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Dataset results
567 results for “Simulated datasets”
Datasets for the comparison between POC estimated from BGC-Argo floats and PISCES model simulations
<p>This dataset is composed of two main folders.</p> <p><strong>clim_3D</strong>: contains 4 files in 3 directories:</p> <ul> <li>BGC-Argo/bbp700_nemo_clim.nc: global monthly climatology of BGC-Argo b<sub>bp700</sub> measurements on the ORCA2_L31 NEMO grid.</li> <li>BGC-Argo/bbp700_nemo_climseas.nc: seasonal climatology (JFM, ...OND) of BGC-Argo b<sub>bp700</sub> measurements on the ORCA2_L31 NEMO grid.</li> <li>biomes/nemobiomes.nc: biomes of Fay and McKinley 2014 (ESSD) reprojected onto the ORCA2_L31 NEMO grid.</li> <li>PISCES/PISCES_1m_19600101_19601231_ptrc_T.nc: PISCES tracers (living organisms and detrial organic carbon), monthly climatology based on pre-industrial simulation described by Aumont et al. 2017 (Biogeosciences).</li> </ul> <p><strong>CATS_1D:</strong> contains 66 folders. The folders are named as <fwmo>_<yyyy>_<orca1cell>, where</p> <ul> <li>fwmo = World Meteorological Organization (WMO) float number</li> <li>yyyy = year</li> <li>orca1cell = number of the NEMO model horizontal grid cell (ORCA1 grid) used to run the PISCES 1D offline simulation.</li> </ul> <p>Each folder contains:</p> <ul> <li>BGC-Argo observations for a given Argo float and year, bined onto the model vertical grid and 5-day peiods: 'Mprof*.nc'</li> <li>Dynamical fields used to run PISCES 1D offline simulations: 'dyna_grid_T.nc'</li> <li>Output of the simulations: PISCES tracers at 5-day resolution: 'PISCES_5d_*_ptrc_T.nc'</li> <li>List of horizontal model grid cells that were visited by the Argo float on a given year: 'xymatch_<fwmo>.csv'. If the list is queried for the grid cell number <orca1cell>, one can obtain the corresponding longitude (xmap), latitude (ymap), and the x and y indices of the grid (plus/minus 1). These indices correspond to the 3x3 horizontal grid of the dynamical fields and the PISCES 1D output.</li> </ul> <p> </p> <ul> </ul>
Dataset of Simulations for LBW and PBF processes
<p>This dataset has been created by TU WIEN and belongs to Work Package 2 about Simulations for LBW and PBF processes.</p> <p>© COPYRIGHT 2021 The CUSTODIAN Consortium. All rights reserved.</p> <p><strong>Table of contents including supplementary information:</strong></p> <ul> <li>LBW_G11.png - <em>Comparative image of cross and longitudinal section of simulation and experiment of LBW; primary beam only; conditions G11 (details given in image).</em></li> <li>LBW_G11_opt.png - <em>Comparative image of cross and longitudinal section of simulation and experiment of LBW; primary and secondary beam; conditions G11 (details given in image).</em></li> <li>LBW_G7.mp4 - <em>Comparative video: high speed camera recording (top) and simulation result (bottom) of LBW; conditions G7: P = 4kW, gap width = 0.3mm, welding velocity = 100mm/s; primary beam only.</em></li> <li>LBW_G7_GrainGrowth.mp4 - <em>Video showing coupled simulation of melting, solidification, and crystallographic grain growth; conditions G7; primary beam only.</em></li> <li>LBW_G9.png - <em>Comparative image of cross and longitudinal section of simulation and experiment of LBW; primary beam only; conditions G9 (details given in image).</em></li> <li>LBW_G9_opt.png - <em>Comparative image of cross and longitudinal section of simulation and experiment of LBW; primary and secondary beam; conditions G9 (details given in image).</em></li> <li>PBF_CM247LC_multitrack.mp4 - <em>Video of simulation showing 6 tracks of PBF-LB/M of CM247LC on a 100μm powder bed; bidirectional scanning path; substrate coloured light grey, powder coloured dark grey, meltpool coloured by temperature.</em></li> <li>PBF_CM247LC_validation.png - <em>Image showing comparison of cross-sectional weld bead obtained experimentally and in simulation for a single track of CM247LC scanned on 100μm powder bed.</em></li> <li>PBF_IN713LC_balling.mp4 - <em>Video of simulation of PBF-LB/M of IN713LC where insufficient heat input (452W) leads to surface energy-driven defect called balling; accompanying image of experimental result on the right</em></li> <li>PBF_IN713LC_Q5plus.png -<em> Image of simulation and experimental result of PBF-LB/M of IN713LC with sufficient heat input to prevent balling (520W).</em></li> <li>PBF_IN713LC_validation.png -<em> Image showing comparison of cross-sectional weld bead obtained experimentally and in simulation for a single track of IN713LC scanned on 50μm powder bed.</em></li> </ul>
Dataset from "Numerical gravitational backreaction on cosmic string loops from simulation"
<p>This file contains a data table representing the average power spectrum, P_n, of Nambu-Goto cosmic strings evolved under numerical gravitational backreaction. The power spectra and the methods used to produce them are reported on in "Numerical gravitational backreaction on cosmic string loops from simulation" [to appear], by the same authors as this dataset. See Fig. 5 of that paper for a visualization.</p> <p>The file is organized in three columns:</p> <ol> <li>The fraction of evaporation, chi. The range is from 0.0 to 0.7 in steps of 0.1.</li> <li>The mode number, n. The range is from 2^0 to 2^39 in multiplicative steps of 2.</li> <li>The logarithmically binned elements of the power spectrum, nP_n. Bin edges are 2^i to 2^(i+1)-1 for i from 0 to 39.</li> </ol>
Dataset for Liu et al. (2022), "Convection and Clouds under Different Planetary Gravities Simulated by a Small-domain Cloud-resolving Model"
<p>Dataset for Liu et al. (2022), "Convection and Clouds under Different Planetary Gravities Simulated by a Small-domain Cloud-resolving Model".</p> <p> </p>
Dataset for "Effective simulations of interacting droplets"
<p>This repository contains the dataset for the numerical simulations reported in the paper "Effective simulations of interacting droplets".</p>
Simulated viral genomes dataset (TargetCall-D5)
<p>Detailed description:</p> <p>The fast5 files in this dataset is generated using Deep Simulator for 7 different viral genomes.</p> <p>This dataset includes 35K fast5 files in total.</p>
Dataset for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme"
<p>Datasets for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme". The global simulation outputs (climatologies and daily anomalies), calculated RMM indexes, and the results of the budget analysis are included.</p>
Dataset for "Energization of Cold Protons and Helium Ions by EMIC Waves in the Inner Magnetosphere: Hybrid Simulations"
<p>* Reduced datasets for figures in the manuscript.</p> <p>* Code for producing the simulation data.</p>
The observed and simulated datasets of "21·7" Henan extremely heavy rainfall event
<p>The uploaded files are the observed and simulated datasets of “21·7” Henan extremely heavy rainfall event, including OTT disdrometers, Radar, national and regional gauges, and WRFOUT files. </p>
Dataset of CCFs, dispersion data, synthetic ambient noise data, and rupture simulation data in the Anninghe study
<p>This is the main dataset supporting the Anninghe paper.</p> <p>sta.loc represents the station list file.<br> 3DVelocityModel folder contains the final velocity model file Anninghe_Vs3D.dat.<br> CCFs_Original folder contains the original CCFs obtained from the Anninghe array.<br> CCFs_Separated folder contains the CCFs after model separation. In CCFs_Separated folder, the merge folder is the final average CCFs used for later tomography.<br> DisperData folder contains the seimiautopicked dispersion data from the separated CCFs.<br> RuptureSimulation folder contains the rupture simulation data in the paper.<br> SynNoiseData folder contains the CCFs calculated from synthetic ambient noise data.<br> </p>
Datasets for numerical Monte Carlo simulations of Diclofenac bio-degradation in a soil-water system
<p>This record contains files and main instructions to repeat the numerical simulations of Diclofenac bio-degradation is a soil-water system. The text file Readme.docx contains a description of the overall content of this record, which is organized in two separate folders.</p>
Simulation dataset for "Quantifying Energy Conversion in Higher Order Phase Space Density Moments in Plasmas"
<p>Simulation dataset for the simulation figure used in the paper</p>
Dataset for "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites"
<p>SCAM5 and LES outputs from "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites". Includes simulation outputs of stochastic UNICON and original UNICON. The LES intercomparison data of DYCOMSRF01 is available at https://gcss-dime.giss.nasa.gov/pub/DYCOMS-II/GCSS7-RF01/gcss7.nc, and the data of CGILS is available at http://www.atmos.washington.edu/~bloss/CGILS2data.tar.</p>
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics [Dataset]
<p>Supplementary data for 'Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics'. </p>
Dataset of "Divergent convective outflow in large eddy simulations "
<p>See README.txt</p> <p>See download_all_simulations.sh</p>
Dataset: Out-of-field measurements and simulations of a proton pencil beam in a wide range of dose rates using a Timepix3 detector: Dose rate, flux and LET
<p>Open data set for Out-of-field measurements and simulations of a proton pencil beam in a wide range of dose rates using a Timepix3 detector: Dose rate, flux and LET</p>
Simulated datasets used for assessing the performance of StrainIQ software
<p>StrainIQ (Strain Identification and Quantification) is a novel tool that implements a new <em>n</em>-gram based algorithm for predicting and quantifying strain-level taxa from whole genome metagenomic sequencing data. We used ten simulated dataset ( from GI tract reference genomes) to measure the sensitivity and specificity of StrainIQ software in strain identification.</p> <p>Please find the new link for this data at https://zenodo.org/record/8132164</p>
Simulated metagenomic datasets used for determining the threshold score cutoff for taxa prediction using StrainIQ software
<p>StrainIQ (Strain Identification and Quantification) is a novel tool that implements a new <em>n</em>-gram based algorithm for predicting and quantifying strain-level taxa from whole genome metagenomic sequencing data. To avoid the false positive prediction of by StrainIQ algorithm, we determined cutoff score for each site specific n-gram model called DSEM (DNA Signature Element Model) using positive and negative metagenomic datasets. The Figure2-datasets.zip has positive (from GI tract genomes) and negative (from non-GI tract genomes) simulated metagenomic datasets that were used for determining optimal cut-off based on GI tract DSEM.</p> <p>Please find the new link for this data at https://zenodo.org/record/8132164</p>
SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs
<p>SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs.</p>
SRDTrans dataset: simulated calcium imaging data at different imaging speeds
<p>SRDTrans dataset: simulated calcium imaging data at different imaging speeds</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.