Find research datasets worth reusing

Human head models and populational framework for simulating brain stimulations: part 3

We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset.    <ol> <li> McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a> </li> <li> Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018 </li> </ol>   This dataset is split into 6 parts, you are currently on part 3. All parts are linked below: Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a> Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a> Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a> Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a> Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a> Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a>

Human head models and populational framework for simulating brain stimulations: part 5

We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset.    <ol> <li> McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a> </li> <li> Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018 </li> </ol>   This dataset is split into 6 parts, you are currently on part 5. All parts are linked below: Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a> Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a> Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a> Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a> Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a> Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a>

Human head models and populational framework for simulating brain stimulations: part 2

We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset.    <ol> <li> McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a> </li> <li> Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018 </li> </ol>   This dataset is split into 6 parts, you are currently on part 2. All parts are linked below: Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a> Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a> Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a> Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a> Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a> Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a>

Human head models and populational framework for simulating brain stimulations: part 1

We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset.    <ol> <li> McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a> </li> <li> Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018 </li> </ol>   This dataset is split into 6 parts, you are currently on part 1. All parts are linked below: Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a> Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a> Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a> Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a> Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a> Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a>

An integrated framework for analysing, simulating and testing UML models

<h1>Project Overview</h1> This is the artefact associated with the paper "An integrated framework for analysing, simulating and testing UML models", submitted to the 27th Brazilian Symposium on Formal Methods (SBMF 2024). <h2>Directory Structure</h2> <ul> <li>CSP_Validation: Contains the validation of our mapping rules from UML diagrams to CNL requirements.</li> <li>NAT2TEST_Projects: Contains the NAT2TEST projects for two case studies: the classical Dijkstra's dining philosophers problem, and a distributed ring-buffer model.</li> <li>UML2CNL: Contains the implementation of our mapping rules from UML diagrams to CNL requirements.</li> </ul>

Developing and Benchmarking Sulfate and Sulfamate Force Field Parameters via Ab Initio Molecular Dynamics Simulations to Accurately Model Glycosaminoglycan Electrostatic Interactions

To cite and for more details: Riopedre-Fernandez et al. J. Chem. Inf. Model. 2024, 64 (18), 7122–7134. DOI: <a href="https://doi.org/10.1021/acs.jcim.4c00981">https://doi.org/10.1021/acs.jcim.4c00981</a> The dataset includes molecular dynamics simulations of sulfated saccharides and their sulfated analogs in the presence of calcium cations in aqueous solution. Several force field parameter sets were compared (CHARMM36, GLYCAM06, AMOEBA, Drude) and new have been developed (prosECCo75 and GLYCAM-ECC75). The uploaded files contain the following simulation input files or/and simulation trajectories: 1) Sulfated_Molecules_Umbrella_Sampling_AIMD: Umbrella sampling ab initio molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water. 2) Sulfated_Molecules_Umbrella_Sampling_FFMD: Umbrella sampling force field molecular dynamics simulations of calcium-methylsufate and calcium N-methylsulfamate ion pairs in water. 3) Sulfated_Molecules_AWH_FFMD: Accelerated weight histogram force field molecular dynamics simulations of calcium interacting with both methylsufate and N-methylsulfamate in water. 4) Disaccharides_FFMD: Unbiased force field molecular dynamics simulations of calcium-sulfated disaccharide aqueous solutions. UPD. Version 2.0 has updated one of the disaccharide-containing simulations (GLYCAM06, N-sulfation) due to incorrect calcium LJ parameters in the original upload.

Simulations of OMPC, SOPC, and DOPC bilayers and monolayers at three different sizes. CHARMM36 with OPC water model

Simulations of OMPC, SOPC, and DOPC bilayers and monolayers with the CHARMM36 force field and OPC water. Three system sizes are used: small ("s", 64 lipids), medium ("m", 256 lipids), and large ("l", 1024 lipids). All simulations are 1 µs long. The simulations are performed using GROMACS, and for each system the following are provided for bilayers ("BIL") and monolayers ("MONO"), separated into two tar archives, one for bilayers and one for monolayers. <ul> <li>run input file (tpr)</li> <li>energy file (edr)</li> <li>trajectory file (xtc)</li> <li>checkpoint file (cpt) </li> <li>final structure (gro)</li> </ul> Additionally, the following are shared by the monolayer and bilayer, and are not included in the tar arcives. <ul> <li>index file (ndx)</li> <li>topology file (top)</li> </ul> The simulation parameter files (mdp) are provided separately for bilayers and monolayers. The molecular topologies (top) are included in TOP.tar. The CHARMM36 force field is obtained from http://mackerell.umaryland.edu/charmm_ff.shtml

The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated by four CMIP6 models'

The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated  by four CMIP6 models'

Post-processed SAM (System for Atmospheric Modeling) simulation output for "Tipping to an Aggregated State by Mesoscale Convective Systems"

Statistics output files for all variables, for a select number of SAM (System for Atmospheric Modeling v. 6.11) simulation runs used in the study  "Tipping to an Aggregated State by Mesoscale Convective Systems". The following simulations are included: DIU, OCEAN, DIU2OCEAN branch A1, DIU2OCEAN branch A2.

Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Model Simulation Data

WRF-Chem simulated daily mean PM2.5 concentrations for: 1) with fires  2) without fires simulations.

Data used in "Revealing dominant patterns of aerosols regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations" (Li et al., Atmos. Chem. Phys. 2024)

This dataset contains the processed EMAC simulation used as input to the clustering algorithm and the resulting regimes discussed in Li et al. (Atmos. Chem. Phys., 2024).

Trapped-Ion Quantum Simulation of Electron Transfer Models with Tunable Dissipation

This package includes the data, the theory, and the source code to plot both in Mathematica to reproduce the figures of the paper.

SWAN wave model simulations for Hornsund, Svalbard, 07.2015-06.2023

Results of SWAN model simulations from the paper: Herman, A., Swirad, Z., Moskalik, M., 2024, Increased exposure of the shores of Hornsund (Svalbard) to wave action due to a rapid shift in sea ice conditions. submitted to Elementa: The Science of Anthropogeny. The dataset contains SWAN results from three stations: Gåshamna (GAS; 76.9506°N, 15.7710°E, 22 m depth), Veslebogen (VES; 76.9951°N 15.4881°E, 16 m depth) and Hansbukta (HBK; 77.0031°N, 15.6298°E, 22 m depth).  For each station, 1D wave energy spectra and integral wave parameters are available, hourly from 01.07.2015 to 30.06.2023. There is one *.mat file for each station. The contents of these files is described in the text file info.txt.

Simulation data of Schmidt et al., An Electro-Chemo-Mechanic Model Resolving Delamination between Components in Complex Microstructures of Solid-State Batteries, 2024, DOI: https://doi.org/10.1149/1945-7111/ad76dc

This data set includes the simulation results of the relevant simulations published in the paper: "Schmidt et al., An Electro-Chemo-Mechanic Model Resolving Delamination between Components in Complex Microstructures of Solid-State Batteries, 2024, DOI: https://doi.org/10.1149/1945-7111/ad76dc". Please refer to the paper for the details of the model as well as the parameterization of the model for the respective simulations. The data is provided in a zip archive. After extracting you find a short README.txt with further hints on the structure and available data.

Movies of the simulations performed in Baïsset et al. paper entitled « Weakening induced by phase nucleation in metamorphic rocks: insights from numerical models »

Here you can find the movies of the evolution of the accumulated plastic strain of the simulations performed in the study. You will also find the table that summarizes the conditions of the different simulations (Table 1), as well as the colorbar (legend.png) corresponding to all the movies.

Dataset for manuscript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model"

Datasets and Jupyterlab python script for plotting all figures relevant to the mansucript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model" by Herbert et al. https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1538/ Data needs to be unzipped and paths (input and output) updated in the jupyterlab python script.

openodc-byMar 2022View details →

Data from: Data-driven analysis of oscillations in Hall thruster simulations & Data-driven sparse modeling of oscillations in plasma space propulsion

Data from: Data-driven analysis of oscillations in Hall thruster simulations   - Authors: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino - Contact email: <a href="mailto:dmaddalo@ing.uc3m.es">dmaddalo@ing.uc3m.es</a> - Date: 2022-03-24 - Keywords: higher order dynamic mode decomposition, hall effect thruster, breathing mode, ion transit time, data-driven analysis - Version: 1.0.4 - Digital Object Identifier (DOI): <a href="https://doi.org/10.5281/zenodo.6359505">10.5281/zenodo.6359505</a> - License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/">Open Data Commons Attribution License</a>   Abstract   This dataset contains the outputs of the HODMD algorithm and the original simulations used in the journal publication: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino, "Data-driven analysis of oscillations in Hall thruster simulations", 2022 Plasma Sources Sci. Technol. 31:045026. Doi: <a href="https://iopscience.iop.org/article/10.1088/1361-6595/ac6444">10.1088/1361-6595/ac6444</a>. Additionally, the raw simulation data is also employed in the following journal publication: Borja Bayón-Buján and Mario Merino, "Data-driven sparse modeling of oscillations in plasma space propulsion", 2024 Mach. Learn.: Sci. Technol. 5:035057. Doi:<a href="https://iopscience.iop.org/article/10.1088/2632-2153/ad6d29"> 10.1088/2632-2153/ad6d29</a>   Dataset description   The simulations from which data stems have been produced using the full 2D hybrid PIC/fluid code <a href="https://ep2.uc3m.es/assets/docs/pubs/conference_proceedings/domi19b.pdf">HYPHEN</a>, while the HODMD results have been produced using an adaptation of the original <a href="https://doi.org/10.1137/15M1054924">HODMD algorithm</a> with an improved <a href="https://doi.org/10.1063/1.4863670">amplitude calculation routine</a>. Please refer to the relative article for further details regarding any of the parameters and/or configurations.   Data files   The data files are in standard Matlab .mat format. A recent version of <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> is recommended. The HODMD outputs are collected within 18 different files, subdivided into three groups, each one referring to a different case. For the file names, "case1" refers to the nominal case, "case2" refers to the low voltage case and "case3" refers to the high mass flow rate case. Following, the variables are referred as: <ul> <li>"n" for plasma density</li> <li>"Te" for electron temperature</li> <li>"phi" for plasma potential</li> <li>"ji" for ion current density (both single and double charged ones)</li> <li>"nn" for neutral density</li> <li>"Ez" for axial electric field</li> <li>"Si" for ionization production term</li> <li>"vi1" for single charged ions axial velocity</li> </ul> In particular, axial electric field, ionization production term and single charged ions axial velocity are available only for the first case. Such files have a cell structure: the first row contains the frequencies (in Hz), the second row contains the normalized modes (alongside their complex conjugates), the third row collects the growth rates (in 1/s) while the amplitudes (dimensionalized) are collected within the last row. Additionally, the time vector is simply given as "t", common to all cases and all variables. The raw simulation data are collected within additional 15 variables, following the same nomenclature as above, with the addition of the suffix "_raw" to differentiate them from the HODMD outputs.   Citation   Works using this dataset or any part of it in any form shall cite it as follows. The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available. Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.6359505.   Acknowledgments   This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (MARETERRA-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). F. Terragni was also supported by the Fondo Europeo de Desarrollo Regional, Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación, under grants MTM2017-84446-C2-2-R and PID2020-112796RB-C22.

Occupant Simulation Data based on Honda Accord 2024 Simplified Passenger Model and Full-factorial Sampling with 3,125 samples and HIII05F, HIII50M, HIII95M

Database and FE-models with 9,375 Honda Accord 2014 passenger occupant simulations.

Occupant Simulation Database and FE-Model based on Honda Accord 2024 Simplified Passenger Model and SOBOL Sampling with 8,192 samples and HIII05F, HIII50M, HIII95M

Database and FE-models with 24,576 Honda Accord 2014 passenger occupant simulations.

Occupant Simulation Database and FE-Model based on Honda Accord 2024 Simplified Passenger Model and SOBOL Sampling with 256 samples and HIII05F, HIII50M, HIII95M

Database and FE-models with 768 Honda Accord 2014 passenger occupant FE-simulations.