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328 results for “Simulation results”
Source code and simulation results for computing resonance expansions of quadratic quantities with regularized quasinormal modes
<p>This data publication supplements the article "Resonance expansion of quadratic quantities with regularized quasinormal modes" [1]. Tabulated data related to the figures in the manuscript is provided along with the Matlab scripts used to generate the results. The Riesz projection software package RPExpand [2] has been extended to support quasi normal modes (QNMs) and, in particular, the proposed method for quadratic quantities. A current version is contained in the directory <code>Code</code>. Furthermore, the input files required for scattering and resonance simulations with the finite element method (FEM) solver JCMsuite [3] are contained.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.1 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Felix Binkowski, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger: Resonance expansion of quadratic quantities with regularized quasinormal modes, Physica Status Solidi A <strong>220</strong>, 2370013 (2023)</p> <p>[2] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[3] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p>
Simulation result from "Simulating Bark Beetle Outbreak Dynamics and their Influence on Carbon Balance Estimates with ORCHIDEE r7791"
<p>Eight locations were selected which represent the range of climatic conditions within the distribution area of spruce in Europe (<em>Picea Abies</em> Karst L.) as shown in Table 4. Half-hourly weather data from the FLUXNET database <a href="https://www.zotero.org/google-docs/?ibBw15">(Pastorello et al., 2020)</a> for these locations were used to drive ORCHIDEE. Some of these locations (FON, SOR, HES, COL, WET) are not populated with spruce but all are located within the species distribution. For each location, a pure spruce stand was simulated and the available FLUXNET data was looped to simulate a 100-year period. The study did not investigate the effect of species mixture in the simulation experiments. Other inputs, including soil texture, pH and soil color were obtained from the USDA map derived from <a href="https://www.zotero.org/google-docs/?aaWPI6">Eswaran et al. (2003</a>), for the corresponding pixel.</p> <p>The amount of fresh breeding woody substrate inputs used by the bark beetles to breed was controlled by modifying the maximum wind speed of a windthrow event in ORCHIDEE. Seven wind speeds ranging between 19 m/s and 40 m/s were selected (Table 3). This range is justified by the observation that mean wind speeds below 19 m/s could not trigger a windthrow event in ORCHIDEE <a href="https://www.zotero.org/google-docs/?jEqNDm">(Chen et al., 2018)</a> while for wind speeds exceeding 40 m/s, more than 60% of the trees are uprooted, leaving too few living trees to trigger a bark beetle outbreak within the same pixel. </p> <p>To investigate the impact of windthrow intensity and background climate on bark beetle outbreaks, the study conducted a total of 56 [8 sites x 7 wind speed intensities] simulations as given in table 3. The same 56 simulations were also used to analyze the sensitivity of the carbon balance of spruce forests to windthrow intensity and background climate.</p> <p>Where most land surface models use a turnover time to simulate continuous mortality <a href="https://www.zotero.org/google-docs/?5jGfXF">(Thurner et al., 2014; Pugh et al., 2019)</a>, ecological reality is better described by abrupt mortality events. An idealized simulation experiment was used to qualify the impact of abrupt mortality on net biome productivity by changing from a framework in which mortality is approximated by a constant background mortality to a framework in which mortality occurs in abrupt, discrete events. To test the impact of a change in mortality framework two versions of ORCHIDEE were compared to create an idealized simulation experiment: (1) a version simulating mortality as a continuous process, labeled ”the continuous version”, and (2) the version capable of simulating abrupt mortality from windthrow and subsequent bark beetle outbreaks, labeled ”the abrupt version”. The effect of simulating abrupt mortality was evaluated over 20-, 50-, and 100-year time horizons.</p> <p>The effect of changing the framework of simulating mortality from continuous to abrupt was qualified on the basis of 112 simulations (8 sites x 7 wind speeds x 2 model versions) of 100 years each. The simulations with abrupt mortality were run first. Subsequently, the number of trees killed was quantified and used as a reference value for the continuous mortality set-up. This approach resulted in the same quantities of dead trees at the end of the simulation for both frameworks, which then differed only in the timing of the simulated mortality. This precaution is necessary to avoid comparing two different mortality regimes where the result would mainly be explained by the intensity of the mortality rather than by its underlying mechanisms. </p>
Simulation results for study on pulsed electron lenses for space charge mitigation
<p>Simulation results for beam loss in the FAIR SIS100 synchrotron for a comprehensive study on pulsed electron lenses for space charge mitigation. The affiliated manuscript "Pulsed electron lenses for space charge mitigation" describing the study parameters is published on arxiv.org (https://arxiv.org/abs/2310.02365) and submitted for journal publication.</p> <p>For the "ffsc" files, each file contains the tabulated beam survival rate of 1000 simulated particles for a given bare tune. A file typically gathers results from scanning the betatron tune quadrant 18.5 <= Qx,y <= 19.0 in tune steps of 0.01.</p> <p>Explanation of file names:</p> <p>- "ffsc": using the fixed frozen Gaussian field map model for space charge (as established in https://doi.org/10.1103/PhysRevAccelBeams.25.054402 );</p> <p>- "nel": number of pulsed electron lenses placed symmetrically in the straight sections of the SIS100 ring;</p> <p>- "alpha": linear compensation degree, alpha=1.0 corresponds to a total electron lens tune implied tune shift equal to the linear rms-equivalent KV space charge tune shift;</p> <p>- "N": intensity in percent units of the FAIR design intensity for Uranium-28+ beams, i.e. N=100 corresponds to the FAIR design intensity;</p> <p>- "2D" or "3D": the 3D results correspond to the full simulation model with (nonlinear) synchrotron motion, the 2D results assume a fixed longitudinal phase-space distribution and only simulate the transverse dynamics (thus, periodic resonance crossing is suppressed by construction).</p>
HeatResilientCity II - work package 2.2: Influence of regional and urban climate on indoor overheating - Results of building performance simulation
<p>This repository contains the <strong>results of the building performance simulations</strong> carried out in the working package 2.2 Influence of regional and urban climate on indoor overheating of the project <a href="http://heatresilientcity.de/">HeatResilientCity II</a>. The buildings under consideration are a multi-residential so-called ‘Gründerzeithaus’ (GZH) and a large-panel construction (LPC) building. The results were extracted for two rooms on the top floor/attic of each building and follow a consistent name convention. Each file contains hourly resolved values for the outdoor air temperature, the indoor air temperature, the indoor operative temperature and the relative humidity indoors and outdoors. Further information can be found in the README of this repository. The simulations were performed for five <strong>different</strong> <strong>regions</strong> in Germany (Dresden, Hamburg, Köln, Stuttgart and Potsdam) for <strong>average present</strong> and <strong>future</strong> <strong>summers</strong> based on meteorological measurement data and under consideration of <strong>urban</strong> <strong>climate</strong>.</p> <p>In addition to the ‘plain’ simulation results, some <strong>heat-indicator variables</strong> were calculated and listed in the files <em>Calculated_Variables.txt</em>. The calculated quantities include temperature-weighted exceedance hours (TWEH) for the limits of 25, 26 and 27 °C (defined in DIN 4108-2:2013 as ‘Übertemperaturgradstunden’) and the maximum operative temperature calculated for the period from April to September.</p> <p>The used <strong>input data</strong> and <strong>building models</strong> can be found in the related repository.</p>
Matsim Simulation Aggregated Results Hasselt
<p>This dataset contains aggregated results for the following scenarios based on Matsim simulations.</p> <p>1. Base scenario</p> <p>2. Car access restriction scenario (Policy 1)</p> <p>3. Increased in bus frequency scenario (Policy 2)</p>
Matsim Simulation Aggregated Results Bologna
<p>This dataset contains aggregated results for the following scenarios based on Matsim simulations.</p> <p>1. Base scenario</p> <p>2. Car access restriction scenario (Policy 1)</p> <p>3. Increased in bus frequency scenario (Policy 2)</p>
Global Storm Tide Modeling on Unstructured Meshes with ADCIRC v55 - Simulation Results and Model Setup
<p>Simulation results and model setup for the paper entitled "Global Storm Tide Modeling with ADCIRC v55: Unstructured Mesh Design and Performance". Simulations conducted using <a href="http://adcirc.org/">ADCIRC</a> (pre-release Version 55) on unstructured triangular meshes of the global Earth's ocean. </p> <p>Contains:</p> <ol> <li>Global Tide Harmonics: Simulated harmonic constituents of global astronomical tide on various mesh designs (*.53.nc).</li> <li>Local Storm Tide: ADCIRC model setup and simulation results from storm tides forced by Hurricane Katrina and Super Typhoon Haiyan on meshes with different local refinements (1.5 km, 500 m, 150 m) in the storm landfall region. <ul> <li>ADCIRC model input files (*.13, *.14, *.15, *.22, *.221, *.222, fort.rotm)</li> <li>Global maximum storm tide elevations (*_maxele.63.nc)</li> <li>Global 3-hourly storm tide elevation time series (*.63.nc)</li> <li>Storm tide elevation and velocity time series (20-min intervals) at selected stations (*.61.nc, *.62.nc)</li> </ul> </li> <li>Zipped archive of the ADCIRC code (adcirc-cg-GLOBAL.zip) used to produce the results archived here.</li> </ol> <p>See the README files for further details.</p>
Simulation results: CryoGrid 3 set-up for ice-wedge polygons
<p>This dataset contains numerical simulation results obtained with the CryoGrid 3 permafrost model for a field site in northeast Siberia.</p> <p>The model code and settings which were used to create these results are deposited at <a href="http://doi.org/10.5281/zenodo.2601973">10.5281/zenodo.2601973</a>.</p> <p>A journal article which describes the simulation results scientifically has been published in the scientific journal <em>The Cryosphere</em> as <a href="http://doi.org/10.5194/tc-13-1089-2019">10.5194/tc-13-1089-2019</a>.</p> <p> </p> <p>The dataset contains two types of simulations described in the article</p> <p><em>VALIDATION_200710-201412_*</em>: These simulations of a 7-year period which been used to evaluate the model performance by comparison to field observations.</p> <p><em>LONGTERM_197910-204412_*</em>: These simulations of a 65-year period have been used to investigate the long-term dynamics of the model as described in the article.</p>
WRF-Chem simulation results of Aerosol Optical Depth and PM2.5 concentrations
<p>This is the data for the manuscript submitted to JGR-Atmosphere: Simulations and characteristics of extreme aerosol events over eastern North America. </p>
Averaged results of blood flow simulations with discrete RBC tracking for microvascular networks
<p>The dataset contains the results for blood flow simulations in 3 cerebral micorvascular networks.The microvascular networks are from the mouse parietal cortex (Blinder et al., 2013) and embedded in a tissue volume of approximately 1 cubic mm. For the blood flow simulations we used a numercial model with discrete tracking of RBCs which is described in Schmid et al., 2017.</p> <p>For each network the following data are provided:<br> - Microvascular network with averaged flow and pressure field, as well as averaged values for the distribution and motion of red blood cells (RBCs).<br> - RBC trajectories describing the motion of individual RBCs through the microvascular networks.<br> - The data is stored as a graph, i.e. vertices connected by edges.<br> - Details regarding the simulation parameters can be found in Schmid et al., 2017.<br> - Data format (pickle - files containing python dictonairies).<br> <br> <strong>Microvascular networks:</strong><br> <strong>edgesDict.pkl:</strong> dictionary with edge related data (dictionary keys: flow [um^3/ms], diameter [um], tuple [-], httBC [-], nkind [-], length [um], htt [-], nRBC [-], diameters [um], points [um])<br> <strong>verticesDict.pkl:</strong> dictionary with vertex related data (dictionary keys: pressure [mmHg], coordinates [um], pBC [mmHg])</p> <p>Additional comments on dictionary keys:<br> - pBC: pressure boundary conditions. 'None' for internal nodes. Assigned based on the hierarchical boundary conditions approach (see Schmid et al. 2017 for details)<br> - tuple: connectivity of graph, tuple of vertices<br> - httBC: tube hematocrit boundary conditions. 'None' for internal nodes. Constant value assigned.<br> - nkind: integere to describe the vessel type. 0: pial artery, 1: pial venule, 2: descending arteriole, 3: ascending venule, 4: capillaries, 5: unknown<br> - htt: tube hematocrit<br> - nRBC: number of red blood cells<br> - points: list of tortuous vessel coordinates per edge<br> - diameters: local diameter measurements associated to the 'points' key.</p> <p><br> <strong>RBC trajectories:</strong><br> <strong>RBC_trajectories.pkl: </strong>dictonary for each RBC with relevant tracking data (dictionary key: RBC index). The relavant tracking data per RBC is stored in another dictionary with the following keys: edges, lengths, times, pressure, nkindsMod, RBCleft</p> <p>Additional comments on dictionary keys per RBC:<br> - RBCleft: bool to indicate that RBC left the computational domain<br> - edges: edge indices through which the RBC moves on its way through the vasculature<br> - pressure: pressure [mmHg] values at the nodes along the RBC trajectory<br> - times: time [ms] the RBC spends in the respective edge segment<br> - nkindsMod: nkind at the nodes along the RBC trajectory <br> - lengths: cummulative length travelled [um]</p>
Simulations results
<p>These are the datasets containing the simulation results for the t-test and linear regression example as demonstrated in the article "Approximate Bayes Factors: flexible and likelihood-free model comparison with approxbayes". </p>
Source code and simulation results: Poles and zeros of electromagnetic quantities in photonic systems
<h4><strong>Summary</strong></h4> <p>This publication supplements the article "Poles and zeros of electromagnetic quantities in photonic systems" with tabulated data and matlab code that allows to reproduce the results. The article elaborates how evaluating resonances based on contour integrals of scalar electromagnetic quantities extends to computing zeros. Furthermore, direct differentiation of underlying scattering problems is used to compute sensitivities with respect to design parameters.</p> <h4><strong>Structure</strong></h4> <p>The script 'main_text.m' can be used to reproduce the results provided in the paper. In tabulated form the results are contained in the directory <strong>tabulated</strong>. Furthermore, the script 'supplement.m' can be used to reproduce results presented in the supplement. The directory <strong>RPExpand </strong>contains the software RPExpand v2, which is available on <a href="https://doi.org/10.5281/zenodo.10371002">Zenodo</a> with additional examples. </p> <h4><strong>Compute residues</strong></h4> <p>The modal expansion of the Fourier transform is based on its residues at the dominant resonances. If the poles are simple, which often is the case, the residues can be obtained directly from the eigenvectors of the generalized eigenvalue problem used to obtain the poles or the zeros. Introducing the Vandermonde matrix</p> <p>\(V = \begin{bmatrix} 1 & \dots & 1 \\ w_1 & \dots & w_M \\ \vdots & & \vdots \\ w_1^{M-1} &\dots & w_M^{M-1} \end{bmatrix}\),</p> <p>the Hankel matrix \(H\) can be written as \(H = V A V^T\) with \(A\) being the diagonal matrix \(\mathrm{diag}(a_1,\dots,a_M)\) containing the residues \(a_m \). This decomposition is a consequence of the Cauchy's reisdue theorem if the poles are simple. Furthermore, we now that \(V^{-T}\) solves the generalized eigenproblem \(H^<X = HX\Omega\) (Eq. 2 in the original paper) and hence the eigenvectors we get from Matlabs eig routine are \(X = V^{-T}D\) where \(D\) is some scaling. It follows that we obtain the residues using \(A = X^T H X (X V^T)^{-2}\)</p> <h4><strong>Derivatives</strong></h4> <p>Similarly, our framework provides a straight forward approach to the derivatives of zeros and poles if they are simple. Using direct differentiation we have access to partial derivatives of the quantity \(q(\omega)\) and hence the derivatives of the moments \(s_k = \frac{1}{2\pi i} \oint_C \omega^k q(\omega) \mathrm{d}\omega\). For the zeros the inverse \(1/q(\omega)\) and the respective derivative are considered. Using Cauchy's residue theorem the derivatives \(\frac{\partial w_m}{\partial p}\)are solutions of the linear system of equations \(\frac{\partial s_k}{\partial p} = \sum_{m = 1}^{M}\left[k\omega_m^{k-1}\frac{\partial w_m}{\partial p} a_m + \omega_m^k\frac{\partial a_m}{\partial p} \right]\).</p> <h4><strong>Higher order singularities</strong></h4> <p>Finding higher order poles and zeros is possible without further adaptation. Computing derivatives and residues requires some special care. The moments are then given by \(s_k = \sum_{m=1}^{M} \sum_{n = 1}^{N_m} a_{m,n} \frac{k! \, \omega^{k-n+1}}{(k-n+1)!(n-1)!}\)with \(a_{m,n}\) being the residue of the pole \(\omega_m\) and \(n \) refers to the order. Accordingly expressions for the derivatives are available.</p> <h4><strong>Error estimates</strong></h4> <p>The estimated errors in Table 1 refer to the number of integration points, i.e. we are interested in the question how close we get with a given number of integration points to the exact solution of the chosen approximate model of the physical system. Due to propagation of the error the convergence of the derivatives is shifted towards a larger number of integration points.</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (version 5.4.3 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holder in 'zeros_poles.m' by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <h4><strong>References</strong></h4> <p>[1] Felix Binkowski, Fridtjof Betz, Rémi Colom, Patrice Genevet, Sven Burger, Poles and zeros of electromagnetic quantities in photonic systems, https://doi.org/10.48550/arXiv.2307.04654</p> <p>[2] Anthony P. Austin, Peter Kravanja, Lloyd N. Trefethen, Numerical algorithms based on analytic function values at roots of unity, SIAM Journal of Numerical Analysis 52, 1795 (2014), https://doi.org/10.1137/130931035</p> <p>[3] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p>
Initial Simulation Results Using ModularBuildingPy on a High-Rise Modular Steel Building
<p>These are output files from 'ModularBuildingPy', a Python-based tool designed for numerical modeling and analysis of volumetric modular steel buildings. Refer to <a href="https://mbbatukan.github.io/ModularBuildingPy/">the documentation</a> for more information. </p>
SPT results - Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury
<p>Results of the study "Simulation of zonation-function relationships in the liver using coupled multiscale models: Application to drug-induced liver injury"</p>
Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm
<p>This publication provides data published in the article "Efficient rational approximation of optical response functions with the AAA algorithm" [1] in tabulated form along with the Matlab scripts that have been used to produce them. These scripts interface the finite element method solver JCMsuite [2,3]. The article presents rational approximations of optical response functions based on an extended version of the AAA algorithm [4] that allows to efficiently reconstruct sensitivty spectra and gives access to sensitivities of poles, residues, and zeros. Furthermore, the rational approximation of a scalar observalbe is used to construct solutions of the source free Maxwell's equation, i.e., a nonlinear eigenvalue problem. </p> <p><strong>The physical Structure</strong></p> <p>The example is based on the chiral metasurface introduced in [5]. For the sake of simplicity we added infinite layers of SiO\(_2\) to the top and the bottom of the structure. The original structure has a SiO\(_2\) substrate and a layer of PMMA polymethyl methacrylate (PMMA) deposited on top. PMMA can be modelled with the same refractive index of 1.45 as SiO\(_2\). Furthermore, our simulations include the 13 nm indium tin oxide (ITO) coating which drastically reduces the Q-factor as it is slightly absorbing. The accuracy of the discrete model is verified by assessing reflection, transmission, and absorption at 241 evenly spaced points within the specified range. Energy conservation requires that the discrepancy between their sum and the energy entering the system is zero. The numerical discretization is chosen such that the maximum relative error is less than \(3\times10^{−5}\).</p> <p><strong>Dispersion</strong></p> <p>Tabulated data for ITO has been taken from the <a href="https://refractiveindex.info/?shelf=other&book=In2O3-SnO2&page=Konig">refractiveindex.info</a> database (T. A. F. König et al., 2014, https://doi.org/10.1021/nn501601e) and the data for TiO2 was kindly provided the authors of [5]. The permittivity \(\varepsilon = (n+ik)^2\) is locally approximated as a rational function, i.e., only data in a vicinity of the frequency range of interest is considered. As we aim for a function with the symmetry \(f^\ast(\omega) = f(-\omega^\ast)\) we add the complex conjugated data at negative frequencies and enforce the symmetry in a second step. The partial fraction decomposition of the required function is of the form: \(\varepsilon(\omega) = \varepsilon_\infty + \sum_{j=1}^{4}a_j/(\omega-\omega_j) - a_j^\ast/(\omega+\omega_j^\ast)\) with the residues \(a_j\) and the poles \(\omega_j\). We expect 4 pairs of poles to sufficiently approximate the data within the range of interest (4 with positive and 4 with negative real parts).</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (at least 6.2.0)</li> <li>MATLAB (tested with version R2023b)</li> </ul> <p>In order to run the simulations with JCMsuite you must replace corresponding place holders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <p><strong>Usage</strong></p> <p>With the content of 'spectra.zip' you can reproduce results presented in the paper. Running the script 'plots.m' will not start any expensive simulation but use the provided data. With 'dispersion.m' the fits to the material data can be reproduced. Additionally, tabulated data is contained in 'data/ascii'. The archive 'eigenmodes.zip' must be extracted in the same directory as 'spectra.zip'.</p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger, Felix Binkowski: Efficient rational approximation of optical response functions<br>with the AAA algorithm, https://doi.org/10.48550/arXiv.2403.19404.</p> <p>[2] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192.</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763.</p> <p>[4] Y. Nakatsukasa, O. Sète, and L. N. Trefethen, The AAA Algorithm for Rational Approximation, SIAM Journal on Scientific Computing <strong>40</strong>, A1494 (2018), http://dx.doi.org/10.1137/16M1106122.</p> <p>[5] X. Zhang, Y. Liu, J. Han, Y. Kivshar, and Q. Song, Chiral emission from resonant metasurfaces, Science <strong>377</strong>, 1215 (2022), http://dx.doi.org/%2010.1126/science.abq7870.</p>
Tsunami Simulation: TsunAWI Testcase "Coquimbo 2015", Setups and Example Results
<p>This dataset contains three setups with example results for TsunAWI simulations for the Chilean coast with a focus on<br> the Coquimbo region. They are used in the LEXIS project, WP6 "Earthquake and Tsunami Pilot".</p> <p><strong>coquimbo_150m.tgz </strong>- coarse setup with down to 150m resolution in Coquimbo and other areas of interest along the Chilean coast</p> <p><strong>coquimbo_20m.tgz </strong>- fine<strong> </strong>setup with down to 20m resolution in Coquimbo, also high resolution along other areas of interest along the Chilean coast</p> <p><strong>coquimbo_20m_new.tgz </strong>- setup tuned for the LEXIS workflow aiming at a real time estimate of the inundation on the City of Coquimbo, thus a focus with 20m resolution only in Coquimbo region.</p> <p> </p> <p>Each dataset includes a README explaining the included files. The following description is taken as pars pro toto from coquimbo_20m_new.tgz.</p> <p><strong>Computational Mesh</strong></p> <ul> <li>mesh/Coquimbo_20m/nod2D.out <br> Location of mesh vertices in geographical coordinates, vertices on the open boundary are flagged with "1"<br> Resolution in Coquimbo area: 20m</li> <li>mesh/Coquimbo_20m/nodhn.out<br> Water depth at the respective vertices. Negative values denote elevation on land. <br> In the ocean: ‘The GEBCO_08 Grid, version 20090202 (http://www.gebco.net)’ <br> On land: SRTM, Shuttle radar topography mission, 1 arcsec resolution, https://www2.jpl.nasa.gov/srtm/ Bilinear interpolation to the mesh vertices.</li> <li>mesh/Coquimbo_20m/elem2D.out<br> Mesh geometry: List of the vertices forming the triangular elements.</li> </ul> <p><strong>Input Parameters</strong></p> <ul> <li>namelist.tsunami<br> Parameters read by TsunAWI at run time, e.g., <ul> <li>path to the mesh and the output</li> <li>the time step dt and the time frame of the simulation T_end</li> <li>physical parameters like Manning friction, viscosity</li> <li>toggle the output </li> </ul> </li> <li>Additional command line parameters<br> The results below are for an idealized source (Cosine bell at the location of the 2015 Coquimbo event, started with <br> ompTsuna.x -xy -71.674 -31.573 -mw 8.3 -id coquimbo_20m</li> </ul> <p><strong>Result</strong></p> <ul> <li>coquimbo_20m_new.nc <br> Example output in netcdf format, raw data on the triangular mesh, see<br> ncdump -h coquimbo_20m.nc </li> <li>coquimbo_20m_new_inundation.nc<br> The same raw data but restricted to the inundated area, for faster postprocessing.</li> </ul> <p>The TsunAWI version used is <br> https://gitlab.awi.de/tsunawi/tsunawi/-/tags/LEXIS<br> with one minor bugfix: Add <br> use mpi_parallel<br> to line 881 of tsunawi/src/parameters.F90 </p> <p>Compiled with tsunawi/src/Makefile.in_karolina<br> and run on karolina.it4i.cz in MPI-parallel mode with 128 tasks.</p> <p> </p> <p>This work was supported by the LEXIS project funded by the EU’s Horizon 2020 research and innovation programme (2014-2020) under grant agreement No 825532.</p>
E3SM simulation results and associated python analysis scripts
<p>This archive contains E3SM Land Model simulation results associated with the <em>Journal of Advances in Modeling Earth Systems (JAMES)</em><em> </em>article titled "More Realistic Intermediate Depth Dry Firn Densification in the Energy Exascale Earth System Model (E3SM)," by Adam M. Schneider, Charles S. Zender, and Stephen F. Price. Also included in the archive are python scripts used to analyze associated data and an offline, statistical firn model.</p>
Monte-Carlo-simulated MR spectra of the rat brain and their quantification results obtained with QUEST and QUEST-MM jMRUI algorithms
<p>MC-simulated spectra of the rat brain along with the corresponding basis set (metabolite signals simulated using NMRScopeB from jMRUI) and the QUEST-MM, QUEST, QUEST(Met+Back) and QUEST(Met+MM) quantification results are stored in the MC_results folder.</p> <p>Results of an in-vivo rat brain MRS signal (SPECIAL, dead time t0 = 0.134 ms, TE = 2.8 ms, at 9.4 T) quantification performed with the QUEST-MM jMRUI algorithm (origin for MC-simulation) are stored in the folder rat_results.</p> <p>All files can be loaded to jMRUI software version 5 and later.</p>
Performance results of different scheduling algorithms used in the simulation of a modern game engine
<p><strong>Performance results of different scheduling algorithms used in the simulation of a modern game engine</strong></p> <p>These results are a companion to the paper entitled "<em>Exploring scheduling algorithms for parallel task graphs: a modern game engine case study</em>" by M. Regragui et al.</p> <p><strong>General information</strong></p> <p>This dataset contains raw outputs and scripts to visualize and analyze the scheduling results from our game engine simulator.<br> The result analysis can be directly reproduced using the script run_analysis.sh. A series of Jupyter Notebook files are also available to help visualize the results.</p> <p><strong>File information</strong></p> <p>- All Scenario*.ipynb files contain python scripts to visualize and analyze the simulation results.<br> - The Scenario*.py files contain python scripts that can be run directly with Jupyter Notebook.<br> - The requirements.txt file contains the names and versions of python packages necessary to reproduce the analysis.<br> - The run_analysis.sh file contains a bash script to install the required python packages and run the Scenario*.py scripts.</p> <p>The results are organized in five folders:</p> <p>1. Result_1 contains the results for Scenario 1 generated using file input_scenario_1.txt.<br> 2. Result_2 contains the results for Scenario 2 generated using file input_scenario_2.txt.<br> 3. Result_3 contains the results for Scenario 3 generated using file input_scenario_3.txt.<br> 4. Result_CP_1 contains the results for the critical path of Scenarios 1 and 2 generated using file input_CP_scenario_1.txt.<br> 5. Result_CP_3 contains the results for the critical path of Scenario 3 generated using file input_CP_scenario_3.txt.</p> <p>Each result file (e.g., HLF_NonSorted_Random_1_200_10.txt) contains 200 lines representing information of the 200 frames that were simulated. Each line contains four values: the frame number, the duration of the frame (in microseconds), a critical path estimation for the previous frame (in microseconds), and the load parameter (value between 0 and 1).</p> <p>The outputs of this analysis include some PDF files representing the figures in the paper (in order) and some CSV files representing the values shown in tables. The standard output shows the p-values computed in parts of the statistical analysis.</p> <p><strong>Software and hardware information</strong></p> <p>The simulation results were generated on an Intel Core i7-1185G7 processor, with 32 GB of LPDDR4 RAM (3200 MHz). The machine ran on Ubuntu 20.04.3 LTS (5.14.0-1034-oem), and g++ 9.4.0 was used for the simulator's compilation (-O3 flag).</p> <p>The results were analyzed using Python 3.8.10, pip 20.0.2 and jupyter-notebook 6.0.3. The following packages and their respective versions were used:</p> <p>- pandas 1.3.2<br> - numpy 1.21.2<br> - matplotlib 3.4.3<br> - seaborn 0.11.2<br> - scipy 1.7.1<br> - pytz 2019.3<br> - python-dateutil 2.7.3<br> - kiwisolver 1.3.2 <br> - pyparsing 2.4.7 <br> - cycler 0.10.0 <br> - Pillow 7.0.0<br> - six 1.14.0 </p> <p><strong>Simulation information</strong></p> <p>Simulation results were generated from 4 to 20 resources. Each configuration was run with 50 different RNG seeds (1 up to 50).</p> <p>Each simulation is composed of 200 frames. The load parameter (lag) starts at zero and increases by 0.01 with each frame up to a value equal to 100% in frame 101. After that, the load parameter starts to decrease in the same rhythm down to 0.01 in frame 200.</p> <p><strong>Algorithms abbreviation in presentation order</strong></p> <p>FIFO serves as the baseline for comparisons.</p> <p>1. FIFO: First In First Out.<br> 2. LPT: Longest Processing Time First.<br> 3. SPT: Shortest Processing Time First.<br> 4. SLPT: LPT at a subtask level.<br> 5. SSPT: SPT at a subtask level.<br> 6. HRRN: Highest Response Ratio Next. <br> 7. WT: Longest Waiting Time First.<br> 8. HLF: Hu's Level First with unitary processing time of each task.<br> 9. HLFET: HLF with estimated times.<br> 10. CG: Coffman-Graham's Algorithm.<br> 11. DCP: Dynamic Critical Path Priority.</p> <p><strong>Metrics</strong></p> <p>* SF: slowest frame (maximum frame execution time)<br> * DF: number of delayed frames (with 16.667 ms as the due date)<br> * CS: cumulative slowdown (with 16.667 ms as the due date)<br> </p>
Simulation results for ProSSE and datafile for case study
<p>This repository includes the data files for the simulation study and the case study in the publication.</p> <p>The simulation_result.Rdata is an R data file that stores simulated data and results. The data is used to generate Figures 2-7, S1, and S2.</p> <p>The casestudy_trees.Rdata is an R data file that stores the posterior samples of the species tree of the Australian rainbow skinks and the model fitting on these trees. The data file is used to generate Figure 8.</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.