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567 results for “Simulated datasets”
Evaluation dataset for urban climate simulation
<p>The dataset was used in the following papers:</p> <p>(1) Li, Z., Zhou, Y., Wan, B., Chung, H., Huang, B., & Liu, B. (2019). Model evaluation of high-resolution urban climate simulations: using the WRF/Noah LSM/SLUCM model (Version 3.7. 1) as a case study. <em>Geoscientific Model Development</em>, <em>12</em>(11), 4571-4584.</p> <p>(2) Li, Z., Wan, B., Zhou, Y., & Wong, H. (2020). Incoming data quality control in high-resolution urban climate simulation: Hong Kong-Shenzhen area urban climate simulation as a case study using WRF/Noah LSM/SLUCM model (Version 3.7. 1). <em>Geoscientific Model Development Discussions</em>, 1-13.</p>
Simulated dataset from 'Quantifying the causal pathways contributing to natural selection'
<p>This dataset (Antechinus.csv) relates to the worked example in the appendix of the paper:</p> <p>Henshaw JM, Morrissey MB, Jones AG (2020). Quantifying the causal pathways contributing to natural selection. Evolution (doi:10.1111/evo.14091)</p> <p>The worked example concerns a hypothetical study of female antechinus. These are small carnivorous marsupials that use torpor to reduce energy consumption from late summer to early winter. They reproduce once per year in late winter or early spring, following which most individuals die. We suppose that researchers tracked female antechinus from mid summer to the end of the breeding season. They recorded the animals' body size, their date of last torpor, whether they survived to breed, their number of mates, and their fecundity. I simulated the dataset resulting from this hypothetical study in Wolfram Mathematica (see Methods below). In the above paper, we analyse the causal structure of natural selection in this dataset (the R code for the causal analysis is included here as 'AntechinusAnalysis.R').</p> <p>The variables in the dataset are: body size in unspecified units (BodySize); the date of last torpor (TorporDate), standardized as the number of days before/after an unspecificed reference date; whether the individual survived to breed, given as a binary variable (Survival); an individual's number of mates (Mates); and her fecundity (Fecundity).</p>
Dataset for "Strain induced electrochemical behaviours of ionic liquid electrolytes in an electric double layer capacitor: Insights from molecular dynamics simulations"
<p>The datafile contains molecular dynamics simulation results for analysing the electrochemical behaviour of ionic liquid based EDLC under compression and tension.</p>
Sample data of the 99 simulated brains dataset
<p>This archive is a supplement for the 99 simulated brains dataset <a href="http://dx.doi.org/10.6097/e230-20200511_1">http://dx.doi.org/10.6097/e230-20200511_1</a> and contains the data of one single sample subject created using MITK Diffusion [1]. It contains simulated MR images (T1, T2, dMRI) of 1 healthy subject as well as corresponding reference fiber tracts used for the MRI simulation. For more details, please refer to the full dataset [2]. If you are using this dataset in your work, please cite it and also cite the full dataset.</p> <p>[1] Neher, Peter. <em>MIC-DKFZ/MITK-Diffusion</em>. C++. 2019. Reprint, MIC-DKFZ, 2020. <a href="https://github.com/MIC-DKFZ/MITK-Diffusion">https://github.com/MIC-DKFZ/MITK-Diffusion</a>.</p> <p>[2] Neher, Peter, und Klaus Maier-Hein. „Simulated MRI Images and Reference Fiber Tracts of 99 Subjects.“ German Cancer Research Center, 2020. <a href="https://doi.org/10.6097/e230-20200511_1">https://doi.org/10.6097/e230-20200511_1</a>.</p> <p><strong>Acknowledgements:</strong></p> <p>Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.</p> <p>This work was supported by the Collaborative Research Center (SFB/TRR 125 Cognition-Guided Surgery) of the German Research Foundation (DFG) grant number INST 35/1120-1, DFG grant MA 6340/10-1, DFG grant MA 6340/12-1 and by the Helmholtz Association Initiative and Networking Fund under project number ZT-I-0003.</p>
MooringSense D3.2 - Public simulation dataset
<p>Deliverable 3.2 of the MooringSense project consists of a simulation dataset, generated with a digital twin of the SATH 10 MW floating offshore wind turbine (FOWT), using the simulation workbench SIMA. In SIMA, two software run simultaneously, viz. SIMO and RIFLEX. While the former solves the equations of motions for rigid-bodies, the latter is based on a finite-element formulation for slender marine structures and wind turbine blades. Wind turbine aerodynamics are included in the model through a BEM formulation, including dynamic wake effects. The modelled environmental conditions include waves, current, and turbulent wind. The load cases correspond to a number of operational and idling conditions as specified in IEC 61400-1, with associated sea states corresponding to the Buchan Deep site. In addition, a number of mooring line failures are considered. The dataset in the repository corresponds to the public part of the results, which is limited to the wind turbine time series.</p>
Observations of AOD and GEOS-Chem simulation model output dataset
<p>Archive of observations of AOD (MODIS Deep Blue, MAIAC, AERONET) used to evaluate the simulations of using high resolution offline dust emission, upon submission of the following manuscript:</p> <p>Meng, Jun, R. V. Martin, P. Ginoux, M. Hammer, M. P. Sulprizio, D. A. Ridley, and A. van Donkelaar, Grid-independent high resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (version 12.5.0), Geoscientific Model Development, Submitted. </p> <p>This dataset can also be accessed via this FTP ftp://stetson.phys.dal.ca/jmeng/GMD_OfflineDust_data/ </p> <p>The folder "GMD_OfflineDust_data" contains three folders, "AERONET", "MODIS" and "GC_output", representing AERONET AOD, MODIS AOD and model outputs respectively. </p>
Graphene simulated dataset
Graphene simulated dataset
Evaluating the performance of probabilistic algorithms for phylogenetic analysis of big morphological datasets: a simulation study
<p>Reconstructing the tree of life is an essential task in evolutionary biology. It demands accurate phylogenetic inference for both extant and extinct organisms, the latter being almost entirely dependent on morphological data. While parsimony methods have traditionally dominated the field of morphological phylogenetics, a rapidly growing number of studies are now employing probabilistic methods (maximum likelihood and Bayesian inference). The present-day toolkit of probabilistic methods offers varied software with distinct algorithms and assumptions for reaching global optimality. However, benchmark performance assessments of different software packages for the analyses of morphological data, particularly in the era of big data, are still lacking. Here, we test the performance of four major probabilistic software under variable taxonomic sampling and missing data conditions: the Bayesian inference-based programs MrBayes and RevBayes, and the maximum likelihood-based IQ-TREE and RAxML. We evaluated software performance by calculating the distance between inferred and true trees using a variety of metrics, including Robinson-Foulds (RF), Matching Splits (MS), and Kuhner-Felsenstein (KF) distances. Our results show that increased taxonomic sampling improves accuracy, precision, and resolution of reconstructed topologies across all tested probabilistic software applications and all levels of missing data. Under the RF metric, Bayesian inference applications were the most consistent, accurate, and robust to variation in taxonomic sampling in all tested conditions, especially at high levels of missing data, with little difference in performance between the two tested programs. The MS metric favored more resolved topologies that were generally produced by IQ-TREE. Adding more taxa dramatically reduced performance disparities between programs. Importantly, our results suggest that the RF metric penalizes incorrectly resolved nodes (false positives) more severely than the MS metric, which instead tends to penalize polytomies. If false positives are to be avoided in systematics, Bayesian inference should be preferred over maximum likelihood for the analysis of morphological data.</p>
Dataset for The Path Towards CAM6: Coupled Simulations with CAM5.4 and CAM5.5
<p>This data set provides the climatological files used to generate figures in the article The Path Towards CAM6: Coupled Simulations with CAM5.4 and CAM5.5. </p>
Simulated T-cells Rep-Seq datasets (called SIMTCR) from "Reconstructing antibody repertoires from error-prone immunosequencing datasets" paper
<p>Simulated test datasets used for benchmarking of immunorepertoire construction tools</p>
Simulated B-cells Rep-Seq datasets (called SIMULATED) from "Reconstructing antibody repertoires from error-prone immunosequencing datasets" paper
<p>Simulated test datasets used for benchmarking of immunorepertoire construction tools</p>
Dataset: The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars
<p>VERSION 1:</p> <p>Dataset for the manuscript 'The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars'. The four netcdf files contain the full outputs to the MGCM for sols 7351-7381 (approximately Ls = 0 - 14, MY 35) with all chemical tracers:</p> <ul> <li>fullry_0_diagfi : 0% release dataset</li> <li>fullry_50_diagfi : 50% release dataset</li> <li>fullry_100_diagfi : 100% release dataset</li> <li>fullry_control_diagfi : control (gas only) dataset</li> </ul> <p>The MGCM values which match locations of observed ozone and water vapour TCA, as well as the observations themselves are found in df_nadir_gcm_vapo3_hyb_spin_20230829.pkl. </p> <p> </p> <p>VERSION 2:</p> <p>Also uploaded are the data for sols 7501-7531 (Ls = 68 - 82, MY 35):</p> <ul> <li>d7501-7531_control_diagfi : control (gas only) dataset</li> <li>d7501-7531_50_diagfi : 50% release dataset</li> </ul> <p> </p> <p>VERSION 3:</p> <p>Uploaded are data for sols 7621-7651 (Ls = 124 - 138, MY 35)</p> <ul> <li>d7621-7651_control_diagfi : control (gas only) dataset</li> <li>d7621-7651_50_diagfi : 50% release dataset</li> </ul> <p> </p>
Dataset associated with manuscript "Magnetosphere-ionosphere coupling via prescribed field-aligned current simulated by the TIEGCM"
<p>The dataset is associated with the manuscript "Magnetosphere-ionosphere coupling via prescribed field-aligned current simulated by the TIEGCM". It is based on simulations using the NCAR Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIEGCM) and comparing the thermosphere-ionosphere response during 28-30 May, 2010 geomagnetic storm due to different realizations of magnetosphere-ionosphere (MI) coupling in the model. Results are presented using for MI coupling the Assimilative Mapping of Ionospheric Electrodynamics (AMIE), the empirical Weimer ion convection, and fitted field-aligned current based on observations from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). The output includes among others high latitude quantities of auroral ionization rates, field-aligned current, electric potential, hemispheric integrated Joule heating, and middle and low-latitude zonal mean neutral wind, density, composition, ionospheric Nmf2, TEC and equatorial vertical drift.</p>
Open Research Datasets – Testing Mantle Convection Simulations with Paleobiology and Other Stratigraphic Observations: Examples from Western North America
<p>Data supporting the findings of "Testing Mantle Convection Simulations with Paleobiology and Other Stratigraphic Observations: Examples from Western North America" by Victoria M. Fernandes, Gareth G. Roberts and Fred D. Richards. Further details about these data can be found in the main manuscript and accompanying Supporting Information. This research was funded by NERC Large Grant MC2: Mantle Convection Constrained (NE/T012595/1). <br><br>The datasets contained in this repository:<br>1) Uplift constraints from youngest outcropping marine to terrestrial stratigraphic transitions, as described in Fernandes et al. (2019) JGR Earth Surface (Figure 1; MarineTerrestrialTransition_Fernandes_etal2019.txt)<br>2) Digitised sediment isopachs from Roberts & Kirschbaum (1995), USGS Professional Paper 1561 (Supplementary Figure S3; Roberts_Kirschbaum_1995_isopachs.zip)<br>3) Air-loaded subsidence grids (Supplementary Figure S4; Subsidence.zip)<br>4) Temperature grids at 25 km depth intervals, from 50–400 km depth, generated from the SLNAAFSA hybrid Vs model of Hoggard et al. (2020), Nature Geoscience (Supplementary Figure S6; Temperature_grids_txt.zip)</p>
Dataset for "Observation and Numerical Simulation of Cold Ions Energized by EMIC Waves"
<p>* Reduced datasets for figures in the manuscript.</p>
Molecular Dynamics Simulation Dataset for "Hydrophobic Mismatch Drives Self-Organization of Designer Proteins into Synthetic Membranes"
<p>This repository contains molecular dynamics (MD) simulation data from the study on the self-organization of designer proteins in synthetic membranes. The data includes simulations for different single lipid compositions (DOPC, DPPC, DYPC) denoted as [lipid]-PL* where PL stands for the different TMD constructs. Multi component simulation are named accordingly. The repository provides initial (eqi.gro) and final (prod.gro) coordinates for each simulation. The 'cmd' file in each directory outlines the assembly process of each simulation, and the 'mdp' folder contains all input files for the simulations. </p>
Datasets for the paper "Advancing Scanning Probe Microscopy Simulations: A Decade of Development in Probe-Particle Models"
<p>Input data for probe-particle (PP) atomic force microscopy (AFM), scanning tunnelling microscopy (STM), and Kelvin probe microscopy (KPFM) simulations in the paper "Advancing Scanning Probe Microscopy Simulations: A Decade of Development in Probe-Particle Models".</p> <p>Included files:</p> <ul> <li>CO_tip_densities.tar.gz: the total and delta electron densities for a CO tip.</li> <li>hartree-density.tar.gz: the hartree potential (LOCPOT.xsf), electron density (CHGCAR.xsf), and xyz geometry with point charges (mol.xyz) for 6 example molecules: C60 fullerene, formic acid dimer (FAD), 4-(4-(2,3,4,5,6- pentafluorophenylethynyl)-2,3,5,6- tetrafluorophenylethynyl) phenylethynylbenzene (FFPB), pentacene, phtalocyanine, perylene carboxylic anhydride (PTCDA).</li> <li>dft-afm.tar.gz: DFT-calculated forces for the CO-tip for all of the example molecules. Saved as numpy npz files that containt the force arrays under the key 'force' and the physical extent of the region in Ångströms under the key 'scan_window'.</li> <li>Fig_4_STM_data.tar.gz: Hartree potential (cube_001_hartree_potential.cube) and eigenvectors (KS_eigenvectors.band_1.kpt_1.out) of PTCDA for PPSTM simulations.</li> <li>KPFM_Hartree.tar.gz: Hartree potentials of FFPB with a bias voltage for KPFM simulations.</li> </ul> <p>Reproducing the figures in the manuscript:</p> <ul> <li>Fig. 2 (comparison of force-field models): Install ppafm with the [opencl] option, and run the example script in the ppafm repository at examples/paper_figure/run_simulation.py. The image is saved into the same folder with the script.</li> <li>Fig. 3 (KPFM LCPD map): Install ppafm, navigate to examples/FFPB_KPFM, and run the script ./run.sh. After running the script, the LCPD map of Fig. 3(a) can be found in LCPD_atoms_cbar_018.png and the AFM map of Fig. 3(b) in Q-0.10K0.25V0.00/Amp0.50/df_atoms_cbar_018.png.</li> <li>Fig. 4 (STM dI/dV map): Download and compile the PPSTM code (<a href="https://github.com/Probe-Particle/PPSTM">https://github.com/Probe-Particle/PPSTM</a>). After that navigate to tests/PTCDA_mol and use the run_all.sh script.</li> <li>Fig. 5 (IETS): Install ppafm, navigate to examples/FePc_Au-IETS, and run the script run_ppafm-iets.sh. The IETS image can be found in the Q0.00K0.24 folder.</li> </ul> <p>The ppafm version 0.3.1, and PPSTM version 1.0.2 (<a href="https://doi.org/10.5281/zenodo.10669867" rel="nofollow">https://doi.org/10.5281/zenodo.10669867</a>) was used when producing the images for the manuscript.</p>
Dataset and visualization of numerical simulations of decaying acoustic turbulence in three dimensions
<p>This dataset contains the time series and spectrum data for the 1000^3 resolution simulations featured in the paper: <em>Primordial acoustic turbulence: three-dimensional simulations and gravitational wave predictions</em> by Jani Dahl, Mark Hindmarsh, Kari Rummukainen, and David Weir. Also included are the various output files produced by the simulation code, the run files that can be used to reproduce the runs, and plots of the time series quantities, fluid snapshots, and movies of the longitudinal and transverse energy spectra.</p>
A Dataset of Reconstructed Carotid Bifurcation Lumen and Plaque Models with Centerline Tree and Simulated Hemodynamics
<p><code>carotid_bifurcation_database.zip</code> contains 79 cases of left and right-side carotid bifurcations (152 inner wall models). For each case, inner wall (lumen) and plaque models were extracted from computed tomography angiography (CTA) scans. The models were segmented, reconstructed, and a centerline tree was created for each geometry using the <a href="https://github.com/PepeEulzer/CarotidAnalyzer">CarotidAnalyzer</a> pipeline. The geometries include varying degrees of internal carotid stenosis. Bifurcations with 100% stenosis were omitted, as the vessel is not discernible in the scan.</p> <p><code>carotid_flow_database.zip</code> contains hemodynamic flow simulations of the above models. Fluid data (velocity, pressure) and surface data (wall shear stress) are given in seperate files for each case. For each field, a systolic and diastolic time step are provided.</p> <p><strong>Further information regarding the extraction pipeline and flow simulations can be obtained from the following publications:<br></strong>P. Eulzer, F. von Deylen, W.-C. Hsu, R. Wickenhöfer, C. M. Klingner, and K. Lawonn (2023), A Fully Integrated Pipeline for Visual Carotid Morphology Analysis. Computer Graphics Forum, 42(3): 25-37. <a href="https://doi.org/10.1111/cgf.14808">https://doi.org/10.1111/cgf.14808</a></p> <p>Kevin Richter, Tristan Probst, Anna Hundertmark, Pepe Eulzer, and Kai Lawonn (2024), Longitudinal wall shear stress evaluation using centerline projection approach in the numerical simulations of the patient-based carotid artery. Computer Methods in Biomechanics and Biomedical Engineering, 27(3): 347-364. <a href="https://doi.org/10.1080/10255842.2023.2185478">https://doi.org/10.1080/10255842.2023.2185478</a></p> <p>P. Eulzer, K. Richter, A. Hundertmark, R. Wickenhöfer, C. M. Klingner, and K. Lawonn (2024), Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity. Computer Graphics Forum 43(3): in print. <a href="https://doi.org/10.1111/cgf.15081">https://doi.org/10.1111/cgf.15081</a></p>
Capturing urban heterogeneity enhances tropical cyclones simulation in Houston (dataset)
<p>WRF simulation output for the paper titled "Capturing urban heterogeneity enhances tropical cyclones simulation in Houston". </p>
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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.