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567 results for “Simulated datasets”
LiftWEC deliverable 3.5 - Dataset from extreme wave impact simulations on LiftWEC rotor using a high-fidelity RANS model
<p>This dataset contains numerical simulation results obtained from extreme wave impact in the LiftWEC rotor obtained using the high-fidelity RANS model employed in the LiftWEC project. An elaborate description of the numerical simulation setup as well as the discussion of the obtained results is available in LiftWEC deliverable D3.5 Extreme Load Analysis Report, also available in the LiftWEC zenodo-community or via the project website liftwec.com.</p> <p>The RANS-based investigation looked into maximum forces obtained using different extreme wave generation mechanisms as well as for different configurations of rotor and foil angle and rotor submergence. The dataset features images of different flow field variables throughout the simulation and time series of obtained forces in horizontal and vertical direction. In combination with the report, the datasets should allow to replicate all investigated design wave cases.</p>
Dataset for the publication: User experience, game satisfaction and engagement with the virtual simulation VR FestLab for alcohol prevention: A quantitative analysis among Danish adolescents
<p>Anonymized dataset for the publication: User experience, game satisfaction and engagement with the virtual simulation VR FestLab for alcohol prevention: A quantitative analysis among Danish adolescents</p>
Dataset for Electron heating associated with magnetic reconnection in foreshock waves: particle-in-cell simulation analysis
<p>The dataset is the simulation data included in the paper of "Electron heating associated with magnetic reconnection in foreshock waves: particle-in-cell simulation analysis".</p> <p>pic_data....zip (4 files) contain PIC simulation data in the binary format.<br> pic_data_x120-180_y-30-0_part1.zip and pic_data_x120-180_y-30-0_part2.zip are for the sub-domain of x=120~180 di, y=-30~0 di<br> pic_data_x120-180_y30-60_part1.zip and pic_data_x120-180_y30-60_part2.zip are for x=120~180 di, y=30~60 di.<br> For each file, the prefix (e.g., bx, ey, ne, etc.) indicates the quantity. The number in the suffix indicates the time step in units of omega_pe^-1, where 3733 omega_pe^-1 corresponds to 0.5 omega_ci^-1. <br> Each file contains float data arrays with a size of 6272x3136x1, corresponding to x-y dimensions. Data can be read by softwares like IDL, python, Matlab, etc, using the standard data reading method.</p> <p>fermi_dat.zip contains data for the Fermi decomposition calculations shown in Figure 8. Each file is for one event. The 3 columns are for time (twci), Fermi_xy, and Fermi_z. Fermi_xy and Fermi_z are for the unit area, and the total Fermi term is equal to Fermi_xy+Fermi_z.</p> <p>je_te_dat.zip contains data for Te and je.E decomposition, used to produce Figures 4 and 6. Each file is for one event, where the region is shown in the filename. The columns are:</p> <p>time (in twci),</p> <p>number of X-lines in the region,</p> <p>total je.E (integrated over the area),</p> <p>jepara.Epara,</p> <p>jeperp.Eperp,</p> <p>sum of the perpendicular jeperp.Eperp decompositions (ideally should be equal to jeperp.Eperp value),</p> <p>Fermi,</p> <p>Betatron,</p> <p>magnetization,</p> <p>demagnetized (jeperp.Eperp in regions with K<1),</p> <p>average Te over the area,</p> <p>delta_Te/miVA^2 where delta_Te is the average Te over the area subtracting the minimum Te in the area, and VA is based on the average |B| and n in the area</p> <p>delta_Te/miVA^2, where delta_Te is the same as above, and VA is based on the magnetic field amplitude in the x-y plane</p> <p>average Te over 2di x 2di surrounding X-line</p> <p>delta_Te/miVA^2, where delta_Te is the average Te over 2dix2di subtracting the minimum Te in the area, and VA is based on average |B| and n in the area</p> <p>average Te over 2dex2de surrounding X-line</p> <p>delta_Te/miVA^2, where dleta_Te is the average Te over 2dex2de subtracting the minimum Te in the area, and VA is based on the magnetic field amplitude in the x-y plane</p> <p>delta_Te/miVA^2, where dleta_Te is the average Te over 2dex2de subtracting the minimum Te in the area, and VA is based on the average |B| and n in the area</p> <p>average |B| in the area</p> <p>average magnetic field amplitude in the x-y plane</p> <p>average density n</p> <p>minimum Te in the area</p> <p> </p> <p>je_te_norx_dat.zip contains Te and je.E data for non-reconnection current sheets. The format is the same with je_te_dat.zip</p> <p>thickness_data_plots.zip contains plots for individual current sheets at the time with a minimum thickness. The filename contains information about the current sheet, for example:</p> <p>cs_te_twci15.5_x45.292_y8.184_dte-1.07_d3.2_dve6.1_jm0.23.png</p> <p>means it is at the time twci=15.5, the X-line location is at x=45.292di, y=8.184 di, delta_Te/miVA^2 is -1.07%, where we take a cut along N across the X-line, delta_Te is the difference between the average Te in the current sheet and that just outside of the current sheet, and VA is based on the inflow parameters outside of the current sheets on both sides of the current sheet. The thickness of the current sheet is 3.2 de. The VeL shear flow at the edges of the current sheet is 6.1 VA, and the maximum jz is 0.23. The thickness information is used for plotting Figure 10.</p> <p> </p> <p> </p>
Dataset of "Parameter regimes of hemispherical dynamo waves in a spherical shell from 3D MHD simulations" submitting to JGR: Planets
<p>Simulation data using Magic code. In the "general properties" part, basic properties (e.g., Pm, Rm, Rol, wave frequency, regime types....) are compressed into 3 python numpy .npz files, according to different Ekman number. For the regime types, we use 0 for failed dynamos, 1 for dipoles, 2 for global waves, 3 for hemispherical waves and 4 for multipolar solutions. In the "cases" part, we include raw output from Magic code for certain cases shown in the article.</p>
Dataset of Larval dispersal simulations of marine benthic invertebrates around New Zealand
<p>Dataset generated from biophysical simulations modelling larval dispersal around New Zealand with OpenDrift and the Moana Hindcast Backbone from 15 different locations and 4 different maximum PLD lengths. </p> <p>Within each file, the first 2 columns are a particles starting co-ordinates, columns 3 and 4 are ending co-ordinates, column 5 was state at the end of simulations (contact Charles Michie for more information), and column 6 is the origin.</p> <p>Abbreviations for locations: LMR - Leigh Marine Reserve, CTC - Cathedral Cove, RKK - Rongokako, CST - Castlepoint, ISB - Island Bay, KAI - Kaikoura, SPO - Seal Point, HMB - Half Moon Bay, DBS - Doubtful Sound, JSB - Jackson Bay, CFW - Cape Foulwind, OKB - Okiwi Bay, CPE - Cape Egmont, PIH - Piha Beach, CPR - Cape Reinga (also called FRN - Far North elsewhere).</p> <p>Data will accompany the publication "Variation in the dispersal of marine invertebrate larvae around coastal New ZealandVariation in the dispersal of marine invertebrate larvae around coastal New Zealand" </p>
An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation (dataset)
<p>5 subjects (4 PD and 1 ET) IMU and sEMG raw data supporting the conclusions of the article "An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation"</p> <p>name standard:<br>pacientnumber_age_sex_disease_arm_timesincediagnose.mat</p> <p>Xs, Xs1 ... Xs17 are the time vectors for sEMG and IMU. </p> <p> </p> <p><strong><em>If you use any part of this data for your research, please cite our paper:</em></strong></p> <pre>@article{pinheiro2024opensim, title={An OpenSim-based closed-loop biomechanical wrist model for subject-specific pathological tremor simulation}, author={Pinheiro, Wellington C and Ferraz, Henrique B and Castro, Maria Claudia F and Menegaldo, Luciano L}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, year={2024}, publisher={IEEE} }</pre> <p> </p> <p>Contact: wellington@peb.ufrj.br</p>
Simulation dataset for: "The Leu/Val6.51 side chain of cannabinoid receptors regulates the binding mode of the alkyl chain of ∆9-tetrahydrocannabinol"
<p>This dataset contains the prepared systems and the input parameters necessary to guarantee reproducibility of the molecular dynamics simulations of "The Leu/Val6.51 side chain of cannabinoid receptors regulates the binding mode of the alkyl chain of ∆9-tetrahydrocannabinol" work.</p>
Stylized Californian EOM simulation dataset
<p>These files are datasets for ANTIGONE, a model developed at EDF R&D to study long-run stakes of electricity market design (<a href="https://github.com/alexis-lebeau-research/ANTIGONE.git">available on GitHub</a>).</p> <p>This dataset is documented and used in:</p> <ul> <li>Lebeau et al. 2023 Long-Term Issues with the Energy-Only Market Design in the Context of Deep Decarbonization</li> </ul> <p>Files description:</p> <ul> <li>D-1_20y.zip is input folder for the Generation Expansion Planning Model</li> <li>EOM.zip contains the different dataset for market simulations (reference case and different sensitivities)</li> </ul> <p>Feel free to contact me for complementary information or to discuss this research work more generally.</p>
Simulated datasets for comparsion between fastBCR and other SOTA methods.
<p>Simulated datasets for comparison between fastBCR and other state-of-the-art (SOTA) BCR clonal family inferencing methods. The simulation process began by generating an ancestor cell through V(D)J random recombinant and simulated the process of antigen activation that led to multiple rounds of proliferation, mutation in the junction region and elimination, ultimately resulting in the formation of a B cell clonal family. We set a baseline mutation rate of approximately 10<sup>−3</sup> mutations per base pair per cell division, as reported in previous studies (Kleinstein, S. H., Louzoun, Y. & Shlomchik, M. J. Estimating hypermutation rates from clonal tree data. The Journal of Immunology 171, 4639-4649 (2003). ). To account for the impact of low cell capture efficiency and potential sequencing errors, we further set three higher mutation rates (0.002, 0.005, and 0.01) in the simulation process. To investigate the performance of different methods across varying clonal family densities and mutation rates, we employed the above framework to generate simulated datasets comprising 10, 30, 50 and 100 clonal families. Additionally, each dataset was augmented with 20,000 random simulated ancestor sequences as noises.</p>
Simulation dataset for "HORNET - A Power Density Quantifying Out of Thermodynamic Equilibrium Energy Conversion"
<p>Simulation dataset for reproducing figures used in the manuscript</p>
Dataset for the publication: Factors associated with risky drinking decisions in a virtual reality alcohol prevention simulation: A structural equation model
<p>This dataset includes the data from the publication <em>Factors associated with risky drinking decisions in a virtual reality alcohol prevention simulation: A structural equation model. </em>The first table sheet lists the data of the variables; the second table sheet describes the coding of the variables. </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>
CMS High Granularity Calorimeter Trigger Cell Simulated Dataset (Part 2)
<p>See <a href="https://doi.org/10.5281/zenodo.8338607">https://doi.org/10.5281/zenodo.8338607</a> for a full description of this dataset.</p>
Modified LRphase and Simulated Dataset for "LRphase: an efficient algorithm for assigning haplotypic identity to long reads"
<p><strong>A modified version of LRphase was used to simulate reads for a single hypothetical human genome with maternal and paternal phasing information. Briefly, haplotype-specific reference sequences were generated with `bcftools consensus` <a href="https://paperpile.com/c/VO12CC/SIk9">(Li 2011)</a> based on the rescued GIAB VCF and hg38 human reference sequence. Each haplotype-specific fasta was fed separately into pbsim2 <a href="https://paperpile.com/c/VO12CC/NMmE">(Ono, Asai, and Hamada 2021)</a> as the reference from which simulated reads were randomly drawn, up to 1X coverage. Parameters controlling the read length distribution, sequencing, and base calling error rates were set to emulate typical performance of the MinIon sequencing platform with flow cell version R10.4.1 (https://nanoporetech.com/products/minion). These are as follows: `--depth 1 –hmm_model R103.model --difference-ratio '23:31:46' --length-mean 25000 --length-min 100 --length-max 1000000 –length-sd 20000 --accuracy-mean 0.98 --accuracy-min 0.01 --accuracy-max 1.00`. Simulated reads were aligned to the hg38 reference genome with minimap2 <a href="https://paperpile.com/c/VO12CC/Y2HP">(Li 2018)</a> and correct phasing and alignment coordinates were encoded in the read names. Finally, samtools <a href="https://paperpile.com/c/VO12CC/YLss">(Li et al. 2009)</a> was used to remove duplicated and supplementary reads, and concatenate, sort, and index reads into a single combined bam file. Of 258,539 total reads, 246,210 were mappable, and 178,504 overlapped at least one heterozygous variant in HG001.</strong></p>
Evaluating the performance of probabilistic algorithms for phylogenetic analysis of big morphological datasets: a simulation study
Open the record for dataset details and reuse information.
Simulated dataset from 'Quantifying the causal pathways contributing to natural selection'
Open the record for dataset details and reuse information.
Estimating heritability in honeybees: comparison of three major methods based on empirical and simulated datasets
Open the record for dataset details and reuse information.
Dataset of REFIR and NAME simulations of the Eyjafjallajokull 2010 eruption
<p>This data set includes supporting data for the manuscript "The impact of eruption source parameter uncertainties on ash dispersion forecasts during explosive volcanic eruptions" submitted to Journal of Geophysical Research - Atmospheres.</p> <p>The data sets consists of 3 folders: elaborated_data, NAME_simulations and REFIR_simulations.</p> <p>1) elaborated_data. This folder includes data mainly used to compare eruption source parameters time series produced by REFIR in the four simulation configurations (all models, Mastin only, only wind models, only empirical models). Specifically:</p> <ul> <li>radar_iskef.xlsx. Time series of the Eyjafjallajokull top plume height (maximum and minimum estimate) retrieved from the C-band radar in Keflavík, Iceland. The time series is limited to the period 5/05/2010 (00:00 UTC) to 8/05/2010 (23:55 UTC). The original time series is stored in the REFIR simulation folders as radar_iskef.txt.</li> <li>PLH.txt. Time series f the Eyjafjallajokull top plume height (average, maximum and minimum solutions) as assessed by REFIR.</li> <li>cases_comparisons_FMER.txt. Time series of the final estimate of Mass Eruption Rate (FMER) for the four configurations and calculations for comparisons discussed in the manuscript.</li> <li>Weather_data_summary.xlsx. Time series of the weather parameters assessed by REFIR for the three estimates of the top plume heights.</li> </ul> <p>2) NAME_simulations. This folders includes results (both raw data and plots) of NAME simulations for the four simulation configurations and for each estimate of the eruption source parameters (average, maximum and minimum).</p> <p>3) REFIR_simulations. This folders includes input data and results (both raw data and plots) of REFIR simulations for the four simulation configurations. The input data include the original and elaborated ERA-Interim weather data for the period 5-8 May 2010 in the folder "raw_reanalysis_weather_data_20100505".</p>
CESM coupled simulation dataset with new (Mod_cp) and original (F09) dynamical coupling scheme from atmosphere, ocean and ice.
<p>1. Coupled models simulate air-sea interaction by coupling components models together. However, in state-of-the-art models such as CESM, the atmospheric model contains dynamic core and physic parameterization that are not differentiated during air-sea interaction. The dynamics-physics (D-P) coupling in CAM causes the prognostic winds of the dynamic core be interpolated onto non-staggered locations. We propose a new dynamical coupling scheme (denoted as Mod_cp) that eliminates the extra interpolation during D-P coupling for the atmosphere-ocean interaction, reducing the numerical diffusion during the dynamics-physics coupling for air-sea interactions.</p> <p>2. Using new and original dynamical coupling scheme, two coupled simulations (100 years) were conducted. Climatology of atmosphere (cam), ocean (ocn) and sea ice (ice) model simulations are archived here (last 50 years). The resolution is around 1 latitude x 1 longitude.</p> <p>3. B1850_mod_cp denotes the simulation with new dynamical coupling scheme, B1850_f09_g16 denotes the simulation with original scheme.</p>
Codes and datasets associated with the paper "Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers"
<p>From here, you will find some of the codes, simulation data in the article: </p> <p>Dai, Y., Basu, S., Maronga, B. and de Roode, S.R., 2020. Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers. <em>arXiv preprint arXiv:2003.09463</em>.</p> <p><strong>Simulation results from DALES: </strong></p> <p>within the folder of DALES, D52 denotes default scheme of Deardorff, H52 denotes the revised Deardorff scheme. Numbers of each fold indicates the grid number in each direction. </p> <p><strong>Simulation results from MATLES:</strong></p> <p>file name: MATLES/variable.out</p> <p>Example: MATLES/aT.out, the potential temperature data (2D, time and height) from MATLES</p> <p><strong>Python code for plotting: </strong></p> <p>dalesfunc.py is the function file used to process data</p> <p>DALES_plotting.py is the code file used for plotting the simulation results from DALES and MATLES</p> <p><strong>DALES input:</strong></p> <p>namoptions in each folder is the file for DALES options input</p>
ScienceDex guides
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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.