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567 results for “Simulated datasets”
Dataset to reproduce MD simulations described in "An atomistic view of the YiiP structural changes upon zinc(II) binding"
<p>Here are collected topologies, parameters, distance restraints, starting coordinates and trajectories for all the simulations reported in the manuscript entitled "An atomistic view of the YiiP structural changes upon zinc(II) binding" </p>
AutoMate Dataset - Simulator 1
<p>Dataset collected in simulator studies for H2020 AutoMate project</p>
Dataset for the study of "Seismic fault slip at depths simulated by high-velocity friction experiments under hydrothermal conditions"
<p>These are measured data of high-velocity friction experiments conducted on gabbro and marble under hydrothermal conditions (initial ambient temperature of 40 to 400 degC and pore pressure of 30 MPa). </p> <p>In each txt file, Column1 to Column9: Time (s), Slip Velocity (m/s), Displacement (m), Friction coefficient, Axial Displacement (mm), Pore water pressure (MPa), Effective normal stress (MPa), Sample Temperature (degC) and Furnace Temperature (degC).</p>
Datasets for "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the datasets for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations".</p>
Dataset of "Detecting Weaknesses and Analyzing Cascading Failures in Critical Infrastructure Networks Using an Interconnected Simulation System"
<p>This article introduces an innovative simulator for critical infrastructure, developed to enable comprehensive<br>simulation of data and power grid and their interconnections. The simulator offers user-input functionality,<br>allowing for detailed modelling and analysis of various impact scenarios on infrastructures. A key feature<br>of this tool is the use of a state matrix to represent the current state of infrastructures, which is updated<br>after each simulation. This update facilitates accurate and dynamic modelling of changes in infrastructure<br>networks and their interconnections.<br>The simulator is designed to test the functionality of both individual components and the overall network<br>integrity, including the analysis of the cascading failures, where a failure in one part can impact other parts<br>of the infrastructure. This capability is essential for a deeper understanding of risks and for developing<br>effective strategies to protect and ensure the resilience of critical infrastructures. The simulator represents a<br>significant advancement in the field of critical infrastructure simulation, providing a tool for better prediction,<br>identification, and mitigation of potential threats, thereby enhancing the security and resilience of critical<br>systems.</p>
Simulated EBSD dataset
<div> <div> <div> <p>A total of 60,000 images, categorized into train, val, and test, with 36000 images in the train set and 12000 images in both the test and validation sets for 6 space groups—221, 223, 225, 227, 229, and 230</p> </div> </div> </div>
Simulation dataset for Off-diagonal Ion Pressure Linked to Hall Fields in Collisionless Magnetic Reconnection
<p>The dataset contains simulation data used in the paper of "Off-diagonal Ion Pressure LInked to Hall Fields in Collisionless Magnetic Reconnection". Data are generated from a particle-in-cell simulation using the VPIC code. Please see the paper for the descriptions of the simulation setup.</p> <p>The .gda files in fiels_moments_gda_files.zip are fields and plasma moments data from the time step presented in the paper. Each file contains float-type data arrays with a size of 6720x1x2240, corresponding to x-y-z dimensions, respectively. Data can be read by softwares like IDL, python, matlab, etc., using the standard data reading methods.</p> <p>The file of harris_m100_x60_Hparticle.89586 contains ion particle data for this time step, for the domain of x=[0,60]di, z=[-8,8]di. The file is written in BINARY, for the information of indivial particles. Each data chunk constitues of the following variables in order: x, z, ux, uy, uz, q. Here x and z are positions of the particles, in unit of de; ux, uy, uz are relativistic velocities in unit of the speed of light (c), i.e., ux = gamma*vx, gamma=sqrt(1+ux^2+uy^2+uz^2); q represents the weight of the particles, so the contribution of a particle to the phase space density needs to multiply by this q factor (In the paper, q factors are re-normalized while the relative weights between particles remain the same).</p> <p>An example IDL sentence of reading particle data: readu, lun, x, z, ux, uy, uz, q. Repeat this sentence for different particles.</p>
Datasets and simulation scripts for "Finite-temperature Rydberg arrays: quantum phases and entanglement characterization"
<p>The files contain the datasets with the results of the manuscript "Finite-temperature Rydberg arrays: quantum phases and entanglement characterization".</p> <p>The URL links to the repository with the simulation scripts used to produce the datasets. Running the simulation scripts require Quantum Green TEA v0.3.23 which comes with Quantum TEA Leaves v0.4.46 as a dependency. Quantum Green TEA is not open source, but is available on request. Exceptionally, the scripts for producing the datasets for convergence analyisis require Quantum Green TEA v0.3.23 and Quantum TEA Leaves version>v0.2.30.</p>
Dataset for protein docking and MD simulations of CARD-CARD interactions of the apoptosome
<p>Protein docking complexes and MD simulation trajectories for the Apaf1-Caspase9 CARD-CARD interactions and the cross-dockings between Apaf1 CARD and Caspase2 CARD, and RAIDD CARD and Caspase9 CARD, respectively (RAIDD and Caspase2 CARD domains interacting in the PIDDosome). Further details in the README file.</p>
CFD Simulation Dataset for Airborne Pathogen Transmission in Turbulent Channels
<p>This dataset contains results from computational fluid dynamics (CFD) simulations conducted using <strong>Ansys</strong> to model airborne pathogen transmission in turbulent airflow channels. The simulations focus on the dispersion of pathogen-laden droplets and aerosols, mimicking scenarios such as coughing-induced flows. The data includes the reception statistics of airborne particles, which are used to derive the infection probability. The dataset supports the studygiven below and can be used for statistical characterization and further analysis of airborne pathogen transmission between two people.</p> <p>F. Gulec, F. Dressler and A. W. Eckford, "A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels," in IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, vol. 9, no. 2, pp. 124-134, June 2023, doi: 10.1109/TMBMC.2023.3273193.</p> <p> </p>
Dataset related to "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations"
<p>This is an accompanying dataset to the article with the title "A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations" (DOI: <a href="https://doi.org/10.1162/netn_a_00429" target="_blank" rel="noopener">10.1162/netn_a_00429</a>). It contains the resulting data of the manipulated SSCx network model, such as benchmarks, fitted stochastic models, manipulated connectomes, structural validations, as well as simulation data and analysis results.</p> <p>The corresponding repository with code, configuration files, and detailed instructions for reproducing the results in this dataset as well as generating the results figures in the accompanying article is available here: <a href="https://github.com/BlueBrain/sscx-connectome-manipulations" target="_blank" rel="noopener">https://github.com/BlueBrain/sscx-connectome-manipulations</a></p> <p>The underlying <em>Connectome-Manipulator</em> software is available here: <a href="https://github.com/BlueBrain/connectome-manipulator" target="_blank" rel="noopener">https://github.com/BlueBrain/connectome-manipulator</a></p> <p>Additional requirement: Original SSCx network model (DOI: <a href="https://doi.org/10.5281/zenodo.8026353" target="_blank" rel="noopener">10.5281/zenodo.8026353</a>)</p> <p>ℹ️ Disclaimer: Some results may have been produced with an older version of <em>Connectome-Manipulator</em>, so slight differences might be possible when re-running with the latest version.</p> <p><strong>Update 25/09/2024 (v2):</strong> Added benchmark results for assessing strong and weak scaling behavior of connectome rewiring.</p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Paracou dataset / Hyperspectral data for spectra simulation and taxonomic diversity estimation
Open the record for dataset details and reuse information.
Supporting Dataset for the study "Influence of Floodplains and Groundwater Dynamics on the Present-Day Climate simulated by the CNRM Model"
<p>This archive contains the data used in the study "Influence of Floodplains and Groundwater Dynamics on the Present-Day Climate simulated by the CNRM Model" submitted to <a href="https://www.earth-system-dynamics.net/">Earth System Dynamics</a>.</p> <p> </p>
Cycles global simulation dataset
Open the record for dataset details and reuse information.
Simulation dataset for Super Typhoon Shanshan (2024)
Open the record for dataset details and reuse information.
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Part 9: Operational and damaged scenarios in regular waves
<p>This dataset contains the results of OpenFAST simulations performed on the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets to which this is part number 9. A report describing this dataset is available on the BEL-Float project website: https://www.owi-lab.be/bel-float.</p>
Autonomous Vehicles_VSIM Simulator_Dataset
<p>The VSIM dataset is a specially designed collection of 5,000 images, created to enhance object detection in autonomous vehicle (AV) applications. Developed with the VSIM simulator in Unity, this dataset captures eight distinct categories across realistic driving scenarios, with a focus on representing varied environments and conditions for AVs. To ensure high-quality data, images were preprocessed and augmented using Roboflow. The dataset is divided into training, validation, and testing sets, with a unique setup for federated learning, where training data is split across three clients. Each client is configured to detect four key object classes—humans, cars, road signs, and bikes—supporting research in both centralized and federated contexts. The VSIM dataset fills a gap in AV data diversity, providing a valuable resource for advancing real-time object detection in distributed learning applications.</p> <p> </p>
Dataset for Underground Hydrogen Storage Simulations
<h1>UHS Dataset</h1> <p>This repositry stores a dataset intended for training machine learning models on the task of predicting underground hydrogen storage. It is the dataset used in the paper ''Deep Learning for Subsurface Flow: A Comparative Study of U-Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage''.</p> <p>The dataset is also available on GitHub: https://github.com/AlvaroC-ML/ml4uhs-dataset.</p>
Dataset and Simulation Files for "Optomechanical microgear cavity"
<p>Dataset and Simulation Files for "Optomechanical microgear cavity"</p>
ClimateNet Dataset as used in "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data"
<p>ClimateNet dataset as it was used by us for the study: "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data" (https://gmd.copernicus.org/preprints/gmd-2024-60/).</p> <p> </p> <p>For the original dataset refer to: https://portal.nersc.gov/project/ClimateNet/</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.