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328 results for “Simulation results”
Raw results of the simulation experiments performed to evaluate different Aloha-based schemes for LoRa-based DtS uplink transmissions
<p>This zip file contains the gnuplot and data files needed to generate the figures showing the experimental results presented in [1].</p> <p>[1] S. Herrería-Alonso, M. Rodríguez-Pérez, R. F. Rodríguez-Rubio and F. Pérez-Fontán, "Improving Uplink Scalability of LoRa-Based Direct-to-Satellite IoT Networks," in <em>IEEE Internet of Things Journal</em>, vol. 11, no. 7, pp. 12526-12535, April 2024, doi: 10.1109/JIOT.2023.3333934.</p>
Simulation results of Manoj J et al (2023, in review)
<p>Simulation results of Manoj J et al (2024)</p> <p>Manoj J, A., Loritz, R., Villinger, F., Mälicke, M., Koopaeidar, M., Göppert, H., and Zehe, E.: Toward Flash Flood Modeling Using Gradient Resolving Representative Hillslopes, Water Resour. Res., 60, https://doi.org/10.1029/2023WR036420, 2024.</p> <p> </p>
Simulation Results in the Paper "Propagation of Slow Slip Events on Rough Faults: Clustering, Back Propagation, and Re-rupturing" [Dataset]
<p>Data file "simulations.mat" contains the 5 simulations of slow slip events on flat or rough faults. </p> <table> <tbody> <tr> <td>structure array</td> <td>description</td> <td>reference</td> </tr> <tr> <td>s0</td> <td> 2.5 km long flat fault</td> <td>Fig. 2b</td> </tr> <tr> <td>s1</td> <td>2.5 km long rough fault</td> <td>Fig. 2c</td> </tr> <tr> <td>s2</td> <td>10 km long rough fault</td> <td>Fig. 5</td> </tr> <tr> <td>s3</td> <td>10 km long fractal fault</td> <td>Fig. 7</td> </tr> </tbody> </table> <p>structure array consists of:</p> <p>t: time (s)</p> <p>x: distance (m)</p> <p>v: slip rate (m/s)</p> <p>slip: accumulated slip (m)</p> <p>tau: shear stress (Pa)</p> <p>sigma: normal stress (Pa)</p> <p>notes: description</p> <p> </p> <p>Data file "catalog.mat" contains 3 simulated slow slip events' catalogs on flat and rough faults, c0, c1, and c2, in Fig. 4a, 4b, and 4c, respectively.</p> <p>It consists of:</p> <p>l: rupture length (m)</p> <p>time: time (s)</p> <p>notes: description</p>
Numerical results from DEM simulations
<p>The DEM code, DEPTH (https://www.jamstec.go.jp/namr/project06.html), was used to simulate the compressive behavior of hierarchical granular piles. The "zenodo_SA24.zip" file contains six folders:</p> <ol> <li>hierarchical_16 <ul> <li>F_wall_1e4_ave.txt (Fig. 19)</li> <li>fig_P_phi_16.pdf</li> </ul> </li> <li>hierarchical_32 <ul> <li>contact_angle_32.dat (Fig. 12)</li> <li>coordination_distance_32.dat (Figs. 9 and 10)</li> <li>distance_32_step_xxxx.dat (Fig. 11)</li> <li>energy_velocity_32.dat (Fig. 13(a))</li> <li>F_wall_1e4_ave.txt (Figs. 7 and 17(a))</li> <li>POV_32_stepxxxx_y.dat (Fig. 2)</li> <li>POV_32_stepxxxx_LR.png</li> </ul> </li> <li>hierarchical_64 <ul> <li>coordination_distance_64.dat (Figs. 9 and 10)</li> <li>distance_64_step_xxxx.dat</li> <li>energy_velocity_64.dat (Fig. 13(b))</li> <li>F_wall_1e4_ave.txt (Figs. 8 and 17(b))</li> <li>POV_64_stepxxxx_y.dat (Fig. S2)</li> </ul> </li> <li>single_16 <ul> <li>F_wall_1e4_single_casex.txt (Fig. 18)</li> </ul> </li> <li>single_32 <ul> <li>F_wall_1e4_single_casex.txt (Fig. 16(a))</li> </ul> </li> <li>single_64 <ul> <li>F_wall_1e4_single_casex.txt (Figs. 15(a) and 16(b))</li> <li>pair_integral_case1.txt (Fig. 15(b))</li> </ul> </li> </ol>
Model results for Miocene simulations
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Simulated wind field outputs from WRF and significant wave height results from SWAN simulations.
<p>Supplementary materials for the article titled <em>“Super Typhoons Simulation: A Comparison of WRF and Empirical Parameterized Models for High Wind Speeds.”</em> The data include simulated wind field outputs and significant wave height results from SWAN simulations.</p>
Data of simulation result
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Tabulation Result of Molecular Docking Simulation
<p>Tabulation Result of Molecular Docking Simulation</p>
Simulation results
<p>Asian monsoon simulation</p>
CAIRDIO simulation results and air-quality observations for Leipzig
<p>This dataset contains results with the model CAIRDIO applied for a simulation case study of Leipzig from 1-3 March 2020, and measured concentration vs. time series of black carbon and particulate matter at 5 different air-monitoring sites for comparison. </p>
Simulation results for "Future bioenergy expansion could alter carbon sequestration potential and exacerbate water stress in the United States"
<p>This dataset is the CTSM simulation results for the paper entitled "Future bioenergy expansion could alter carbon sequestration potential and exacerbate water stress in the United States" that published in Science Advances.</p>
SCALE-AMPS data: Simulation results of SHEBA case_Appednix C
<p>SCALE-AMPS simulation data for the article "Modeling Performance of SCALE-AMPS: Simulations of Arctic Mixed-Phase Clouds Observed during SHEBA<span> </span>". The simulation setup is specified in the Appendix C.</p>
Results of Simulation Study II for "New weighting methods when cases are only a subset of events in a nested case-control study"
<p>This file includes the result of Simulation Study II in Section 4.3 of the manuscript.</p>
Simulation results of the technology diffusion model for electrolysis capacity
<p>This upload contains the pre-run simulation output of the technology diffusion model for the article:</p> <p>Odenweller, A., Ueckerdt, F., Nemet, G. F., Jensterle, M., and Luderer, G.: "Probabilistic feasibility space of scaling up green hydrogen supply".</p> <p>You may use the files to run the model code (available on GitHub) in reproduction mode, which reproduces all figures of the article without having to run the time-consuming simulation yourself. All files are provided in the rds format.</p> <p>Contents of this upload:</p> <ul> <li>01_conventional_growth_parameters.rds - Parameters of the distributions in the conventional growth case</li> <li>02_conventional_growth_sample.rds - Sample of the distributions in the conventional growth case</li> <li>03_conventional_growth_results.rds - Simulation results of model in the conventional growth case</li> <li>04_unconventional_growth_parameters.rds - Parameters of the distributions in the unconventional growth case</li> <li>05_unconventional_growth_sample.rds - Sample of the distributions in the unconventional growth case</li> <li>06_unconventional_growth_results.rds - Simulation results of model in the unconventional growth case</li> </ul>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for multi-capillary dilation scenarios mimicking pericyte ablation
<p>Documentation to reproduce in silico analyses related to the manuscript<br> <strong>Pericyte remodelling is deficient in the aged brain and contributes to impaired capillary flow and structure</strong></p> <p>by</p> <p>Andrée-Anne Berthiaume, Franca Schmid, Stefan Stamenkovic, Vanessa Coelho-Santos, Cara D. Nielson, Bruno Weber, Mark W. Majesky and Andy Y. Shih</p> <p>Published in<br> Nature Communications (doi: 10.1038/s41467-022-33464-w)</p> <p>All simulations are performed based on the in silico blood flow model with discrete red blood cell (RBC) tracking as described in Schmid et al., 2017, PLoS Comp Biol (doi: <a href="https://doi.org/10.1371/journal.pcbi.1005392">10.1371/journal.pcbi.1005392</a>). The bi-phasic blood flow simulations have been performed in two realistic microvascular networks from the somatosensory cortex of the mouse first published in Blinder et al., 2013, Nature Neuroscience (doi: 10.1038/nn.3426). </p> <p>For further information and instructions please contact Franca Schmid (franca.schmid@unibe.ch, orcid.org/0000-0002-0689-9366).</p> <p><br> <strong>Simulation results:</strong></p> <p>All time-averaged simulation results are saved as vascular graphs building on the python library igraph and stored as python pickle files (Python 2.7). For each simulation two files are available: <em>verticesDict.pkl</em> and <em>edgesDict.pkl</em>containing all vertex and edge specific data, respectively. A summary of the vertex and edge attributes is provided below. The folder <em>Baseline</em> contains the simulation results for microvascular network 1 (MVN1) and MVN2 for the reference simulation, i.e. without any dilation. Folder <em>Dilated</em> contains the simulation results mimicking the four pericyte ablation scenarios. Subfolders <em>dc_x.x</em> contain the simulation results for the different diameter changes. Note that, folder <em>dc_0.0</em> contains no new simulation results but is a dummy folder containing the information about the vessels to be dilated for the different dilation scenarios (namely edge attribute: <em>toDilate</em> and <em>base_capillary</em>). </p> <p> </p> <p><strong>Reproducing figure 8:</strong></p> <p>Panels a-c: created by illustrating the simulation results with the open source software Paraview (v5.7.0).<br> Panels d-f & h: can be generated by executing make_all_figures.py in Python 2.7 within the provided folder structure.<br> Panel g: can be generated by executing make_figure_8g.py after installation of the the vgm-framework (further information see below). </p> <p><br> Output: All created Figures are saved in the folder <em>Figures</em>. The associated source data is available in Excel format in the folder <em>SourceData</em>.</p> <p> </p> <p><strong>Edge attributes:</strong></p> <p>diameter: vessel diameter [µm]<br> mainAV: 1 if ascending venule main branch, 0 otherwise<br> connectivity: vertex tuple to define location of edge<br> flow: flow rate [µm<sup>3</sup>/ms]<br> mainDA: 1 if descending arteriole main branch, 0 otherwise<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> htt: tube hematocrit [-]<br> toDilate: 1 if vessel is dilated for the current dilation scenario, 0 otherwise<br> base_capillary: 1 if vessel is the base capillary of the current dilation scenario, 0 otherwise</p> <p> </p> <p><strong>Vertex attributes:</strong></p> <p>index: vertex index<br> pressure: pressure [mmHg]<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> coords: vertex coordinates x,y,z [µm]<br> pBC: pressure boundary conditions at inflow vertices [mmHg], None at internal nodes</p> <p> </p> <p><strong>Obtaining simulation results:</strong><br> General:</p> <ul> <li>Running bi-phasic blood flow simulations requires setting-up the vgm-framework available at: <a href="https://github.com/Franculino/vgm.git">https://github.com/Franculino/vgm.git</a> (v.1.0).</li> <li>vgm is written in Python 2.7 and builds on standard python libraries.</li> <li>vgm has been used on macOS, Ubuntu and Windows Systems.</li> <li>Installation time < 5min. Further details available within the vgm README.</li> <li>Runtime depends on the network size, the chosen blood flow model and the initial conditions (e.g. ~8hrs for a Restart simulation of MVN1 with the bi-phasic blood flow model, see Restarty.py).</li> <li>scripts/Test.py provides an example how a simulation can be initiated. A Demo case is provided (details see below).</li> <li>Output: sampledict_BackUp_xx.pkl</li> <li>The bi-phasic blood flow model can be applied on all kind of microvascular graphs.</li> </ul> <p>Specific for current application:</p> <ul> <li>Simulations are a restart on the statistical steady state of the baseline cases.</li> <li>All relevant pre-processing functions for the current study are available in scripts/find_stroke_locations.py. Further details are available from the definition of the different functions.</li> <li>The simulations are initiated with scripts/Restart.py.</li> <li>To obtain the time-averaged simulation results scripts/01_put_together_sampledicts.py and scripts/02_convergenceDiscrete.py need to be executed. This results in the file G_averaged.pkl that is used for further analyses.</li> </ul> <p>Demo:</p> <ul> <li>Contains a small hexagonal microvascular network to test the code.</li> <li>1) Run Test.py to start the simulation</li> <li>2) Run 01_put_together_sampledicts.py</li> <li>3) Run 02_convergenceDiscrete.py to obtain time-averaged results (<em>G_averaged.pkl</em>)</li> </ul>
Results of the experiments within the Discrete Manufacturing Simulation Environment
<p>This dataset is a MySQL file and contains the Results of the experiments within the Discrete Manufacturing Simulation Environment.<br> <br> </p> <p>How do you use the files?<br> You need a MySQL instance on your computer (e.g. via XAMPP). If you do not already have one, create a database there with the name: "rlskipping". The command for this is: </p> <blockquote> <p>CREATE DATABASE IF NOT EXISTS rlskipping;</p> </blockquote> <p>On Windows:</p> <p> 1) Open a new CMD-Shell.<br> 2) Navigate to the path where the sql file is located.<br> (e.g.: cd C:\Users\MrScience\Desktop\Work\myProject\Paper\SQL)<br> 3) Import the Files.<br> (e.g.: mysql -u root -p rlskipping < evaluierung.sql)<br> (e.g.: mysql -u root -p rlskipping < calculatedsamples.sql)</p>
LISFLOOD-FP 8.1: New GPU accelerated solvers for faster fluvial/pluvial flood simulations - simulation results
<p>Simulation result data for Sharifian et al. (2022) for the following case studies:</p> <p>1- Lower Triangle catchment</p> <p>2- Upper Lee catchment</p> <p>3- Eden catchment</p> <p>4- Glasgow urban area</p> <p>5- Cockermouth urban area</p>
Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"
<p>Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"</p> <p>Yuhang Zhang1, Aizhong Ye1*, Phu Nguyen2, Bita Analui2, Soroosh Sorooshian2, Kuolin Hsu2</p> <p>1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.</p> <p>2 Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA.</p> <p>## Dataset </p> <p>Streamflow simulations from one observed precipitation (CMA) and three satellite precipitation products (PDIR, IMERG-F, and GSMaP) for 522 sub-basins.</p> <p>- Q-CMA (streamflow reference)<br> - Q-PDIR (uncorrected)<br> - Q-IMERGF (uncorrected)<br> - Q-GSMAP (uncorrected)</p> <p>### Data structure</p> <p>- Head section (row1-row5)<br> - SubNO: 522 <br> - BeginT: 2003-01-01 00:00 <br> - EndT: 2019-12-31 00:00 <br> - Interval: 1440s (daily)<br> - Revise: 10 (scaling factor to keep int datatype)<br> - Point1 Point2 ... (Subbasin No.)<br> - Data section<br> - 6209 rows, 522 cols</p> <p>## Results</p> <p>Two post-processing model results for test period (2015-1-1 to 2018-12-31).</p> <p>### Data structure</p> <p>- 1462 rows, every row denotes each day from 2015-1-1 to 2018-12-31</p> <p>- 100 columns, every column denotes each quantile from 0.005 to 0.995, total 100 quantiles.</p> <p>### qrf-output</p> <p>- pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs)</p> <p>### lstm-output</p> <p>- pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs)</p> <p> </p>
MD Simulation data, results and scripts for "Simulating the Skin Permeation Process of Ionizable Molecules"
<p>Input and output data from MD simulations in the "Simulating the Skin Permeation Process of Ionizable Molecules" article.</p> <p>The data of the uncharged permeants were already published in Lundborg et al. <br>Skin permeability prediction with MD simulation sampling spatial and alchemical reaction coordinates. Biophys. J. 2022, 121, 3837-3849.</p> <p>Also includes scripts to rerun the analyses.</p>
Transport and distribution of sodium ions in Mercury's magnetosphere: results from multi-fluid MHD simulations
<p>This dataset contains VTK files of the four simulations that are presented in the paper<em> </em><a href="https://essopenarchive.org/doi/full/10.22541/essoar.171629607.76912814/v1">Transport and distribution of sodium ions in Mercury’s magnetosphere: results from multi-fluid MHD simulations</a> .</p> <p>The physical parameters for each of the simulations are summarized below:</p> <div> <div>Sim-1: IMF = [0,0,-8.5] nT, V_sw = [-400,0,0] km/s</div> <div>Sim-2: IMF = [0,0,-8.5] nT, V_sw = [-400,0,0] km/s</div> <div>Sim-3: IMF = [0,0,-8.5] nT, V_sw = [-400,0,0] km/s</div> <div>Sim-4: IMF = [-15.2,8.4,-8.5] nT, V_sw = [-400,50,0] km/s</div> <div> <div> </div> <div>The Hall term is switched off in sim-2, but switched on in the rest of the simulations. The sodium source is used in all the simulations except for sim-3. In all the simulations, the solar wind density is 40 amu/cc with a temperature of 7.5 eV. We refer the readers to the aforementioned paper for more details.</div> </div> </div> <p>These VTK files can be opened with Paraview.</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.