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567 results for “Simulated datasets”
Dataset and Software: Heat Transfer in Pyroclastic Density Current - Ice Interactions: Insights from Experimental and Numerical Simulations
<p>This repository contains experiment data and executable MATLAB codes for the research article 'Heat Transfer in Pyroclastic Density Current - Ice Interactions: Insights from Experimental and Numerical Simulations'.</p> <p>Contained within the experiment data file Valeetal_Data.Zip are:</p> <ul> <li>Temperature, melt and steam data separated by particle type. See files: <ul> <li>Ballotini_Data_Valeetal</li> <li>Pumice_Data_Valeetal</li> <li>Ruapehu_Data_Valeetal</li> </ul> </li> <li>Melt and steam summary data plots. See file: <ul> <li>Data_Plots_Valeetal</li> </ul> </li> <li>Document with figures to demonstrate system behaviours. See file: <ul> <li>Valeetal_SI01_JGR</li> </ul> </li> </ul> <p>Contained within the model data file Model_Repository.zip are simulation data and codes that implement, process, and plot numerical solutions to the mathematical model. See README.txt for a brief description of each file.</p> <p> </p>
Dataset of OFES2 ensemble simulations for Impact of atmospheric wind on SST in the northwestern Pacific
<p>Dataset to plot figures and make tables in Sasaki et al., Impact of atmospheric wind on sea surface temperature in the Kuroshio-Oyashio confluence and Oyashio southward intrusion regions.</p>
Simulation dataset for "Statistics of Pressure Fluctuations in Turbulent Kinetic Plasmas"
<p>All the dataset used in the paper and the python codes used to plot the figures can be found here. </p>
Dataset used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence"
<p>This dataset repository includes input and output spatial data of urban microclimate simulations performed through QGIS and ENVI-met software used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence", published in the Building and Environment Journal, <a href="https://doi.org/10.1016/j.buildenv.2023.110854">https://doi.org/10.1016/j.buildenv.2023.110854</a>.</p>
CMS High Granularity Calorimeter Trigger Cell Simulated Dataset (Part 1)
<p>The dataset consists of simulated events of electron-positron pairs (<em>e</em><sup>+</sup><em>e</em><sup>−</sup>) with flat transverse momentum <em>p</em><sub>T</sub> distribution <em>p</em><sub>T</sub> ∈ [1,200] GeV, with Phase 2 conditions, 200 pileup, V11 geometry, HLT TDR Summer20 campaign The <a href="https://cmsweb.cern.ch/das/request?input=dataset%3D%2FDoubleElectron_FlatPt-1To100%2FPhase2HLTTDRSummer20ReRECOMiniAOD-PU200_111X_mcRun4_realistic_T15_v1-v2%2FGEN-SIM-DIGI-RAW-MINIAOD&instance=prod/global">original dataset (CMS-internal)</a>.</p> <p>This derived dataset in ROOT format contains generator-level particle and simulated detector information. More information about how the dataset is derived is available at this <a href="https://twiki.cern.ch/twiki/bin/viewauth/CMS/HGCALTriggerPrimitivesSimulation">TWiki (CMS-internal)</a>.</p> <p>A description of each variable is below.</p> <table> <thead> <tr> <th>Variable</th> <th>Description</th> <th>Type</th> </tr> </thead> <tbody> <tr> <td><code>run</code></td> <td>Run number</td> <td><code>int</code></td> </tr> <tr> <td><code>event</code></td> <td>Event number</td> <td><code>int</code></td> </tr> <tr> <td><code>lumi</code></td> <td>Luminosity section</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_n</code></td> <td>Number of primary generated particles</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_PUNumInt</code></td> <td>Number of pileup interactions</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_TrueNumInt</code></td> <td>Number of true interactions</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_x</code></td> <td>Simulated primary vertex <em>x</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_y</code></td> <td>Simulated primary vertex <em>y</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_z</code></td> <td>Simulated primary vertex <em>z</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>gen_eta</code></td> <td>Primary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_phi</code></td> <td>Primary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_pt</code></td> <td>Primary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_energy</code></td> <td>Primary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_charge</code></td> <td>Initial generated particle charge</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_pdgid</code></td> <td>Primary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_status</code></td> <td>Primary generated particle generator status</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_daughters</code></td> <td>Primary generated particle daughters (empty)</td> <td><code>vector<vector<int>></code></td> </tr> <tr> <td><code>genpart_eta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_phi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pt</code></td> <td>Primary and secondary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_energy</code></td> <td>Primary and secondary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvz</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovy</code></td> <td>Primary and secondary generated particle original vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovz</code></td> <td>Primary and secondary generated particle original vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_mother</code></td> <td>Primary and secondary generated particle parent particle index (-1 indicates no parent)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_exphi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exeta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_fbrem</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pid</code></td> <td>Primary and secondary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_gen</code></td> <td>Index of associated primary generated particle</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_reachedEE</code></td> <td>Primary and secondary generated particle flag: <code>2</code> indicates that the particle reached the HGCAL, <code>1</code> indicates the particle reached the barrel calorimeter, and <code>0</code> indicates other cases</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_fromBeamPipe</code></td> <td>Deprecated variable, always true</td> <td><code>vector<bool></code></td> </tr> <tr> <td><code>genpart_posx</code></td> <td>Primary and secondary generated particle position <em>x</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posy</code></td> <td>Primary and secondary generated particle position <em>y</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posz</code></td> <td>Primary and secondary generated particle position <em>z</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>ts_n</code></td> <td>Number of trigger sums</td> <td><code>int</code></td> </tr> <tr> <td><code>ts_id</code></td> <td>Trigger sum ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_subdet</code></td> <td>Trigger sum subdetector</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_zside</code></td> <td>Trigger sum endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_layer</code></td> <td>Trigger sum layer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafer</code></td> <td>Trigger sum wafer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafertype</code></td> <td>Trigger sum wafer type: 0 indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, 1 indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and 2 indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_data</code></td> <td>Trigger sum ADC value</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_pt</code></td> <td>Trigger sum transverse momentum in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_mipPt</code></td> <td>Trigger sum energy in units of transverse MIP</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_energy</code></td> <td>Trigger sum energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_eta</code></td> <td>Trigger sum pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_phi</code></td> <td>Trigger sum azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_x</code></td> <td>Trigger sum <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_y</code></td> <td>Trigger sum <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_z</code></td> <td>Trigger sum <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_n</code></td> <td>Number of trigger cells</td> <td><code>int</code></td> </tr> <tr> <td><code>tc_id</code></td> <td>Trigger cell unique ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_subdet</code></td> <td>Trigger cell subdetector ID (EE, EH silicon, or EH scintillator)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_zside</code></td> <td>Trigger cell endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_layer</code></td> <td>Trigger cell layer number</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferu</code></td> <td>Trigger cell wafer <em>u</em> coordinate; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferv</code></td> <td>Trigger cell wafer <em>v</em> coordinate; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_wafertype</code></td> <td>Trigger cell wafer type: <code>0</code> indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, <code>1</code> indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and <code>2</code> indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon)</td> <td> </td> </tr> <tr> <td><code>tc_cellu</code></td> <td>Trigger cell <em>u</em> coordinate within wafer; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_cellv</code></td> <td>Trigger cell <em>v</em> coordinate within wafer; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_data</code></td> <td>Trigger cell ADC data at 21-bit precision after decoding from 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_uncompressedCharge</code></td> <td>Trigger cell ADC data at full precision before compression</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_compressedCharge</code></td> <td>Trigger cell ADC data compressed into 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_pt</code></td> <td>Trigger cell transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_mipPt</code></td> <td>Trigger cell energy in units of transverse MIPs</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_energy</code></td> <td>Trigger cell energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_simenergy</code></td> <td>Trigger cell energy from simulated particles in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_eta</code></td> <td>Trigger cell pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_phi</code></td> <td>Trigger cell azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_x</code></td> <td>Trigger cell <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_y</code></td> <td>Trigger cell <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_z</code></td> <td>Trigger cell <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_cluster_id</code></td> <td>ID of the 2D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_id</code></td> <td>ID of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_pt</code></td> <td>Transverse momentum <em>p</em><sub>T</sub> in GeV of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<float></code></td> </tr> </tbody> </table>
Dataset for Effects of coaxial cables on high-voltage lightning impulse measured parameters: A comparative between measurements and simulations
<p>Dataset for publication named: "Effects of coaxial cables on high-voltage lightning impulse measured parameters: A comparative between measurements and simulations".</p>
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
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Simulation datasets for: Intense surface winds from gravity wave breaking in simulations of a destructive macroburst
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Simulated genotype data from: Effective population size estimation in large marine populations: Considering current challenges and opportunities when simulating large datasets with high-density genomic information
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Dataset for: Phosphorus mobilization from intact soil monoliths flooded under simulated summer versus spring snowmelt with intermittent freeze-thaw conditions
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BYTE Simulation Dataset from the B3GET Model (2021)
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COMSOL simulation & example datasets for: Nonuniform structural properties of wings confer sensing advantages
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Dataset for METAPOPGEN 2.0: a multi-locus genetic simulator to model populations of large size
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Data and code for: Generation and applications of simulated datasets to integrate social network and demographic analyses
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LES-DPM simulation dataset for oscillatory flow over a mobile, rippled bed
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NanoVar Simulation Datasets
<p>The data stored here were used for benchmarking long-read structural variant (SV) callers in the publication:</p> <p>Tham, CY., Tirado-Magallanes, R., Goh, Y. <em>et al.</em> NanoVar: accurate characterization of patients’ genomic structural variants using low-depth nanopore sequencing. <em>Genome Biol</em> <strong>21, </strong>56 (2020). https://doi.org/10.1186/s13059-020-01968-7</p> <p>The data consist of six simulated SV genomes generated using various open source tools and comprise also of simulated long-read FASTA files and ground truth SV information. More information can be found in the README.md.</p>
Simulation datasets for "Is Nightside Outflow Required to Induce Magntospheric Sawtooth Oscillations?"
<p>Processed simulation data for "Is Nightside Outflow Required to Induce Magntospheric Sawtooth Oscillations?"</p> <p>"OpenFlux_sawtooth.mat" is the time series of the integrated open magnetic flux in the cusp O+ outflow simulation.</p> <p>"tiltangle.mat" is the time evolution of magnetic inclination angle extracted at geostationary orbit for comparing the spatial extension of sawtooth substorms with observations from Cai et al. [2006].</p> <p> </p>
Dataset for Lattice Boltzmann simulation of water flow through rough nanopores
<p>All the datasets used to produce the figures in our paper "<strong>Lattice Boltzmann simulation of water flow through rough nanopores</strong>".</p>
The dataset of the manuscript "Numerical study of the initial condition and emission on simulating PM2.5 concentrations in Comprehensive Air Quality Model with extensions version 6.1 (CAMx v6.1): Taking Xi'an as example"
<ul> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/bcfile.rar?versionId=4909d094-5877-408e-bd4f-0c969c54e585">bcfile.rar</a>: the clean initial and boundary condition files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/Emis_forNov.rar?versionId=0a1e8b66-5157-4819-8c03-20fb7797d8ef">Emis_forNov.rar</a> and <a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/Emis_forDec.rar?versionId=d4db00f1-ec1b-4096-973e-6a87133e4eac">Emis_forDec.rar</a>: the emission files in November and December 2016.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/tuvfile.rar?versionId=5dcf0977-e416-466e-9089-bbf0726c788d">tuvfile.rar</a> and <a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/o3mapfile.rar?versionId=9c25cae9-f00e-4ad3-b4dc-7a721f7f44d7">o3mapfile.rar</a>: the photolysis files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.cp1.rar?versionId=09ded31b-4c42-4e40-a21c-0f18877e9e41">camx.cp[1-5].rar</a>: the results of sensitivity experiments for using clean initial condition files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.r1120p1.rar?versionId=45d6e209-8e66-43ed-bc0b-dc8af5521352">camx.r1120p[1-3].rar</a>: the results of sensitivity experiments for R1120.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.r1124.rar?versionId=c138e436-0416-4701-948d-ce761cf6c5cf">camx.r1124.rar</a>: the results of sensitivity experiments for R1124.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/contnuous_B12.rar?versionId=34485c43-77ac-4001-8a6d-a57b7ff821e3">contnuous_B12.rar</a>: the results of sensitivity experiments for CT12.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/contnuous_B24.rar?versionId=0f325a61-f19c-4bac-b8b4-7e229c332bf9">contnuous_B24.rar</a>: the results of sensitivity experiments for CT24.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/scripts.zip?versionId=b030444c-51a5-4673-b9d1-7e80ec42a3b9">scripts.zip</a>: all scripts covering every data processing action for all the results reported in the paper.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/data.zip?versionId=52917a53-fca7-4a72-ba2f-a2ce7593adc4">data.zip</a>: final data tables used to plot figures and tables.</li> </ul>
Datasets for reproduction of simulation study results presented in article "Network Aggregation to Enhance Results Derived from Multiple Analytics"
<p>The included datasets can be used to reproduce the simulation study results presented in the paper:<br> Duroux D., Climente-González H., Wienbrandt L., Van Steen K. (2020) Network Aggregation to Enhance Results Derived from Multiple Analytics. In: Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 583. Springer, Cham.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 813533.</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.