro and the results of our study. </p> <p> </p>
Datasets for "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the datasets for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations".</p>
Improved cross-scale snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model
<p>Noah-MP data used to support the analyses conducted by Abolafia-Rosenzweig et al.: <strong>Improved </strong><strong>cross-scale </strong><strong>snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model</strong></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.