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102
datasets available to search
ShareScore release 0.9.0
Dataset results
102 results for “Network simulation”
Video simulations for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Videos of the comparison between numerical and deep learning simulations for test datasets 1, 2, and 3 for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p>
Data: Upsamling Monte Carlo Neutron Transport Simulation Tallies Using a Convolutional Neural Network
<p>This repository contains:</p> <ul> <li>openmc-data-XXXX.tar.gz - Training Data generated with the OpenMC Monte Carlo code representing neutron flux tallies in 4,400 unique light water reactor fuel assemblies in HDF5 format. Training samples consist of tallies in 64x64 pixels and 8 neutron energy groups, and tallies in 128x128 pixels and 16 neutron energy groups. Folders 0008 to 0023 contain training and validation data. Folder 0024 contains test data.</li> <li>out.mat - Upsampling results using a Convolutional Neural Network for 300 testing data samples in MATLAB format. These data include OpenMC tally uncertainties in low and high resolution tallies, scaling values used in data pre-processing, low resolution inputs to the CNN, and high resolution upsampled results as well as high resolution ground truth values.</li> </ul>
Simulated_bending_spectras_and_NN_network
<p>This folder is used as a supplementary material for article "Fiber shape sensing using Long Period Fiber Grating and Machine Learning Numerical analysis". It comprises first matlab generated data in the form of fiber random bending profiles and their associated LPG spectral transmission, as calculated with coupled-mode-theory. Then, the data has been used to train a keras NN model, that is also given in the folder.</p>
Fig. 4. 3D docking simulations and 2D in Meroterpenoids from the leaves of Psidium guajava (guava) cultivated in Korea using MS/MS-based molecular networking
Fig. 4. 3D docking simulations and 2D diagrams of ligand interactions for compounds 1–4 in the active site of PTP1B (PDB code 1NNY).
rTMS for the Treatment of Chronic Tinnitus: Optimization by Simulation of the Cortical Tinnitus Network
ClinicalTrials.gov study NCT01663324. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Efficient parallelization of tensor network contractions for simulating quantum computation
Open the record for dataset details and reuse information.
Data from: Network analysis by simulated annealing of taxa and islands of Macaronesia (North Atlantic Ocean)
Open the record for dataset details and reuse information.
Supporting data for "Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision" by Chevalier et al., 2020
<p>Code, data, and notebooks used for the synthesis and simulations of ensembles of Boolean networks for the tumor invasion model introduced in <a href="https://doi.org/10.1371/journal.pcbi.1004571">(Cohen et al, 2015)</a></p> <p>Visualize online:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Simulations%20-%20Mutant%20analysis.ipynb">Simulations - Mutant analysis.ipynb </a></li> <li> <a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Tumour%20-%20Synthesis%20with%20BoNesis.ipynb">Tumour - Synthesis with BoNesis.ipynb</a></li> </ul> <p>The notebooks can be executed within the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> image 2020-07-01:</p> <pre><code>pip install -U colomoto-docker colomoto-docker -V 2020-07-01 --bind . </code></pre> <p>The synthesis additionally requires executing the following command (within the Docker image):</p> <pre><code>pip install --user bonesis-preview-20200514.zip </code></pre> <p>The ensembles have been generated with the following commands.</p> <pre><code>python synthesis.py synthesis --exact-pkn --globalfps python synthesis.py synthesis --exact-pkn --globalfps --mutant p53 --mutant NICD </code></pre> <p> </p> <ul> </ul>
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing - Model Weights, Chains, BNN Samples, and Simulated Datasets
<p>The model weights, chains, simulated datasets, and BNN samples used to produce the results shown in LSST DESC Collaboration paper "Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing." All files presented here are meant for use in tandem with the python package "ovejero" (<a href="https://github.com/swagnercarena/ovejero">https://github.com/swagnercarena/ovejero</a>).</p>
Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks
<p>Files for reproducing results from Kelkar et al. (JPCB 2020) - Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks</p> <p> </p> <p>This folder contains simulations starter files and also plug-and-play datasets to test ML algorithms on molecular dynamics (MD) simulation data.</p> <p> </p> <p>All analysis scripts can also be found on GitLab on this link: https://gitlab.com/atharva-kelkar/kelkar_et_al_jpcb_2020</p>
Data and Software: Upsampling Monte Carlo Reactor Simulation Tallies in Depleted SFR Assemblies using a Convolutional Neural Network
<p>Datasets and code used in upsampling OpenMC SFR simulation neutron flux tallies.</p>
Evaluation of the CaloINN generative network - Fast detector simulation
<p>These are the sampled used in the paper "Normalizing Flows for High-Dimensional Detector Simulations" for the evaluation of the CaloINN and the CaloVAE+INN models.</p> <p>Each zip file contains the generated showers stored in the CaloChallenge format together with figures of high-level observables.</p> <p>We include the layer energy deposition, the center of energy, the width of the center of energy, the layer sparsity, the ratio E_tot/E_inc, and the full voxel energy distribution.</p> <p>For these features we provide:</p> <ul> <li>energy inclusive histograms for all the datasets;</li> <li>for dataset 1, three incident energies: 256 MeV, 8.192GeV, 262.144 GeV;</li> <li>for dataset 2 and 3, three incident energy windows, (1-10), (10-100), (100-1000) GeV;</li> <li>separation power between Geant4 and our samples calculatd from the histograms.</li> </ul>
Simulation and experimental data of frequency domain and time domain optical signal measurements for optical network digital twins
<p>The dataset contains IQ optical constellation samples for 16-QAM optical connections. Data have been generated both experimentally and through simulations with a MATLAB-based simulator. Different configurations have been simulated: 62 lightpaths having a different number of spans and links and 4 soft-failures affecting a lightpath with increasing failure magnitude.</p>
research data supporting "Revealing the organization of catalytic sequence-defined oligomers via combined molecular dynamics simulations and network analysis"
<p>This repository contains all the data generated and analyzed including the starting structures, the input files, the trajectory files, the output data from cpptraj and network analyses, and in-house scripts used to prepare the network and module files shown in the paper <strong>"Revealing the organization of catalytic sequence-defined oligomers via combined molecular dynamics simulations and network analysis"</strong> published in <strong>Journal of Chemical Information and Modeling</strong> (DOI: 10.1021/acs.jcim.2c00101). </p>
Dataset for network simulator comparison
<p>Dataset for network simulator comparison</p>
Simulated random habitat networks and generic networks
<p>Simulated random habitat networks and generic networks</p>
Artificial neural networks enable genome-scale simulations of intracellular signaling
GEO Series GSE202515. Homo sapiens. 190 samples. Type: Expression profiling by high throughput sequencing.
Input data files for RSS-NET analysis of simulated GWAS summary statistics and B cell regulatory network
<p>Details of these data files are provided in https://suwonglab.github.io/rss-net/wtccc_bcell.</p> <p>Contact:<code> xiangzhu[at]psu.edu </code></p>
Simulation results of an agent-based model of civil violence with the effect of introducing a small world network
<p>The data set contains the simulation results of an agent-based model of civil violence with the effect of introducing a small world network. Some details on the agent-based model (without small world network) can be found in [Maria Fonoberova, Vladimir A. Fonoberov, Igor Mezic, Jadranka Mezic and P. Jeffrey Brantingham, Nonlinear Dynamics of Crime and Violence in Urban Settings, Journal of Artificial Societies and Social Simulation, 15(1), 2, http://jasss.soc.surrey.ac.uk/15/1/2.html, DOI: 10.18564/jasss.1921].</p> <p>Files in folder "Appropriate_Rate_of_Violence" are related to the case with the appropriate rate of violence.</p> <p>Subfolder NoSWN_CitVis_14 is for the case with no small world network and the citizen vision of 14.</p> <p>Subfolder SWN_CitVis_13.16 is for the case with small world network and the citizen vision of 13.16.</p> <p>Subfolder SWN_CitVis_14 is for the case with small world network and the citizen vision of 14.</p> <p>Each file with name starting with Act has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds.</p> <p>Files in folder "High_Rate_of_Violence" are related to the case with the high rate of violence.</p> <p>Subfolder Beta0.2 is for the case with small world network and \beta=0.2.</p> <p>Subfolder Beta0.8 is for the case with small world network and \beta=0.8.</p> <p>Subfolder NoSWN is for the case with no small world network.</p> <p>Each file with name starting with Actives has one row for each time step of the simulation and has columns that are: number of active citizens, number of intimidated citizens, number of LEOs, number of non-intimidated citizens. These files are provided for lattice sizes from 100x100 to 300x300 and for different random seeds.</p>
Relief delivery simulations using the transport network of Visayas, Philippines
<p><strong>In this study, we constructed a transportation network connecting the cities and municipalities of the Visayas island group in the Philippines with their regional relief hubs and set up a relief delivery simulation to affected town centers, defined to be those within a 100 km radius of the path of Typhoon Haiyan. We measured the relief delivery efficiency of the transport network over a range of damage scenarios relative to the undamaged baseline case. </strong></p><p><strong>This contains the files and codebase necessary to construct the Visayas transportation network and perform relief simulations.</strong></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.