Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
567
datasets available to search
ShareScore release 0.9.0
Dataset results
567 results for “Simulated datasets”
ConFiRMa dataset_04: simulation of tests on CRM strengthened masonry buildings with the OOFEM code (intermediate, multi-layer level modelling)
<p>The Dataset collects the input files developed for the simulation of tests on one, two and three stroeys masonry buildings strengthened through Composite Reinforced Mortar with the free open-source code OOFEM (intermediate, multi-layer level modelling).</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provides a description of the different input files.</p>
Dataset - Simulation results GNSS raytracing INTOMO
<p>The 3D ray-tracing algorithm is based on the reconstruction of the signal path within the grid model (3D refractivity field). For our studies, an extension of the original 3D ray-tracing module from ATom (Atmospheric Tomography) GNSS software package [37] is used. The necessary modifications were inspired by Javaherian et al. [44]. The first step of ray-tracing in our approach (shooting<br> technique) is based on the prior knowledge of transmitter and receiver positions, as well as information about weather parameters and thus, refraction at nodal points of the grid. The transmitter and receiver are the GPS satellite and the LEO satellite, respectively. In the current version, coordinates are delivered by atmPhs files in an earth-centered inertial (ECI) coordinate system [45].</p>
Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations
<p>Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.</p> <p>For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.</p>
satmut_utils simulated datasets
<p>Simulated datasets were generated with satmut_utils v1.0.1-dev001. Datasets contain between hundreds to thousands of simulated variants in real CBS alignments.</p>
Dataset for: All-atom simulations reveal the intricacies of signal transduction upon binding of HLA-E ligand to the transmembrane inhibitory CD94/NKG2A receptor
<p>This dataset contains relevant structures, input and other files that are associated with our article "<em>All-atom simulations reveal the intricacies of signal transduction upon binding of HLA-E ligand to the transmembrane inhibitory CD94/NKG2A receptor", available at https://pubs.acs.org/doi/full/10.1021/acs.jcim.3c00249</em></p>
Dataset of Molecular Dynamics Simulations for the Upregulated Biomarker PSMB8: 3UNF and its G210V Mutant in Experimental Autoimmune Encephalomyelitis
<p>This dataset contains molecular dynamics simulations data generated using GROMACS for the upregulated biomarker 3UNF and its G210V mutant in the context of Experimental Autoimmune Encephalomyelitis (EAE). EAE is a widely studied animal model for multiple sclerosis, and investigating the behavior of biomarkers in this model is crucial for understanding disease progression and potential therapeutic interventions.</p> <p>The dataset includes trajectory files, coordinate files, and relevant parameters used in the simulations. These simulations provide valuable insights into the structural dynamics, conformational changes, and interactions of the PSMB8 biomarker 3UNF and its G210V mutant within the EAE system. The data offers researchers an opportunity to analyze and explore the behavior of these biomarkers at the atomic level, aiding in the identification of potential binding partners, functional sites, and mechanisms associated with disease progression.</p> <p>By sharing this dataset, we aim to contribute to the scientific community by providing a valuable resource for further analysis, validation, and comparison of the molecular behavior of the upregulated biomarker 3UNF and its G210V mutant in Experimental Autoimmune Encephalomyelitis.</p>
Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways
<p>This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).</p>
Simulated ChannelSpin Dataset
<p>This example dataset corresponds to the paper<a href="https://arxiv.org/abs/1907.01898"> https://arxiv.org/abs/1907.01898</a>.</p> <p>Moscovich, A., Halevi, A., Andén, J., & Singer, A. (2020). Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes. <em>Inverse Problems</em>, <em>36</em>(2), 024003.<br> <br> Expresses the ChannelSpin feature without ChannelStretch. Includes 54 generated volumes sized (54,54,54) and stack of 10000 projection images (54,54).<br> <br> The dataset was originally created by the paper authors in using several legacy MATLAB functions denoted by "gen_fakekv*". That dataset was converted into a compressed Numpy (.npz) dataset compatible with ASPIRE-Python. The dataset includes parameters (rotations, amplitudes, states, etc) required to simulate an equivalent image stack in Python starting from the volumes, or used directly to provide a ground truth reference. Alternatively, these parameters can be modified to generate different image stacks.</p>
Dataset for "Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data"
<p>This dataset contains the MESWA (Middle East and Southwest Asia) seismic model and auxiliary data used in the creation of the model (Rodgers, 2023). MESWA is a three-dimensional model of the seismic properties of crust and upper mantle of the Middle East and Southwest Asia. The MESWA model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>). </p> <p> </p> <p>Also included are the earthquake source parameters for all 327 Global Centroid Moment Tensor events considered in this study in ASCII text format. Also included are lists of the selected 192 inversion events and 66 validation events in ASCII text format. Lastly, we include a list of all receivers used in the creation and validation of MESWA. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p> </p> <p>The following table provides a listing of the files in the dataset:</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>MESWA.nc</p> </td> <td> <p>MESWA model in NetCDF format</p> </td> </tr> <tr> <td> <p>MESWA.h5</p> </td> <td> <p>MESWA model in HDF5 format, used by Salvus</p> </td> </tr> <tr> <td> <p>MESWA.xmdf</p> </td> <td> <p>Auxiliary file for MESWA.h5, used to import model into Paraview</p> </td> </tr> <tr> <td> <p>events_project.csv</p> </td> <td> <p>Table of event source parameters for all 327 events considered in the project</p> </td> </tr> <tr> <td> <p>inversion_events_192.csv</p> </td> <td> <p>Table of 192 inversion events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>validation_events_66.csv</p> </td> <td> <p>Table of 66 validation events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_inversion.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the inversion (ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_validation.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the validation (ASCII comma separated value)</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophys. J. Int.</em>, 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p> </p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. <em>The Visualization Handbook</em>, 717(8). <a href="https://doi.org/10.1016/b978-012387582-2/50038-1">https://doi.org/10.1016/b978-012387582-2/50038-1</a></p> <p> </p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. <em>Journal of Open Research Software</em>, 5(1). <a href="https://doi.org/10.5334/jors.148">https://doi.org/10.5334/jors.148</a></p> <p> </p> <p>Rodgers, A. (2023). Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data, technical report, LLNL-TR- 851939.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This project was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008 and the National Nuclear Security Administration. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-852402</p> <p> </p>
Dataset for article: Structure Formation in Tailor-Made Buriti Oil Emulsion During Simulated Digestion
<p><strong>Dataset for publication:</strong></p> <p>Structure Formation in Tailor-Made Buriti Oil Emulsion During Simulated Digestion<br> <em>Rafael V. M. Freire, Linda Hong, Miroslav Peterek, Stéphane Canarelli, Serge Rezzi, Stefan Salentinig</em><br> Advanced Funtional Materials 2023 (DOI 10.1002/adfm.202303854)</p> <p>Setup and conditions for the experiments are described in the experimental section of the published (open access) manuscript.</p> <p>Data description in README.txt file.</p>
Simulated datasets for detector and particle flow reconstruction: CLIC detector
<p><strong>Data description</strong></p> <p>Datasets generated using Key4HEP and the CLIC detector model suitable for particle flow reconstruction studies.</p> <p>The datasets contain generator particles, reconstructed tracks and calorimeter hits, reconstructed Pandora PF particles and their respective links in the <a href="https://doi.org/10.5281/zenodo.7221473">EDM4HEP</a> format.</p> <p>The following processes have been simulated with Pythia 8:</p> <ul> <li>p8_ee_tt_ecm380: ee -> ttbar, center of mass energy at 380 GeV</li> <li>p8_ee_qq_ecm380: ee -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>p8_ee_ZH_Htautau: ee -> ZH -> Higgs decaying to tau leptons, center of mass energy at 380 GeV</li> <li>p8_ee_WW_fullhad: ee -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>p8_ee_tt_ecm380_PU10: ee -> ttbar with on average 10 Poisson-distributed events from ee->gg overlayed, center of mass energy at 380 GeV</li> </ul> <p>The following single particle gun samples have been generated with ddsim:</p> <ul> <li>e+/e-: single electron with energy between 1 and 100 GeV</li> <li>mu+/mu-: single muon with energy between 1 and 100 GeV</li> <li>kaon0L: single K0L with energy between 1 and 100 GeV</li> <li>neutron: single neutron with energy between 1 and 100 GeV</li> <li>pi+/pi-: single charged pion with energy between 1 and 100 GeV</li> <li>pi0: single neutral pion with energy between 1 and 100 GeV</li> <li>gamma: single photon with energy between 1 and 100 GeV</li> </ul> <p>The detector simulation has been done with Geant4, the reconstruction with Marlin interfaced via Key4HEP which includes PF reconstruction with Pandora, all using publicly available models and code.</p> <p> </p> <p><strong>Contents</strong></p> <p>This record includes the following files:</p> <ul> <li>*_10files.tar: small archives of 10 files for each data sample, suitable for testing</li> <li>dataset_full.txt: the full list of files, hosted at the Julich HPC courtesy of the Raise CoE project, ~2.5TB total</li> <li>*.cmd: the Pythia8 cards</li> <li>pythia.py: the pythia steering code for Key4HEP</li> <li>run_sim.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380, p8_ee_qq_ecm380, p8_ee_ZH_Htautau, p8_ee_WW_fullhad datasets</li> <li>run_sim_pu.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380_PU10 dataset</li> <li>run_sim_gun.sh: the steering script for generating the single-particle gun samples</li> <li>run_sim_gun_np.sh: the steering script for generating multi-particle gun samples (extensive datasets have not yet been generated)</li> <li>check_files.py: the main driver script that configures the full statistics and creates submission scripts for all the simulations</li> <li>PandoraSettings.zip: the settings used for Pandora PF reconstruction</li> <li>main19.cc: the Pythia8+HepMC driver code for generating the events with PU overlay</li> <li>clicRec_e4h_input.py: the steering configuration of the reconstruction modules in Key4HEP</li> <li>clic_steer.py: the steering configuration of the Geant4 simulation modules in Key4HEP</li> <li>clic-visualize.ipynb: an example notebook demonstrating how the dataset can be loaded and events visualized in Python</li> <li>visualization.mp4: an example visualization of the hits and generator particles of a single ttbar event from the dataset</li> </ul> <p> </p> <p><strong>Dataset semantics</strong></p> <p>Each file consists of event records. Each event contains structured branches of the relevant physics data. The branches relevant to particle flow reconstruction include:</p> <ul> <li>MCParticles: the ground truth generator particles</li> <li>ECALBarrel, ECALEndcap, ECALOther, HCALBarrel, HCALEndcap, HCALOther, MUON: reconstructed hits in the various calorimeter subsystems</li> <li>SiTracks_Refitted: the reconstructed tracks</li> <li>PandoraClusters: the calorimeter hits, clustered by Pandora to calorimeter clusters</li> <li>MergedRecoParticles: the reconstructed particles from the Pandora particle flow algorithm</li> <li>CalohitMCTruthLink: the links between MC particles and reconstructed calorimeter hits</li> <li>SiTracksMCTruthLink: the links between MC particles and reconstructed tracks</li> </ul> <p>The full details of the EDM4HEP format are available <a href="https://doi.org/10.5281/zenodo.7221473">here</a>.</p> <p> </p> <p><strong>Dataset characteristics</strong></p> <p>The full dataset in dataset_full.txt consists of 43 tar files of up to 100GB each. The tar files contain in total 58068 files, 2.5TB in the ROOT EDM4HEP format.</p> <p>The subset in *_10files.tar for consists of 150 files, 26GB in the ROOT EDM4HEP format.</p> <p> </p> <p><strong>How can you use these data?</strong></p> <p>The ROOT files can be directly loaded with the <a href="https://zenodo.org/record/8239801">uproot</a> Python library.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>These are simulated samples suitable for conceptual machine learning R&D and software performance studies. They have not been calibrated with respect to real data, and should not be used to derive physics projections about the detectors.</p> <p>Neither CLIC nor CERN endorse any works, scientific or otherwise, produced using these data. All releases will have a unique DOI that you are requested to cite in any applications or publications.</p>
Dataset from "Deciphering the Catalytic Mechanism of Virginiamycin B Lyase with Multiscale Methods and Molecular Dynamics Simulations"
<p>Dataset from "Deciphering the Catalytic Mechanism of Virginiamycin B Lyase with Multiscale Methods and Molecular Dynamics Simulations", containing the most relevant simulation output trajectories ran with GROMACS 2021:</p> <p>1) apo simulations, including wildtype, Y28F, and H228A;<br> 2) holo simulations, including the two tested protonation states for the antibiotic;<br> 3) mutant simulations, including Y18F, H228A, E268Q, and E284Q.</p> <p>All folders contain the topology file (.top), restraint files (.itp), the initial coordinates file (.gro), and the coordinates after the first minimization (em1.gro). The output trajectories of all replicas (per system) have been concatenated in a single compressed file (.xtc).</p>
Dataset and Simulation Files for article "Bright and Vivid Diffractive-Plasmonic Reflective Filters for Color Generation"
<p>This work was supported by Ministério da Ciência Tecnologia, Inovações e Comunicações, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil, Finance, Code 001, National Counsel of Technological and Scientific Development, and São Paulo Research Foundation (Fapesp) through grants 2018/15580-6, 2018/15577-5, 2016/18308-0, 2012/ 17610-3, and 2012/17765-7. Part of the results presented in this work were obtained through Project 4716-11, funded by Samsung Eletrônica da Amazônia Ltda., under the Brazilian Informatics Law 8.248/91. The authors thank the Center for Semiconductor Components and Nanotechnologies for the nanofabrication infrastructure.</p>
Dataset related to the article "Laboratory-scale hydraulic fracturing dataset for benchmarking of Enhanced Geothermal System simulation tools"
<p>Experimental results from hydraulic fracturing experiments performed in granite and marble samples of size 30 cm × 30 cm × 45 cm under well-defined boundary conditions.</p> <p>Datasets include:</p> <ul> <li>pressure versus flow-rate response</li> <li>acoustic emission data from a dense network of 32 seismic sensors</li> <li>detailed description of the experimental set-up and adopted test protocol</li> <li>mechanical and petrophysical properties of the samples</li> <li>python code for seismic data processing</li> </ul> <p>This complete collection of data, obtained within the framework of European Union’s Horizon 2020 project GEMex, is rare in its kind and indispensable for verification of model assumptions and constitutive relationships of numerical codes used for designing field-scale hydraulic fracturing experiments.</p>
Dataset related to the publication "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086
<p>This folder contains the raw data from which the graphs in paper "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086, have been obtained.</p>
Dataset to "Applied Research of the Hygrothermal Behaviour of an Internally Insulated Historic Wall without Vapour Barrier: In Situ Measurements and Dynamic Simulations"
<p>This record contains raw data of a 3-month monitoring period of the HeLLo project.</p> <p>The datafiles titled MeasLog_YYYY-MM-DD.dat correspond to the raw data used for the data analysis presented in “Hygrothermal analysis at critical points of an internally insulated historic wall without vapour barrier: in situ measurements and dynamic simulation”, submitted for publication in journal <em>energies</em>.</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat, corresponds to daily registered data monitored every minute.</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuratio, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643).</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.</p>
Structural dynamics of DNA depending on methylation pattern: Simulation dataset
<p>Dataset of molecular dynamics simulations of double-stranded DNA<br> (repeats of CpG dinucleotides with regular patterns of methylation).</p> <p>- Input: Parameters and initial structures<br> - Output: Trajectories</p> <p>NAMD 2.13 (multi-core with CUDA) was used for the simulations.</p>
Dataset and Simulation Files for article "Alumina coating for dispersion management in ultra-high Q microresonators"
<p>Link to arXiv submission: <a href="https://arxiv.org/abs/2009.07826">https://arxiv.org/abs/2009.07826</a></p> <ul> <li>Figure 1 <ul> <li>Data, Jupyter scripts, and comsol simulations files for parts (d,e)</li> <li>Data, Jupyter scripts, and simulations files for parts (f,g,h)</li> </ul> </li> <li>Figure 2 <ul> <li>Raw AFM data for use in Gwydeon software</li> <li>Data, Jupyter scripts, and comsol simulations files for parts (d,e)</li> </ul> </li> <li>Figure 3 <ul> <li>Raw data and Jupyter processing scripts for transmission spectrum calibration (parts - b)</li> <li>Frequency calibrated transmission spectrum and GVD/ Quality factor Jupyter scripts (parts d-g)</li> </ul> </li> <li>Figure 4 <ul> <li>Experimental data for parts a-g</li> </ul> </li> <li>Supplementary data <ul> <li>Raw ellipsometer data</li> <li>1D COMSOL FEM solver and analytical validation (including Julia Jupyter script)</li> <li>Quality factor estimation based on scattering and water adsorption</li> </ul> </li> </ul>
Azure dataset sample for OpenDC Serverless simulator
<p>This dataset is representative of the sample used in the Serverless in the Wild paper [1] experiments. The dataset is used to reproduce the experiments in the OpenDC Serverless FaaS simulator [2]. The original dataset [3] has been sampled converted into the OpenDC Serverless trace format. </p> <p>This is part of Soufiane Jounaid's Bachelor thesis on designing, evaluating, and implementing the first FaaS platform simulator conducted within the AtLarge Research group in the Vrije Universiteit Amsterdam.</p> <p>[1] <a href="https://arxiv.org/pdf/2003.03423.pdf">Shahrad, M., Fonseca, R., Goiri, Í., Chaudhry, G., Batum, P., Cooke, J., ... & Bianchini, R. (2020). Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider. arXiv preprint arXiv:2003.03423.</a></p> <p>[2] <a href="https://drive.google.com/file/d/12hox3PwagpD0jNFA57tO4r2HqvOonkY3/view?usp=sharing">https://drive.google.com/file/d/12hox3PwagpD0jNFA57tO4r2HqvOonkY3/view?usp=sharing</a> </p> <p>[3] <a href="https://github.com/Azure/AzurePublicDataset/blob/master/AzureFunctionsDataset2019.md">https://github.com/Azure/AzurePublicDataset/blob/master/AzureFunctionsDataset2019.md</a></p>
Datasets for "Hydrodynamic and hydromagnetic energy spectra from large eddy simulations"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Hydrodynamic and hydromagnetic energy spectra from large eddy simulations" Haugen & Brandenburg. If anything turns our to be incomplete, please email brandenb@nordita.org.</pre>
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.