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2,208 results for “coupling”
IMMERSE Horizon 2020 Project Downstream User Toolbox – data for tutorial on impact of wave coupling on surface particle dispersion simulations
<p>Exemplary data for tutorial on impact of wave coupling on surface particle dispersal simulations<br> <a href="https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU">https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU</a><br> created as part of the downstream user toolbox of the IMMERSE Horizon 2020 project (<a href="https://immerse-ocean.eu/">https://immerse-ocean.eu/</a>).</p> <p>In the tutorial the impact of new options for the representation of wave-current interactions in the NEMO ocean model (<a href="https://www.nemo-ocean.eu/">https://www.nemo-ocean.eu/</a>) on surface particle simulations are tested in a case study for the Mediterranean Sea. The tutorial consists of two jupyter notebooks: Parcels_CalcTraj.ipynb and CompTraj_uncoupledVScoupled.ipynb. Parcels_CalcTraj.ipynb calculates Lagrangian particle trajectories based on velocity output from ocean only as well as coupled ocean-wave model simulation by making use of the OceanParcels software (<a href="https://oceanparcels.org/">https://oceanparcels.org/</a>). CompTraj_uncoupledVScoupled.ipynb compares dispersal statistics of Lagrangian particle trajectories calculated from ocean-only vs coupled ocean-wave model simulations.</p> <p>This repository contains the surface velocity and ocean model grid data needed to run Parcels_CalcTraj.ipynb, as well as the trajectory data produced by Parcels_CalcTraj.ipynb, which is needed to run CompTraj_uncoupledVScoupled.ipynb. The surface velocity data stems from two simulations with a regional high-resolution (1/24° horizontal resolution) model configuration for the Mediterranean Sea: a coupled ocean-wave model simulation and a complimentary ocean-only simulation. These model simulations make use of the NEMO v4.2-RC ocean model, the Wave Watch 3 v.6.07 wave model, the OASIS3-MCT coupler, and ECMWF atmospheric fields; they are described in detail in IMMERSE deliverable D5.7 “Assessment of wave-current effects on the circulation in theMed-MFC system”<strong>.</strong></p>
Strong collisionless coupling between an unmagnetized driver plasma and a magnetized background plasma
<p>This repository contains some of the simulation data presented in the recent article in plasma physics titled "Strong collisionless coupling between an unmagnetized driver plasma and a magnetized background plasma" (<a href="https://arxiv.org/abs/2302.00149">https://arxiv.org/abs/2302.00149</a>). The data available are for 1D particle-in-cell (PIC) simulations that consider the interaction between a uniform unmagnetized driver plasma flowing against a uniform magnetized background plasma, for multiple values of the driver density and background magnetic field. The simulations were performed with OSIRIS, a massively parallel and fully-relativistic, PIC code.</p> <p>Using the data from the simulations, we studied the coupling between the plasmas and determined the compression ratio and the velocities of the magnetic cavity and magnetic compression that were visible in the simulations. More information on the simulations and on the obtained results are presented in the article.</p> <p>The datasets contain the main data of some of the simulations presented in the paper (.h5 files), the coupling parameters measured in the simulations (coupling_data.csv), and a Jupyter Notebook file to look at the simulation results from the available datasets (read_dataset.ipynb).</p>
Data of publication: "Collective atom-cavity coupling and nonlinear dynamics with atoms with multilevel ground states"
<p>The uploaded files contain the raw data of the measurements and simulations presented in <a href="https://doi.org/10.1103/PhysRevA.107.023714">https://doi.org/10.1103/PhysRevA.107.023714</a></p>
Magnetic coupling of divalent metal centers in postsynthetic metal exchanged bimetallic DUT-49 MOFs by EPR spectroscopy
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20201111_ULEI_21_DUT49Mn@7K </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20201111_ULEI_00_DUT49Mn@simulation </strong>folder includes computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>DUT49Cu – </strong>DUT-49(Cu) MOF, <strong>DUT49Mn – </strong>DUT-49(Mn) MOF, <strong>DUT49CuZn – </strong>DUT-49(CuZn) MOF, <strong>DUT49MnCu – </strong>DUT-49(MnCu) MOF.</li> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> </ul> </li> </ul> <p>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</p>
Dynamically coupled kinetic chemistry in brown dwarf atmospheres I. Performing global scale kinetic modelling
<p>Gifs and Exo-FMS GCM output from the 3D brown dwarf atmospheric simulations in Lee, Tan and Tsai (2023). </p> <p>Animated gifs for each effective temperature (Teff - first number in filename) of the brown dwarf (OLR and CH4 VMR). The gifs frames are every hour of simulation for 4 simulated days.</p> <p>Exo-FMS GCM output in netCDF format containing the 3D T-p structure and chemical results from the coupled mini-chem and GCM model for each Teff simulation (number in filename).</p> <p>`average' is the averaged output of the last 100 days.</p> <p>`daily' is the snapshot at the end of the simulation.</p>
A Linked Application of Discrete Differential Evolution Algorithm Coupled with Simulation- Optimization Model and Comparative Analysis by Genetic Algorithm for Discrete Groundwater Management Problems
<p>Complete dataset of publication name as "The complete publication dataset is "A Discrete Differential Evolution- Linear Programming Algorithm for Groundwater Management Problems." You can find all the written codes in the zip file.</p>
Ionome analysis of Salmonella mutants by Inductively coupled plasma mass spectrometry (ICP-MS)
<p>In many Gram-negative bacteria, the stress sigma factor of RNA polymerase, σS/RpoS, remodels global gene expression to reshape the physiology of quiescent cells and ensure their survival under non-optimal growth conditions. In the foodborne pathogen <i>Salmonella enterica</i> serovar Typhimurium, σS is also required for biofilm formation and virulence.</p><p>We have previously shown that a Δ<i>rpoS</i> mutation affects the <i>Salmonella</i> ionome. Indeed, inductively coupled plasma mass spectrometry analyses have unraveled a significant effect of the Δ<i>rpoS </i>mutation on the cellular concentration of manganese, magnesium, cobalt and potassium, suggesting that σS controls fluxes of ions that might be important for the fitness of quiescent cells (Metaane et al. 2022, PLoS ONE 17(3): e0265511).</p><p>Study: These findings prompted us to evaluate the impact on the<i> Salmonella</i> ionome of deletions of genes encoding the <i>Salmonella</i> Mn2+ transporters (<i>sitABCD</i> and <i>mntH</i>), the Co2+ transporter (<i>cbiMNQO</i> operon) and small proteins of unkown function (<i>yqaE</i> and <i>yqjDEK</i>) that accumulate in quiescent <i>Salmonella</i> under the tight control of σS (Levi-Meyrueis et al. PloS one. 2014; 9(5):e96918, Lago et al. Scientific reports. 2017; 7(1):2127 and Metaane et al. 2022, PLoS ONE 17(3): e0265511).</p><p>Material and Methods: Cell-associated contents of several elements were measured by inductively coupled plasma mass spectrometry (ICP-MS) as previously described in Metaane <i>et al </i>2022 PLoS ONE 17(3): e0265511.Dried cell pellets were prepared by V. Monteil and F. Norel (Institut Pasteur, Université de Paris, CNRS UMR3528, Biochimie des Interactions Macromoléculaires, F-75015, Paris, France). Cell-associated contents of several elements were measured by S. Ayrault and L. Bordier (ICP-MS platform, Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRSUVSQ,Université Paris-Saclay, 91191, Gif-sur-Yvette, France)</p><p><strong>This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).</strong></p><p><strong>Linked studies:</strong></p><ul><li>NOREL Francoise, MONTEIL Veronique, DOUCHE Thibaut, & MATONDO Mariette. (2023). Global effects of deletions of the sitABCD, mntH, cbiMNQO and corA genes, encoding transporters for manganese, cobalt and magnesium on protein abundance in Salmonella enterica serovar Typhimurium grown to stationary phase in LB. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8279780</li><li>Metaane S, Monteil V, Douché T, Giai Gianetto Q, Matondo M, Maufrais C, Norel F. Loss of CorA, the primary magnesium transporter of <i>Salmonella, </i>is alleviated by MgtA and PhoP-dependent compensatory mechanisms. PloS one. 2023;18(9):e0291736.</li></ul>
Dataset supporting the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold. ACS Nano 17, 10608 (2023)"
<p>Dataset corresponding to theoretical calculations in the paper "Large Orbital Moment of Two Coupled Spin‑Half Co Ions in a Complex on Gold" ACS Nano 17, 10608 (2023), https://pubs.acs.org/doi/10.1021/acsnano.3c01595</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:<br> .siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (https://doi.org/10.5281/zenodo.3581159).<br> CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).<br> .agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</p>
Assessing the prospective environmental performance of hydrogen from high-temperature electrolysis coupled with concentrated solar power
<p>Hydrogen is currently being promoted because of its advantages as an energy vector, its potential to decarbonise the economy, and strategical implications in terms of energy security. Hydrogen from high-temperature electrolysis coupled with concentrated solar power (CSP) is especially interesting since it enhances the last two aspects and could benefit from significant technological progress in the coming years. However, there is a lack of studies assessing its future environmental performance. This work fills this gap by carrying out a prospective life cycle assessment based on the expected values of key performance parameters in 2030. The results show that parabolic trough CSP coupled with a solid oxide electrolyser is a promising solution under environmental aspects.<br> It leads to a prospective hydrogen carbon footprint (1.85 kg CO2 eq/kg H2) which could be classified as low-carbon according to current standards. The benchmarking study for the year 2030 shows that the assessed system significantly decreases the hydrogen carbon footprint compared to future hydrogen from steam methane reforming (81% reduction) and grid electrolysis (51%), even under a considerable penetration of renewable energy sources.</p>
The coupled ice sheet-Earth system model Bern3D v3.0: Model output
<p>This dataset contains model output of climate and ice sheet variables for the simulations performed in the study:</p> <p>Pöppelmeier, F., Joos, F., Stocker, T. F. (2023). The coupled ice sheet-Earth system model Bern3D v3.0. Journal of Climate.</p> <p>2D and 3D output variables are available for the preindustrial (PI) and Last Glacial Maximum (LGM) control simulations. Timeseries output is provided for CO<sub>2</sub> experiments for which CO<sub>2</sub> concentrations were increased to 2 and 4 times PI concentrations with rates of 0.5, 1, and 2% per year. Timeseries output is also provided for the simulation of the entire last glacial cycle in the standard setup and with logarithmically scaled dust for the aerosol radiative forcing. More details are provided in the above mentioned manuscript.</p>
A localization transition underlies the mode-coupling crossover of glasses
<p>This dataset is associated to "A localization transition underlies the mode-coupling crossover of glasses" by D. Coslovich, A. Ninarello and L. Berthier [<a href="https://arxiv.org/abs/1811.03171">https://arxiv.org/abs/1811.03171</a>].</p> <p>It includes post-processed data and workflow to reproduce the analysis and the figures of the article and of the supplemental information.</p> <p><strong>Supplementary information is available in the Supplement section of the project document (project.pdf).</strong></p> <p>The easiest way to reproduce the analysis and figures, and then check the results, is to use the make script:</p> <pre><code class="language-bash">./make all</code></pre> <p>Alternatively, the analysis and figures can be reproduced in any of the following ways</p> <ul> <li>following the workflow described in the <a href="https://orgmode.org">org-mode</a> project file project.org</li> <li>using the individual bash and gnuplot scripts in src/ and plots/</li> </ul> <p>Folders and files description:</p> <ul> <li>analysis/: post-processed data</li> <li>src/: bash, python and gnuplot scripts needed to reproduce the analysis</li> <li>plots/: eps figures that appear in the paper and supplemental information and associated gnuplot scripts</li> <li>make: convenience script to setup the python environment, analyze the data and reproduce the figures</li> <li>project.org: org-mode project file with workflow and supplemental information</li> <li>project.pdf: pdf project file with workflow and supplemental information</li> <li>project.bib: bibtex bibliography associated to the project</li> <li>project.setup: org-mode export configuration</li> </ul> <p>Dependencies:</p> <ul> <li>numpy (1.21.6)</li> <li>scipy (1.11.1)</li> <li>argh (0.26.2)</li> <li><a href="https://pypi.org/project/atooms/">atooms</a> (1.9.1)</li> <li>gnuplot (5.0.0)</li> </ul> <p>The analysis scripts have been tested with python 3.8. The org-mode project file has been tested with org version 9.1.13.</p> <p>Note: this dataset does not contain (at least yet) the particle configurations associated to saddle points, only the post-processed files containing selected properties of their normal modes.</p> <p>Changelog:</p> <ul> <li>1.2.2 <ul> <li>fix requirements</li> </ul> </li> <li>1.2.1 <ul> <li>fix ./src/adiff.py</li> <li>fix final check of ./make all</li> <li>improve pdf layout</li> <li>improve handling of org properties</li> </ul> </li> <li> <ul> </ul> </li> <li> <ul> </ul> </li> <li>1.2.0 <ul> <li>add analysis of eigenvector-following optimizations</li> <li>small changes and fixes to analysis scripts</li> </ul> </li> <li>1.1.0 <ul> <li>add "all" target to ./make</li> <li>fix ./make check</li> <li>improve setup description</li> </ul> </li> <li>1.0.0 <ul> <li>initial submission</li> </ul> </li> </ul>
Coupling Microkinetics with Continuum Transport Models to Understand Electrochemical CO2 Reduction in Flow Reactors
<p>Data supporting the manuscript published in PRX Energy titled "Coupling Microkinetics with Continuum Transport Models to Understand Electrochemical CO2 Reduction in Flow Reactors". Jupyter notebook and included data for recreating the figures in the paper and for additional analysis.</p>
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
<p>This is the underlying dataset of the publication "Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3" by Gürses et al. (in press), Geoscientific Model Development. In addition to unstructured mesh information, it contains the results of ocean biogeochemistry in the Regulated Ecosystem Model version 3 (REcoM3) coupled to the ocean and sea ice model FESOM2.1. The model simulations cover the period 1958 to 2021 and are forced with observed atmospheric CO<sub>2</sub> and JRA55-do atmospheric reanalyses. Three simulations are provided:</p> <p><strong>simulation A:</strong> with varying climate forcing conditions and varying atmospheric CO<sub>2</sub></p> <p><strong>simulation B:</strong> with constant climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p><strong>simulation D:</strong> with varying climate forcing conditions and constant atmospheric CO<sub>2</sub></p> <p>The following 2D/3D monthly-averaged fields (data period is given in parentheses) are provided on the native model grid:</p> <ul> <li><strong>Alk:</strong> Alkalinity (2012-2021)</li> <li><strong>CO2f:</strong> Air-Sea CO<sub>2</sub> flux (1800-2021)</li> <li><strong>DFe: </strong>Dissolved Iron concentration (2012-2021)</li> <li><strong>DIN:</strong> Dissolved Inorganic Nitrogen concentration (2012-2021)</li> <li><strong>DIC:</strong> Dissolved Inorganic Carbon concentration (1800, 1994-2021)</li> <li><strong>DSi:</strong> Dissolved Inorganic Silicon concentration (2012-2021)</li> <li><strong>DiaChl:</strong> Chlorophyll a concentration of diatoms (2012-2021)</li> <li><strong>DetC:</strong> Carbon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetCalc: </strong>Calcite concentration in slow-sinking detritus (2012-2021)</li> <li><strong>DetSi: </strong>Silicon concentration in slow-sinking detritus (2012-2021)</li> <li><strong>idetz2c:</strong> Carbon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2calc:</strong> Calcite concentration in fast-sinking detritus (2012-2021)</li> <li><strong>idetz2si:</strong> Silicon concentration in fast-sinking detritus (2012-2021)</li> <li><strong>HetC:</strong> Small zooplankton carbon biomass (2012-2021)</li> <li><strong>MLD:</strong> Mixed Layer Depth (2012-2021)</li> <li><strong>NPPn:</strong> Net Primary Production of small pyhtoplankton (2012-2021)</li> <li><strong>NPPd:</strong> Net Primary Production of diatoms (2012-2021)</li> <li><strong>O2:</strong> Dissolved Oxygen concentration (2012-2021)</li> <li><strong>PhyChl:</strong> Chlorophyll a concentration of small phytoplankton (2012-2021)</li> <li><strong>Zoo2C:</strong> Macrozooplankton carbon biomass (2012-2021)</li> <li><strong>pCO2s:</strong> Partial pressure of carbon dioxide of the surface ocean (1970-2021)</li> <li><strong>salt:</strong> Salinity (2012-2021)</li> <li><strong>temp:</strong> Temperature (2012-2021)</li> <li><strong>w:</strong> Vertical velocity (2012-2021)</li> </ul> <p>Please contact the corresponding author (ozgur.gurses@awi.de) for further information.</p>
Stable isotope and conservative tracer data used to estimate uptake of stream water dissolved organic carbon (DOC) through a whole-stream addition of a ¹³C-DOC tracer coupled with laboratory measurements of bioavailability of the tracer and stream water DOC using lability profiling with bioreactors
We performed a whole-stream addition of a ¹³C-DOC tracer and made laboratory measurements of the biological availability of the tracer as well as stream water DOC. The study was performed in October 2002 in a 1.27 km stretch of the third-order White Clay Creek in southeastern Pennsylvania. The tracer was prepared as a cold-water leachate of ¹³C-labeled tulip poplar saplings and it was added to the stream along with sodium bromide, a conservative tracer, over a 2-h period. Stream water samples were collected at 8 downstream stations over an 8-h period, filtered, and analyzed for concentrations of bromide and DOC. DOC was measured by Pt-catalyzed, persulfate oxidation, Br- was analyzed by ion chromatography, and C isotope samples were rotary evaporated, acidified, lyophilized, combusted, and the CO₂ analyzed with an elemental analyzer interfaced with an isotope ratio mass spectrometer. Lability profiling of the ¹³C-DOC tracer and stream water DOC were performed with a series of plug-flow bioreactors of increasing empty-bed contact times with the concentration of biodegradable DOC operationally defined as the difference between the DOC concentrations in the influent and effluent waters of the bioreactors. The bioreactor measurements were performed 2 days after the whole-stream release. Data were analyzed to estimate the uptake of stream water DOC associated with labile and semi-labile fraction of biodegradable DOC. These data have been previously used in a 2008 publication in Freshwater Biology, doi:10.1111/j.1365-2427.2007.01941.x.
MCR LTER: Coral Reef: Coupled Natural-Human Systems: GPS-enabled survey of fish in Moorea’s lagoons
This dataset includes counts and sizes of fish within Moorea’s lagoons, with more than 130,000 fish recorded between 2018 and 2021. In summer of each year observers swim through Moorea’s lagoons, observing a 5-m wide swath and recording species and sizes of fish encountered in every minute of swim (about 10 linear meters of sampling). The fish counts are limited to a fixed species list (57 taxa) which was chosen to capture most of the reef associated species targeted in Moorea’s fishery, as well as important herbivores. Only fish larger than 10 cm were recorded, a size chosen to match the small end of fish observed in the catch. The resulting counts were georeferenced based on simultaneously collecting GPS data. Data are presented here on the scale of a minute of sampling. This scale coincides with benthic data which was simultaneously collected but is presented in a different data set (EDI data package: knb-lter-mcr.4014.1) doi:10.6073/pasta/f1e4cbcd79ebbbb0b0cbb79ab2ff8901 These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396).
MCR LTER: Coral Reef: Coupled Natural-Human Systems: GPS Benthic Data
This dataset includes cover of benthic organisms and substrates in more than 600,000 photographic samples of Moorea’s lagoon habitats, collected between 2018 and 2021. In summer of each year swimmers towed downward facing cameras, taking 1.5 photos per second of habitats they passed over. The resulting pictures were processed through a computer vision algorithm (CoralNet), resulting in percent cover estimates in each photo of organisms such as coral and algae and substrates such as sand and rubble. These data were georeferenced based on simultaneously collecting GPS data. Data are presented here on the scale of the photograph, and are also aggregated to the scale of a minute of sampling and a transect of swimming. The latter scales are chosen to coincide with fish counting data which was simultaneously collected but is presented in a different data set (EDI data package ID: knb-lter-mcr.4013.1) doi:10.6073/pasta/ff3de88334edda65fef26de45ff26c1d These data were collected as part of CNH-L: Multiscale Dynamics of Coral Reef Fisheries: Feedbacks Between Fishing Practices, Livelihood Strategies, and Shifting Dominance of Coral and Algae (BCS-1714704) with additional support from the Moorea Coral Reef LTER (OCE- 1637396).
Dataset for "Attitude and orbit coupling of planar helio-stable solar sails"
<p>Dataset and figures of the "Numerical test cases" section in the paper</p> <p>Miguel, N., Colombo, C., (2019). Attitude and orbit coupling of planar helio-stable solar sails. <em>Celestial Mechanics and Dynamical Astronomy </em>131, 59. doi:10.1007/s10569-019-9937-x.</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>
Realistic modeling of mesoscopic ephaptic coupling in the human brain
<p>Comsol models with E-field distributions generated by dipole sources in a realistic head model and a stylized 'toy' model representing a sulcus.</p>
Propagating mechanisms of the 2016 Summer BSISO Event: air-sea coupling, vorticity, and moisture
<pre>This repository contains the data from the WRF+HYCOM coupled simulations and WRF simulations for the BSISO event in July and August, 2016. </pre>
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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.