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53 results for “Model membrane”
Datasets for the paper "Finite element modelling of the vibro-acoustic response in dielectric elastomer membranes"
<p>This upload contains the numerical datasets used for the numerical plots reported in the paper "Finite element modelling of the vibro-acoustic response in dielectric elastomer membranes" by G. Moretti et al., In <em>Electroactive Polymer Actuators and Devices (EAPAD) XXIV</em> (https://doi.org/10.1117/12.2612784).</p> <p>Please refer to the readme file for information on the files structure and content. </p>
3D models of the forearm's Interosseous membrane
<p> <strong>DATA CONTENT</strong></p> <p>This dataset corresponds to the 3D models generated through Micro-CT and standard CT of 5 cadaveric forearms. Each Folder contains the following subfolders:</p> <ul> <li>Bones: containing radius and ulna 3D STL models</li> <li>Insertion_Points : containing radial and ulnar attachment of the individual ligaments of the IOM</li> <li>IOM: contains the STL of the entire interosseous membrane </li> </ul> <p>We kindly ask to cite our work in case of usage of any of the presented models. You may contact the corresponding author if you wish more information about the data or if you consider some details are missing.</p> <p> <br> © The Balgrist 2019. </p> <p><br> Any redistribution or reproduction of part or all of the contents in any form is prohibited other than the following:</p> <p>• you may use the data as a part of a research project, providing you cite our institution and publication<br> • you may print or download to a local hard disk extracts for your personal and non-commercial use only<br> • you may copy the content to individual third parties for their personal use, but only if you acknowledge the authors as the source of the material</p> <p>You may not, except with our express written permission, distribute or commercially exploit the content. Nor may you transmit it or store it in any other website or other form of electronic retrieval system.</p> <p><br> Fabio Carrillo<br> ETH Zürich<br> University Hospital Balgrist<br> fabio.carrillo@balgrist.ch</p>
3D models for the ligaments of the Interosseous Membrane of 5 forearms with their biomechanical simulation scenes
<p>This dataset contains a group of 15 ligaments corresponding to the five specimens (3 per forearm) modeled as 3D tetrahedral meshes. In addition, 15 simulation scenes written in SOFA framework (INRIA) are supplied to implement stretch experiments. Details about the study are provided in the technical report.</p>
DeepBacs – Artificial labeling of E. coli membranes dataset and fnet/CARE models
<p>Training and test images of <em>E. coli </em>cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>Example image shows an <em>E. coli</em> bright field image and PAINT membrane image predicted by the neural network (scale bar is 1 µm).</p> <p> </p> <p><strong>Training and testing dataset</strong></p> <p><strong>Data type</strong>: Paired bright field and super-resolution images</p> <p><strong>Microscopy data type</strong>: Bright field and fluorescence microscopy (widefield and point accumulation for imaging in nanoscale topography (PAINT) images)</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli </em>K12 strain derivatives</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512x512 px<sup>2</sup> with different pixel sizes:</p> <p>1x tube lens: 158 nm (raw) and 19.75 nm (8x upscaled for PAINT images)</p> <p>1.5x tube lens: 106 nm (raw) (widefield fluorescence only)</p> <p> </p> <p><strong>fnet model (PAINT membrane images)</strong></p> <p>The fnet 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 200,000 steps on 33 paired images (image dimensions: (512 x 512 px²), patch size: (128 x 128 px²)) with a batch size of 4, a learning rate of 0.0004, 10% validation split and 4x data augmentation (flipping and rotation).</p> <p>Model weights can be used with the ZeroCostDL4Mic fnet 2D notebook.</p> <p> </p> <p><strong>CARE model (PAINT membrane images):</strong></p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 300 epochs (100 steps/epoch) on 33 paired images (image dimensions: 512 x 512 px², patch size: 256 x 256 px²) with a batch size of 4, a learning rate of 0.0004, 90/10% train/validation split and 4x data augmentation (flipping and rotation).</p> <p>Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin.</p> <p><br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
Dataset to "Hydrogen in tungsten trioxide by membrane photoemission and density functional theory modeling"
<p>Dataset to "Hydrogen in tungsten trioxide by membrane photoemission and density functional theory modeling" as published in Physical Review B, 103 (2021), 205304</p>
BRAIN Journal-Isomorphism Between Estes' Stimulus Fluctuation Model and a Physical- Chemical System-Figure 1. Two compartments containing solution separated by a membrane.
<p>In fact, this equation will be found first if one consults physical or chemical textbooks for<br> diffusion. Also one may be able to find already-existing diffusion simulators to see vivid images of<br> the process.</p>
Describing ion transport and water splitting in an electrodialysis stack with bipolar membranes by a 2-D model: Experimental validation
<p>Electrodialysis with bipolar membranes (EDBM) has drawn attention motivated by their application in gener- ating reagents from salts. Due to the water splitting (WS) occurring at the junction of the bipolar membranes (BPMs), where the anion and cation layers are in strict contact, H+ and OH- are released from the BPM producing acid and alkali on the respective compartment. Considering this application, the interest of this work is to provide further understanding of the mechanisms of WS and transport of species in EDBM. This work develops and utilizes, for the first time, an experimentally validated two-dimensional (2-D) computational model, in which the Navier-Stokes and Nernst-Planck equations are coupled with the description of WS given by the Second Wien effect. In addition, a 1-D geometry is also proposed to perform a comparison between electroneutrality and Poisson charge conservation. The model is computationally solved using COMSOL Multiphysics. According to simulations, electroneutrality is valid for 2-D geometries. Moreover, the semipermeable characteristics of the membranes are assessed by means of evidencing a polarization effect resulting in a double-electric layer. The model proposed predicts a significant proton leakage, and facilitates the study of WS within the BPMs.</p>
Gamma-hemolysin components: computational strategies for LukF-Hlg2 dimer reconstruction on a model membrane
<p>Project files provided as supporting information to the manuscript “Gamma-Hemolysin Components: Computational Strategies for LukF-Hlg2 Dimer Reconstruction on a Model Membrane”. The data set contains the following folders:</p> <ul> <li>LukF_Hlg2_distance: files with the minimum distance between LukF and Hlg2 as a function of time for the simulated replica (Fig. S1); files with the minimum distance between each LukF residue and the Hlg2 monomer and vice versa, averaged over the last 600 ns of simulation in the replica where the spontaneous dimerization is observed (fig. S5).</li> <li>HADDOCK_dimer_crystal_pore_displacement: files with the displacement between Hlg2 residues in the HADDOCK model dimer and the same dimer in the crystal pore, after alignment on the LukF monomer (for both the HADDOCK model in presence and in absence of the LukF N-ter) (Fig. S13)</li> <li>interface_area: files with the interface area between the LukF and the Hlg2 monomers in the replica where the spontaneous dimerization on the membrane is observed, as a function of time (total interface and contribution of LukF and Hlg2 rim domains) (Fig. S4)</li> <li>angles: files with the histograms of the angle between LukF and the axis perpendicular to the membrane, for the simulation of the single LukF monomer and for that capturing the spontaneous dimerization on the membrane (Fig. 4); file with the angle between the LukF and the Hlg2 axis as a function of time in the replica where the spontaneous dimerization is observed (Fig. S6)</li> <li>HADDOCK_scores: files with the HADDOCK scores of the predicted LukF-Hlg2 dimers and their RMSD values computed with respect to the same dimer in the crystal pore. The data are reported for the four top-scored models of each cluster ( for both the HADDOCK models in the presence and in the absence of the LukF N-ter) (Fig. 5).</li> <li>RMSD: files with the RMSD as a function of time for the LukF and the Hlg2 monomers in the replica where the spontaneous dimerization on the membrane is observed (Fig. 2, Fig. S2, Fig. S3)</li> <li>RMSF: files with the RMSF of the LukF and the Hlg2 residues in the replica where the spontaneous dimerization on the membrane is observed and in the simulations of the single monomers (Fig. 2)</li> <li>interaction_persistences: files with H-bond (side chain + backbone and backbone only atoms) salt-bridge, and hydrophobic contact persistence matrices for the single LukF monomer simulated alone (299 x 299) and for the LukF-Hlg2 dimer (299+280 x 299+280) (Fig. 4, Fig. S7 + interactions reported in the manuscript)</li> <li>distance_protein_membrane: files with the minimum distance between each monomer and the membrane, in the last 200ns of the simulation of spontaneous dimerization on the membrane (Figure S9).</li> <li>distance_residues_interface: files with the distance between functionally relevant residues measured along the simulation of the HADDOCK dimer in the absence of LukF N-terminus (Figure S14).</li> </ul>
Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
<p>The dynamics and organization of the actin cytoskeleton are crucial to many cellular events such as motility, polarization, cell shaping, and cell division. The intracellular and extracellular signaling associated with this cytoskeletal network is communicated through cell membranes. Hence the organization of membrane macromolecules and actin filament assembly are highly interdependent. Although the actin-membrane linkage is known to happen through many routes, the major class of interactions is through the direct interaction of actin-binding proteins with the lipid class containing poly-phosphatidylinositols (PPIs). Among the PPIs, phosphatidylinositol bisphosphate (PI(4,5)P<sub>2</sub>) acts as a significant factor controlling actin polymerization in the proximity of the membrane by binding to actin-associated proteins. The molecular interactions between these actin-binding proteins and the membrane lipids remain elusive. Here, using molecular modeling, analytical theory, and experimental methods, we investigate the binding of three different actin-binding proteins, mDia2, NWASP, and gelsolin, to membranes containing PI(4,5)P<sub>2</sub> lipids. We perform molecular dynamics simulations on the protein-bilayer system and analyze the membrane binding in the form of hydrogen bonds and salt bridges at various PI(4,5)P<sub>2</sub> and cholesterol concentrations. Our experimental study with PI(4,5)P<sub>2</sub>-containing large unilamellar vesicles mimics the computational experiments. Using the multivalencies of the proteins obtained in molecular simulations and the cooperative binding mechanisms of the proteins, we also propose a multivalent binding model that predicts the actin filament distributions at various PI(4,5)P<sub>2 </sub>and protein concentrations.</p>
Model systems with GPCRs embedded in multicomponent membranes
<p>Files required to run coarse-grained simulations on various lipid membranes with embedded adenosine A_2A and dopamine D_2 receptors. These simulations were performed to study the effect of the presence of polyunsaturated fatty acid on GPCR oligomerization. The detailed description of the aims, methodologies and results of this study are explained in the research paper [1]. The composition and purpose of each simulated system will also be explained in this paper [1].</p> <p>The Martini force field [2,3] was employed in the study, and the simulations were performed with version 4.5.x of the GROMACS package [4].</p> <p>For each system the following (in GROMACS compatible format) is provided:</p> <p>1) Initial structure (*Start.gro)</p> <p>2) Topology file (*.top)</p> <p>3) Index file (*.ndx)</p> <p>In addition, for systems other than those containing only one protein, the final structure is given (*End.gro).</p> <p>Other files required to run the simulations, which are common for all the systems, are also provided:</p> <p>4) Simulation parameter file (.mdp)</p> <p>5) Force field parameters (.itp)</p> <p> </p> <p><strong>References:</strong></p> <p> </p> <p>[1] Guixà-González et al., Membrane omega-3 fatty acids modulate the oligomerisation kinetics of adenosine A2A and dopamine D2 receptors. <em>Scientific Reports</em> <strong>6</strong>, Article number: 19839 (2016) <strong>DOI:</strong>10.1038/srep19839</p> <p>[2] Marrink et al., The MARTINI Force Field:  Coarse Grained Model for Biomolecular Simulations.<em> Journal of Physical Chemistry B </em><strong>111</strong>, 7812–7824 (2007), <strong>DOI:</strong>10.1021/jp071097f</p> <p>[3] Monticelli et al., The MARTINI Coarse-Grained Force Field: Extension to Proteins. <em>Journal of Chemical Theory and Computation </em><strong>4</strong>, 819–834 (2008), <strong>DOI:</strong>10.1021/ct700324x</p> <p>[4] Pronk et al., GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. <em>Bioinformatics </em><strong>29</strong> 845-854 (2013), <strong>DOI:</strong>10.1093/bioinformatics/btt055</p>
pLMMoRF: A web server that accurately predicts membrane-interacting molecular recognition features by employing a protein language model
<p>pLMMMoRF predictor scrips and MemMoRF prediction of the human proteome.</p>
Multifaceted Activity of Fabimycin: Insights from Molecular Dynamics Studies on Bacterial Membrane models
<p>This dataset presents a comprehensive collection of input data for Molecular Dynamics (MD) simulations performed using the GROMACS simulation software. The included systems cover various membrane environments, each with distinctive properties. The systems consist of:</p> <ol> <li><strong>IM (Inner Membrane):</strong> Simulations involving the bacteral mimicking inner membrane environment.</li> <li><strong>IM_OM (Inner Membrane and Outer Membrane Complex):</strong> Complex systems encompassing both inner and outer bacterial membrane models.</li> <li><strong>OM_D (Double Symmetric Outer Membrane):</strong> Simulations featuring a symmetric outer membrane structure.</li> <li><strong>OM (Asymmetric Outer Membrane):</strong> Simulations with an asymmetric outer membrane configuration.</li> <li><strong>PC Membrane (Phosphatidylcholine Membrane):</strong> Simulations involving membranes composed of phosphatidylcholine.</li> </ol> <p>For each membrane type, the dataset provides three replicas. The dataset includes initial and final structures (.gro files), simulation parameter files (.mdp), index files (.ndx), and topology files (.itp and .top) applicable to all systems. </p>
Source molecular simulation data for calculating energy and friction profiles and permeability coefficients through model lipid membranes
<p>Energy files from GROMACS molecular dynamics simulations with enhanced free energy sampling contain time-dependent evolution of the free energy profiles and friction profiles (and other energies and simulation properties) that were used for calculating permeability coefficients in the publication https://www.biorxiv.org/content/10.1101/2021.07.16.452599v1</p> <p>Simulation system contains a lipid POPC or DPPC bilayer with a varying amount of cholesterol (specified as mol% in the file name). Hydrophobic level of the permeating particle is specified as "level-I", "level-II" etc. When unspecified in the file name, the particle is hydrophobic level "III". Lipids D-C14-PC denote PC lipids with both tails monounsaturated of length 14 carbon atoms. DOPC is equivalent to D-C18-PC. (Detailed description in the publication)</p> <p>Adaptive Weighted Histogram (AWH) method was used to sample the free energy profile of translocating small molecule through the lipid bilayer.</p> <p>GROMACS tool `gmx awh` reads the files and provides the described profiles.</p> <p>Files were generated by GROMACS `mdrun` simulation engine version 2019.3.</p> <p> </p> <p>Coarse-grained MARTINI 3.0 model was used for modeling the biomolecular interactions.</p> <p>Scripts to perform the simulations and the files with initial configurations and simulation settings are stored in a public GitHub repository depozited on Zenodo.org: <a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p> <p> </p> <p>Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).</p> <p>Lindahl, V., Lidmar, J. & Hess, B. Accelerated weight histogram method for exploring free energy landscapes. J. Chem. Phys. 141, 044110 (2014).</p> <p>Souza, P. C. T. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18, 382–388 (2021).</p> <p>Melcr, J. Git repository with analysis scripts for MD simulations of permeability through lipid membranes. (2021) doi:<a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p>
Raw Data for the article: Atrial Septostomy for Left Ventricular Unloading During Extracorporeal Membrane Oxygenation for Cardiogenic Shock: Animal Model
<p><strong>Objectives: </strong>The aim of this study was to quantify and understand the unloading effect of percutaneous balloon atrial septostomy (BAS) in acute cardiogenic shock (CS) treated with venoarterial (VA) extracorporeal membranous oxygenation (ECMO).</p> <p><strong>Background: </strong>In CS treated with VA ECMO, increased left ventricular (LV) afterload is observed that commonly interferes with myocardial recovery or even promotes further LV deterioration. Several techniques for LV unloading exist, but the optimal strategy and the actual extent of such procedures have not been fully disclosed.</p> <p><strong>Methods: </strong>In a porcine model (n = 11; weight 56 kg [53-58 kg]), CS was induced by coronary artery balloon occlusion (57 minutes [53-64 minutes]). Then, a step-up VA ECMO protocol (40-80 mL/kg/min) was run before and after percutaneous BAS was performed. LV pressure-volume loops and multiple hemoglobin saturation data were evaluated. The Wilcoxon rank sum test was used to assess individual variable differences.</p> <p><strong>Results: </strong>Immediately after BAS while on VA ECMO support, LV work decreased significantly: pressure-volume area, end-diastolic pressure, and stroke volume to ∼78% and end-systolic pressure to ∼86%, while superior vena cava and tissue oximetry did not change. During elevating VA ECMO support (40-80 mL/kg/min) with BAS vs without BAS, we observed 1) significantly less mechanical work increase (122% vs 172%); 2) no end-diastolic volume increase (100% vs 111%); and 3) a considerable increase in end-systolic pressure (134% vs 144%).</p> <p><strong>Conclusions: </strong>In acute CS supported by VA ECMO, atrial septostomy is an effective LV unloading tool. LV pressure is a key component of LV work load, so whenever LV work reduction is a priority, arterial pressure should carefully be titrated low while maintaining organ perfusion.</p>
Hyperactive antifreeze protein from the beetle Rhagium mordax stabilizes model lipid membranes during temperature dependent phase transition
<p>Data from the study submitted in the paper Hyperactive antifreeze protein from the beetle Rhagium mordax stabilises model lipid membranes during temperature-dependent phase transition</p> <p>Data Includes;</p> <p>1. DSC results of RmAFPs interactions with liposomes showing Tm, ΔHcal and Full width at half maximum (FWHM) as well as Phase transitions thermographs by DSC on 1.5mg/ml SUV liposomes either with 60μM (or 0.75mg/ml) RmAFPs or without RmAFPs as control.</p> <p>2. fluorescence spectroscopy data, a complete compilation</p>
Membrane Fluctuation Model to Understand the Effect of the Receptor Nanoclustering on the Activation of Natural Killer Cells through Biomechanical Feedback
<p>Data for all the plots from original paper "Membrane Fluctuation Model to Understand the Effect of the Receptor Nanoclustering on the Activation of Natural Killer Cells through Biomechanical Feedback".</p>
Role of Divalent Ions in Membrane Models of Polymyxin-Sensitive and Resistant Gram-Negative Bacteria - Dataset
<p>The present dataset holds all the input files linked to the related work, concerning the effect of the removal and displacement of membrane-bound divalent ions in models of the outer membrane of Gram-negative bacteria.</p> <p>This dataset presents a comprehensive collection of input data for Molecular Dynamics (MD) simulations performed using the GROMACS simulation software concerning the effect of the removal and displacement of membrane-bound divalent ions in models of the outer membrane of Gram-negative bacteria. The dataset consists of the input files stored in five tarballs each related to a part of this work:</p> <ol> <li><strong>all_atom_global_stress.tar.gz:</strong> Set of simulations used to investigate the effect of divalent ions (calcium ions) removal on the properties of the outer membrane of Gram-negative bacteria. The simulations involve a set of four models of the outer membrane of <em>Salmonella enterica</em>, two of which are polymyxin-sensitive (P2 and P1), while the two other are polymyxin-resistant (PEtn and Ara-4N). Four replicas were made for each simulation of divalent ion removal.</li> <li><strong>all_atom_local_stress.tar.gz:</strong> Set of simulations introducing a new collective variable (CV) used to study the effect of divalent ions displacement. Simulations are made for the four model membranes described in 1. For each system, a steered MD (SMD) simulation is present, where the system is pulled along the CV to create a defect in the membrane. This SMD simulation is used to calculate a free profile along the CV using an umbrella sampling (US) procedure. One simulation set is devoted to the case of the presence of a polymyxin molecule in the vicinity of the defect.</li> <li><strong>coarse_grained_local_stress.tar.gz:</strong> Analog dataset as in 2. but ported for coarsed-grained MD simulations.</li> <li><strong>polymyxin_e.tar.gz:</strong> Topology parameters for polymyxin E for CHARMM36 and Martini 2.2.</li> <li><strong>force_fields.tar.gz:</strong> The versions of CHARMM36 and Martini 2.2 used for this work.</li> </ol> <p>For each membrane type, the dataset provides three replicas. The dataset includes initial and final structures (.gro files), simulation parameter files (.mdp), index files (.ndx), and topology files (.itp and .top) applicable to all systems. </p> <p>Refer to the manuscript and to the provided README for more information.</p> <p>In case of further questions, you can address them to: timothee.rivel@ceitec.muni.cz<br><br>Version log:</p> <p>Version 1.0: initial release of the dataset</p> <p>Version 1.1: files in <strong>all_atom_global_stress</strong> updated for the global stress run</p>
MD simulation of a model Gram-positive bilayer membrane (60% PG, 35% lysyl-PG, 5% CL)
<p><strong>Composition</strong>:</p> <p>Model bilayer composition aimed at replicating the behavior of the Staphylococcus aureus membrane (See also Mohanan et al. Chem Sci 2020, 11, 4669: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8159255/"><strong>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8159255/</strong></a>)</p> <p>Per leaflet we have 48 POPG (16:0 / 18:1; -1 net charge), 28 DPPGK (16:0 / 16:0; +1 net charge), 4 TOCL1 (18:1,18:1 / 18:1,18:1; -1 net charge), giving 60% PG, 35% lysyl-PG, 5% CL. Bilayer is symmetric. </p> <p><strong>Technical details</strong>:</p> <p>OpenMM 7.5.1</p> <p>CHARMM36m force field</p> <p>6,712 TIP3P waters</p> <p>150 mM KCl (64 K+, 16 Cl-)</p> <p>Temperature = 303.15 K</p> <p>Trajectory output every 100 ps</p> <p>Trajectory length = 500 ns ( = 5,000 frames)</p> <p>(Standard CHARMM-GUI relaxation protocol)</p>
Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
Open the record for dataset details and reuse information.
Computational dataset, scripts and models for 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide'
<p>Analysis scripts and computational models used in the manuscript 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide', by Marcin Makowski, Octávio L. Franco, Nuno C. Santos and Manuel N. Melo.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.