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1,028 results for “simulation model”
High-resolution simulations of Mediterranean windstorm Adrian with the Meso-NH atmospheric model
<p>The dataset provides numerical simulations of Mediterranean windstorm Adrian of 29 October 2018 at two horizontal resolutions and using different representations of surface turbulent fluxes at the air-sea interface. The simulations are run with the Meso-NH non-hydrostatic mesoscale atmospheric model of the French research community, version 5.4, freely available under CeCILL-C license agreement: <a href="http://mesonh.aero.obs-mip.fr/" target="_blank" rel="noopener">http://mesonh.aero.obs-mip.fr/</a></p> <p>The data is formatted in Network Common Data Form (NetCDF) using the CF Metadata Conventions and standard Meso-NH names for physical variables. The data files are named as following: <strong>EXP.N.CONTENT.nc</strong></p> <ul> <li><strong>EXP</strong> describes the numerical experiment (name of the parameterization of surface turbulent fluxes or their absence) </li> <li><strong>N</strong> the horizontal resolution (1=1000m, mesoscale simulation; 2=200m, large-eddy simulation) </li> <li><strong>CONTENT</strong> the type of data (3D zoom over the windstorm center or vertical profiles in the same area at 1530 UTC, or 2D surface fields every 6 min from 12 to 18 UTC)</li> </ul>
Whole-body physiology model to simulate respiratory depression of fentanyl and associated naloxone reversal
<p>Opioid use in the United States and abroad is an endemic part of society with yearly increases in overdose rates and deaths. As rates of overdose incidence increase, the use of the safe and effective reversal agent, naloxone, in the form of a nasal rescue spray is being fielded and used by emergency medical technicians (EMTs) at a greater and greater rate. Despite advances in the deployment of these rescue products, deaths are continuing to increase. There is evidence that repeated dosing of a naloxone nasal spray (such as Narcan) is becoming more common due to the amount and type of opiate being administered. Despite the benefits of naloxone related to opioid reversals, we lack repeated dosing guidelines as a function of opiates and the amount the patient has taken. Goal-directed rescue dosing, where respiratory markers such as oxygen saturation or end-tidal carbon dioxide, are being used as an indication of the patient's recovery. These rescue methods require time, training, and understanding by the EMT to administer with most patients receiving naloxone doses with no follow-up or additional monitoring. To measure repeat dosing guidelines, we construct a whole-body model of the pharmacokinetics and dynamics of an opiate, fentanyl on respiratory depression. We then construct a model of nasal deposition and administration of naloxone to investigate repeat dosing requirements for large overdose scenarios. We demonstrate that naloxone is highly effective at reversing the respiratory symptoms of the patient and recommend dosing requirements as a function of the fentanyl amount administered. By designing the model to include circulation and respiration we investigate physiological markers that may be used in goal-directed therapy rescue treatments.</p>
Automatic message sequence chart creation from simulation run of the parametric colored Petri net model of the Chandy-Lamport algorithm with four processes
<p><span>The video shows the creation of the message sequence chart from a simulation run of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm using the CPN tool with four constituting processes. The picture shows the resulting message sequence chart. </span></p> <p><strong><span>Message Sequence Chart of Parametric Model With 4 Processes via Automatic Simulation Run_SuppInfo.mp4</span></strong><span>: This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The model's number of constituting processes is parametric and was set to four. The video was generated using the authors' updated CPN tool extension server. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition at each step in an automatic simulation run.</span></p> <p><strong><span>Picture of Message Sequence Chart of Parametric Model With 4 Processes_SuppInfo.png:</span></strong><span> This picture shows the automatically generated message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm that is visible in the above video clip. The number of constituting processes was set to four. </span></p>
A new climatology of depth of nitracline in the Bay of Bengal for improving model simulations
<p>This link contains:</p> <p>Sensor_data.nc - This file contains Bioargo data with nitrate sensor used in the manuscript in figures 1, and 3 to 5</p> <p>NO3_Verical_Profile.xlsx - Contains the vertical profile of the nitrate used in figure 3</p> <p>Bottle_SurDensity+1_D26_Nitracline_depth.xlsx - contains the depths of surface density+1, D26, and Nitracline collected from various cruises/observational data mentioned in Table 1</p>
Simulation and laboratory eddy current testing data - modelling compound defects via perturbation theory
<p>This dataset serves to fit and validate a perturbation approach to the modelling eddy current signals of compound defects. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection). The simulation data was generated with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. The simulation data is supplied as csv. The laboratory data was gathered by Rainer Pohl in the eddy current laboratory of BAM, section 8.4. It is supplied in the DICONDE data format. The first frame of the pixel array in the DICONDE files corresponds to the real part and the second frame corresponds to the imaginary part of the signal. The data set is analyzed in an upcoming article.</p> <p><span> </span></p>
Clinical phenotypes in acute and chronic infarction explained through human ventricular electromechanical modelling and simulations
<p>This dataset includes the meshes, model parameters, and Alya executable binary for simulating acute and chronic stage post-myocardial infarct using Alya, to replicate the results in the article <a href="https://doi.org/10.7554/eLife.93002.1">https://doi.org/10.7554/eLife.93002.1</a></p> <p>For each scenario simulated, a baseline simulation folder is provide with all the required meshes, fields, and model parameters necessary to run an Alya simulation. An additional series of models with variability in ionic conductances is also included for each scenario under the folder <scenario>_pom/, under which 20 simulations are included. For each simulation, only the file describing the ionic conductance scaling factors (ventricular_cell.txt) are included, all other files required to run each particular simulation can be found in the <scenario>_baseline/ version. </p> <p>The file structure is as follows:</p> <ul> <li>Alya executable binary</li> <li>control_baseline</li> <li>control_pom</li> <li>75%_transmural_scar <ul> <li>acute <ul> <li>bz1_baseline</li> <li>bz1_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> <li>bz2_baseline</li> <li>bz2_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> <li>bz3_baseline</li> <li>bz3_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> </ul> </li> <li>chronic <ul> <li>rz1_baseline</li> <li>rz1_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> <li>rz2_baseline</li> <li>rz2_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> </ul> </li> <li>fast_pacing <ul> <li>alternans1</li> <li>alternans4</li> </ul> </li> </ul> </li> </ul> <p>The simulation files in alternans4/ was use to generate results Figure 6 of the accompanying article, and alternans1/ was used to generate results Figure 7. </p> <p>The Alya executable binary has been built on ARCHER2 with the following loaded modules:</p> <p>1) craype-x86-rome <br>2) libfabric/1.12.1.2.2.0.0<br>3) craype-network-ofi <br>4) perftools-base/22.12.0 <br>5) xpmem/2.5.2-2.4_3.30__gd0f7936.shasta <br>6) bolt/0.8 <br>7) epcc-setup-env <br>8) load-epcc-module <br>9) gcc/11.2.0 <br>10) craype/2.7.19 <br>11) cray-dsmml/0.2.2 <br>12) cray-mpich/8.1.23 <br>13) cray-libsci/22.12.1.1 <br>14) PrgEnv-gnu/8.3.3 <br>15) tk/8.6.13 <br>16) tcl/8.6.13 <br>17) cray-python/3.9.13.1<br>18) matplotlib/3.7.2<br><br>To replicate the study, access to an installation of the code in the Nord supercomputer can be requested to <a title="mailto:mariano@elem.bio" href="mailto:mariano@elem.bio">mariano@elem.bio</a></p>
Simulated terrestrial biosphere variables across Termination V (iLOVECLIM model)
<p>The following files contain output data from the 32-kyr iLOVECLIM simulation covering Termination V: both sequences start at 436 kyr BP and end at 404 kyr BP with a yearly time step.</p> <ul> <li>Simulated_carbon_stock.nc: the average simulated carbon stock over latitudinal bands for each of the four carbon components (green biomass, structural biomas, slow Soil Organic Matter (SOM) and fat SOM) and the total carbon stock (sum of the four components).</li> <li>Simulated_tree_fraction.nc: the global simulated tree fraction (in %).</li> </ul>
Are terrestrial biosphere models fit for simulating the global land carbon sink?
<p>This repository contains the data and code required for reproducing the results presented in the paper "Are terrestrial biosphere models fit for simulating the global land carbon sink?" by Seiler et al., 2021. The study evaluates an ensemble of terrestrial biosphere models (<a href="https://sites.exeter.ac.uk/trendy/">TRENDY</a>; v9; S3 simulations) against a wide range of reference data using the Automated Model Benchmarking R package (AMBER; version 1.1.1). The only requirement for reproducing our results is access to a Linux machine with <a href="https://docs.conda.io">conda</a>, an open-source package management system and environment management system, installed. Follow the steps described in the <em>readme</em> file to install AMBER and run the analysis. The repository also contains all output produced by our analysis. </p>
Phanerozoic global climatic fields simulated using the FOAM ocean-atmosphere general circulation model
<p>These files contain the output of Phanerozoic global climate simulations conducted using the coupled ocean-atmosphere FOAM general circulation model. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. All simulations have been conducted using identical boundary conditions; pCO2: 2240 ppm, solar luminosity: 1368 W m-2, vegetation: rocky desert, orbital configuration: null eccentricity and minimum obliquity. Only the continental configuration was varied from one time slice to the other (sensitivity test to the continental configuration), using the reconstructions of Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/).</p> <p>The reader is referred to the associated paper for a full description of the model and boundary conditions.</p> <p>All file names use the following pattern: "[age]rd_1368W_EccN_[model_component]_2240ppm.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos', 'ocean' or 'coupl' (atmospheric and oceanic components, plus coupler).</p>
Near-inertial wave energetics modulated by background flows in a global model simulation
<p>This data set contains data from a forward global HYCOM simulation (EXPT 19.2) with realistic atmospheric forcing. This is a 4-km simulation with 41 layers. All data is on the native tri-polar grid. Data is stored as netcdf4 classic. The 2D data sets are 7055 x 9000 (lat x lon).</p> <p>More details and context of this data can be found in the article: <a href="https://doi.org/10.1175/JPO-D-21-0130.1">https://doi.org/10.1175/JPO-D-21-0130.1</a>. Please cite this article along with any use of this data.<br> Raja, K. J., Buijsman, M. C., Shriver, J. F., Arbic, B. K., & Siyanbola, O. (2022). Near-Inertial Wave Energetics Modulated by Background Flows in a Global Model Simulation, <em>Journal of Physical Oceanography</em>, <em>52</em>(5), 823-840.</p>
Experimental and model data for "Nitrogen oxide production in laser-induced breakdown simulating impacts on the Hadean atmosphere"
<p>This is raw data and supporting figures associated with the publication: Heays, A. N., Kaiserová, T., Rimmer, P. B., Knížek, A., Petera, L., Civiš, S., et al. (2022). Nitrogen oxide production in laser-induced breakdown simulating impacts on the Hadean atmosphere. <em>Journal of Geophysical Research: Planets</em>, 127, e2021JE006842. <a href="https://doi.org/10.1029/2021JE006842">https://doi.org/10.1029/2021JE006842</a></p> <p>Two data files contain model output of the ARGO atmospheric photochemistry code that was used to generate figures for Sec. 3 of the paper:</p> <ul> <li>ARGO_model_data_neutral_case.txt</li> <li>ARGO_model_data_reducing_case.txt</li> </ul> <p>The following data files contain a tabulation of laboratory-measured and modelled photoabsorption spectra as described in Sec. 2 of the paper. The A-G letter-encoding of these files follows Table 1 of the paper, and the spectral ranges correspond to the strongest bands of NO, N2O, and NO2. </p> <ul> <li>laboratory_spectrum_experiment_A_species_N2O.txt</li> <li>laboratory_spectrum_experiment_A_species_NO2.txt</li> <li>laboratory_spectrum_experiment_A_species_NO.txt</li> <li>laboratory_spectrum_experiment_B_species_N2O.txt</li> <li>laboratory_spectrum_experiment_B_species_NO2.txt</li> <li>laboratory_spectrum_experiment_B_species_NO.txt</li> <li>laboratory_spectrum_experiment_C_species_N2O.txt</li> <li>laboratory_spectrum_experiment_C_species_NO2.txt</li> <li>laboratory_spectrum_experiment_C_species_NO.txt</li> <li>laboratory_spectrum_experiment_D_species_N2O.txt</li> <li>laboratory_spectrum_experiment_D_species_NO2.txt</li> <li>laboratory_spectrum_experiment_D_species_NO.txt</li> <li>laboratory_spectrum_experiment_E_species_N2O.txt</li> <li>laboratory_spectrum_experiment_E_species_NO2.txt</li> <li>laboratory_spectrum_experiment_E_species_NO.txt</li> <li>laboratory_spectrum_experiment_F_species_N2O.txt</li> <li>laboratory_spectrum_experiment_F_species_NO2.txt</li> <li>laboratory_spectrum_experiment_F_species_NO.txt</li> <li>laboratory_spectrum_experiment_G_species_N2O.txt</li> <li>laboratory_spectrum_experiment_G_species_NO2.txt</li> <li>laboratory_spectrum_experiment_G_species_NO.txt</li> </ul> <p>The following file contains a tabulation of the full-spectral-range laboratory-measured photoabsorption spectrum of experiment A, along with a modelled spectrum.</p> <ul> <li><a href="https://zenodo.org/api/files/49f05962-9a34-4bbc-855c-1a0976f62531/laboratory_spectrum_experiment_A_full_spectrum.txt?versionId=79a70ade-63d1-4fd7-b423-176e27f8dc37">laboratory_spectrum_experiment_A_full_spectrum.txt </a></li> </ul> <p>The following file contains plots of the experimental spectra for all NxOy species in all measurements as well as the residual error of models fit to these spectra. Additional residual errors of model neglecting NxOy species indicates their contribution to the spectra.</p> <ul> <li>laboratory_spectrum_figures.pdf</li> </ul> <p> </p>
Model-informed target product profiles of long-acting- injectables for use as seasonal malaria prevention: code and simulation data
<p>This simulation data set and code reproduces the Figures and analysis of PLOS Global Public Health peer-reviewed article </p> <p><strong>Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention</strong></p> <p>Authors:</p> <p>Lydia Burgert<sup>1, 2</sup>, Theresa Reiker<sup>1, 2</sup>, Monica Golumbeanu<sup>1,2</sup>, Jörg J. Möhrle<sup>1, 2, 3</sup>, Melissa A. Penny*<sup>1, 2</sup></p> <p> </p> <p><sup>1</sup> Swiss Tropical and Public Health Institute, Basel, Switzerland</p> <p><sup>2</sup> University of Basel, Basel, Switzerland</p> <p><sup>3 </sup>Medicines for Malaria Venture, Geneva, Switzerland</p> <p>*Corresponding author: <a href="mailto:melissa.penny@unibas.ch">melissa.penny@unibas.ch</a></p>
Benchmark problems for transcranial ultrasound simulation: Datasets for intercomparison of compressional wave models
<p>This dataset contains the skull maps and modeling results associated with the forthcoming publication "Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models".</p>
ARCHIMED-φ simulation files for the simulation of Design A from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal
<p>Input files for the simulation of Design A in ARCHIMED-φ from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal.</p> <p>See https://archimed-platform.github.io/archimed-phys-user-doc/ for more details on the model.</p> <p>Make a simulation by opening a terminal at the root of the folder and type: `java -jar .\archimed-phys.jar .\DesignA_MockUpA_seed1_MAP_72.yml`.</p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
MPAS-Albany Land Ice model simulations of Humboldt Glacier, North Greenland, from 2007–2100
<p>This dataset contains model input and output in netCDF format, model code, and analysis scripts for simulations of Humboldt Glacier, North Greenland, through the 21st century (Hillebrand et al., 2022) using the MPAS-Albany Land Ice model (Hoffman et al., 2018). We calibrate parameters controlling basal traction, iceberg calving, and submarine melt against observations from 2007–2017. We then explore the glacier’s sensitivity to climate forcing, iceberg calving, and basal conditions in an ensemble of 24 simulations from 2007–2100. We further explore its sensitivity to uncertainties in ice-shelf melt, bed topography, and calving rate limits in targeted sensitivity experiments. Input files include surface mass balance, ocean thermal forcing, and subglacial runoff forcings provided by ISMIP6 (Nowicki et al., 2020; Slater et al., 2020). Output includes basal traction optimization solutions for the year 2007; annual 2D ice speed, basal shear and driving stresses, and geometry; annual 3D temperature; and grounded, floating, and global mass budgets at every timestep.</p> <p>References:</p> <p>Hillebrand, T. R., Hoffman, M. J., Perego, M., Price, S. F., and Howat, I. M. (2022): The contribution of Humboldt Glacier, northern Greenland, to sea-level rise through 2100 constrained by recent observations of speedup and retreat, The Cryosphere, 16, 4679–4700, <a href="https://doi.org/10.5194/tc-16-4679-2022">https://doi.org/10.5194/tc-16-4679-2022</a>.</p> <p>Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., et al. (2018). MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids. <em>Geoscientific Model Development</em>, <em>11</em>(9), 3747–3780.<a href="https://doi.org/10.5194/gmd-11-3747-2018"> https://doi.org/10.5194/gmd-11-3747-2018</a></p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., et al. (2020). Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models. <em>The Cryosphere</em>, <em>14</em>(7), 2331–2368.<a href="https://doi.org/10.5194/tc-14-2331-2020"> https://doi.org/10.5194/tc-14-2331-2020</a></p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., et al. (2020). Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution. <em>The Cryosphere</em>, <em>14</em>(3), 985–1008.<a href="https://doi.org/10.5194/tc-14-985-2020"> https://doi.org/10.5194/tc-14-985-2020</a></p>
Sub-national tailoring of malaria interventions in Mainland Tanzania: simulation of the impact of strata-specific intervention combinations using modelling
<p>Simulation dataset. </p>
Accompanying simulated data for "Go multivariate: a Monte Carlo study of a multilevel hidden Markov model with categorical data of varying complexity"
<p>The multilevel hidden Markov model (MHMM) is a promising vehicle to investigate latent dynamics over time in social and behavioral processes. By including continuous individual random effects, the model accommodates variability between individuals, providing individual-specific trajectories and facilitating the study of individual differences. However, the performance of the MHMM has not been sufficiently explored. Currently, there are no practical guidelines on the sample size needed to obtain reliable estimates related to categorical data characteristics We performed an extensive simulation to assess the effect of the number of dependent variables (1-4), the number of individuals (5-90), and the number of observations per individual (100-1600) on the estimation performance of group-level parameters and between-individual variability on a Bayesian MHMM with categorical data of various levels of complexity. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. Meanwhile, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the complexity of the data and the study objectives of the practitioners.</p> <p>This repository contains data generated for the manuscript: "Go multivariate: a Monte Carlo study of a multilevel hidden Markov model with categorical data of varying complexity". It comprehends: (1) model outputs (maximum a posteriori estimates) for each repetition (n=100) of each scenario (n=324) of the main simulation, (2) complete model outputs (including estimates for 4000 MCMC iterations) for two chains of each repetition (n=3) of each scenario (n=324). Please note that the empirical data used in the manuscript is not available as part of this repository. A subsample of the data used in the empirical example are openly available as an example data set in the R package <a href="https://cran.r-project.org/web/packages/mHMMbayes/index.html">mHMMbayes on CRAN</a>. The full data set is available on request from the authors.</p>
Dataset for "WUS256: An Adjoint Waveform Tomography Model of the Crust and Upper Mantle of the Western United States for Improved Waveform Simulations"
<p>This dataset contains the WUS256 seismic model and auxiliary data used in the creation of the model (Rodgers et al., 2022). WUS256 is a three-dimensional model of the seismic properties of crust and upper mantle of the western United States. The WUS256 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>Also included are the earthquake source parameters for the 72 inversion events and 18 validation events in ASCII text format. Lastly, we include a list of all waveforms used in the creation of WUS256. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p>This effort was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008. 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-833624</p>
Data generated by the model presented in the research article entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell"
<p>This repository provides all the data and scripts necessary to reproduce the line plots shown in the manuscript entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell".</p>
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.