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2,216 results for “membrane”
Data for UV Plasmon-Enhanced Chiroptical Spectroscopy of Membrane-Binding Proteins, June 2024
<p>Extinction spectra of arrays of aluminum nanoparticles with diameters between 40 - 100 nm.</p> <p>Circular dichroism spectra of Tol-BINAP films on Al nanoparticle arrays before and after annealing of the films.</p> <p>Electromagnetic simulations of phase, electric (Eenh) field and magnetic (Henh) field enhancements as well as optical chirality density (Cenh) enhancement around flat aluminum hexagonal pyramid at specified wavelength. The simulations were performed with FDTD using Ansys Lumerical.</p>
X-Ray Diffraction data from Membrane transport protein AcrB, V612F mutant with bound minocycline, source of 9FHC structure
<p>Crystals were grown of the membrane transport protein AcrB, V612F mutant, with bound minocycline. </p> <p>X-ray diffraction data of this upload: 400 frames of 0.5° width were collected on 2007-04-30 at the X06SA beamline of Swiss Light Source at Paul-Scherrer-Institute (Switzerland).</p> <p>The data can be processed with XDS; XDS.INP is provided as part of the upload.</p> <p>The data are the basis of the PDB 9FHC structure.</p>
Interactions of the EphA2 Kinase Domain with a PIP2 containing membrane
<p>Last frames of atomistic simulations revealing the interactions of the transmembrane, juxtamembrane (JM), and kinase domains with the membrane. The structures highlight how the kinase domain is oriented relative to the membrane and how the JM region can modulate this interaction. These structures highlight the role of phosphatidylinositol phosphates (PIPs) in mediating the interaction of the kinase domain with the membrane and, conversely, how positively charged patches at the kinase surface and in the JM region induce the formation of nanoclusters of PIP molecules in the membrane.</p> <p>Analysis of the orientation of the kinase domain when bound to the PIP<sub>2</sub>-containing membrane suggests that there are two main modes of interaction. The predominant binding mode (inter1.pdb) involves the N-terminal lobe of the kinase domain. In this interaction mode, the activation loop of the kinase is accessible to phosphorylation. In the secondary mode (inter2.pdb), the interaction with the bilayer involves both the N- and C-terminal lobes of the kinase and thus the activation loop less accessible. </p>
Data set for "State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice"
<p>Data set for: Pala A, Petersen CCH (2018) State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7: e35869. DOI: https://doi.org/10.7554/eLife.35869.</p> <p>There are 12 files in this data upload:</p> <p>1. '2018_Pala_eLife.pdf' - this is a pdf version of the online publication: Pala & Petersen (2018).</p> <p>2. 'data.mat' - this is a Matlab data structure, which contains all the data for the publication.</p> <p>3. 'DataViewer.m' - this is a Matlab code for viewing the data.</p> <p>4. 'DataViewer.fig' - this is a Matlab figure file, which is the GUI layout for 'DataViewer.m'.</p> <p>5. 'PalaPetersen_Plot.m' - this is a Matlab code, which plots the figures for Pala & Petersen (2018).</p> <p>6. 'PalaPetersen_Analysis.m' - this is a Matlab code, which analyses the data for the figures of Pala & Petersen (2018).</p> <p>7. 'blankAPs.m' - this is a Matlab code, which blanks action potentials from the membrane potential trace.</p> <p>8. 'lowpassfilt.m' - this is a Matlab code, which low pass filters the LFP.</p> <p>9. 'medianFiltAPs.m' - this is a Matlab code, which median filters the membrane potential trace to remove action potentials.</p> <p>10. 'remTrialswithAPs.m' - this is a Matlab code, which removes trials with action potentials.</p> <p>11. 'retrieveSegDur.m' - this is a Matlab code, which retrieves chunks of the recording of a given length.</p> <p>12. 'suptitleAP.m' - this is a Matlab code, which puts titles above subplots.</p>
Tailoring PVDF Membranes Surface Topography and Hydrophobicity by a Sustainable Two-Steps Phase Separation Process: dataset
<p>This is the dataset related to the article published in ACS Sustainable Chemistry & Engineering “Tailoring PVDF Membranes Surface Topography and Hydrophobicity by a Sustainable Two-Steps Phase Separation Process” DOI: 10.1021/acssuschemeng.8b01407</p>
Homologous membrane protein structures (HOMEP) version 3
<p><strong>Homologous membrane protein structures (HOMEP)</strong> version 3 (created 2013)<br> An updated version of v2 <a href="https://doi.org/10.5281/zenodo.2646539">10.5281/zenodo.2646539</a> and v1: <a href="https://doi.org/10.5281/zenodo.2646534">10.5281/zenodo.2646534</a></p> <p><strong>Table 1</strong> = Alpha-helical membrane protein structures in the HOMEP3 data set (2013), listed by family<br> From Stamm M, Forrest LR, Proteins 2015 (Supplementary Table 1):<a href="http://https://www.ncbi.nlm.nih.gov/pubmed/26178143"> https://www.ncbi.nlm.nih.gov/pubmed/26178143</a> </p> <p>Contains the following columns:<br> Protein family, Protein databank identifier, Chain identifier, Name, Source organism, Resolution (Å)</p> <p> </p> <p><strong>Table 2</strong> = Beta-barrel membrane protein structures in the HOMEP3 data set (2013), listed by family<br> From Stamm M, Forrest LR, Proteins 2015 (Supplementary Table 2)</p> <p>Contains the following columns:<br> Protein family, Protein databank identifier, Chain identifier, Name, Source organism, Resolution (Å)</p> <p> </p> <p><strong>HOMEP3_pairs_alpha.txt</strong><br> List of all pairs of protein structure chains of alpha-helical proteins</p> <p> </p> <p><strong>HOMEP3_pairs_beta.txt</strong><br> List of all pairs of protein structure chains of beta-barrel proteins</p> <p> </p> <p><strong>HOMEP3_pdbs.tar.gz</strong><br> All pdb files for individual chains in both alpha-helical and beta-barrel subsets</p>
A dataset on ion-exchange membrane fouling by humic acid during electrodialysis
<p>This dataset decribes the effect of process setting on the fouling of an electrodialysis pilot installation treating a sodium chloride solution in the presence of humic acid in a series of 22 experiments. The electrical resistance over the electrodialysis stack was monitored in time while varying the crossflow velocity in the compartments, the current applied and the salt concentration in the processed. The active cycle was maintained for a maximum of 1.5h after which the polarity was reversed to remove the fouling layer. In the pilot, additional data is included such as the temperature, pH, flow rate, conductivity, pressure in the different compartments of the ED stack.</p>
Data supporting publication: Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples
<p>This repository includes the data corresponding to the figures shown in the journal article entitled Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples, published in small. </p>
A proteome-wide quantitative platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs
<p><strong>EM Quantitation:</strong></p> <p>Raw data gathered from EM images taken to determine nanodisc population size distribution.</p> <p> </p> <p><strong>NNB TGN46 analysis:</strong></p> <p>Data analysis of the Native Nanobleach experiments of TGN46 in native nanodiscs to determine population distribution of oligomeric organizations.</p> <p> </p> <p><strong>Polymer conditions:</strong></p> <p>Physiochemical characteristic and extraction conditions for all polymers in the library both commercially available and in-house.</p> <p> </p> <p><strong>Protein groups polymer screen original file:</strong></p> <p>Original output of MaxQuant data processing of polymer screen data.</p> <p> </p> <p><strong>Organelle matching:</strong></p> <p>Code used for mathcing proteins identified in the proteomics output to organelle or residence for all organellar annotations.</p> <p> </p> <p><strong>Polymer code:</strong></p> <p>Code used to process and normalize the MaxQuant output and calulate extraction efficiency across all detected proteins.</p> <p> </p> <p><strong>MAP Library Details:</strong></p> <p>Graphic and table explaining chemical details of all polymer used in the screen, both commerically available and in-house synthesized.</p> <p> </p> <p><strong>NNB TGN46:</strong></p> <p>Raw scope files for the TIRF microscopy single molecule step photobleaching experiment with TGN46.</p> <p> </p> <p><strong>Organellar Breakdown Database:</strong></p> <p>Proteins detected in the polymer screen through proteomics experiments stratified into organelle of residence.</p> <p> </p> <p><strong>Human Proteome FASTA:</strong></p> <p>The FASTA file used for proteome searching in processing the proteomics data to build the screening database.</p> <p> </p> <p><strong>Hand Curated Organellar Proteomes:</strong></p> <p>Organellar proteomes used for organellar sorting and identification of proteins detected in the screen.</p> <p> </p> <p><strong>Polymer SEC Superdex75:</strong></p> <p>Size exculsion chromatography traces for chloroSMA series of polymers. Was used to characterize length and population polydispersity.</p> <p> </p> <p><strong>Negative Stain Raw:</strong></p> <p>RAW TEM scope images of purified synaptophysin-vamp2 containing nanodiscs. Populatoin size distribution was determined.</p> <p> </p> <p><strong>FSEC Polymer CS80:</strong></p> <p>Fluoresence size exclusion chromatogram for purified synaptophysin-vamp2 containing nanodiscs to ensure population homogeneity and purity.</p> <p><strong>NMR Raw data:</strong></p> <p>NMR raw files for characterizing the in-house synthesized Chloro-SMA series and AASTY series.</p> <p> </p>
Shape, membrane morphology, and morphodynamic response of metabolically active human mitochondria revealed by scanning ion conductance microscopy
<p>This contains the hole data set as well as all analysed data for the paper published in Beilstein Journal of Nanotechnology "Shape, membrane morphology and morphodynamic response of metabolically active human mitochondria revealed by Scanning Ion Conductance Microscopy".</p> <p>Most of the images were taken with the SICM. These uncompressed tiff files can be read and processed with the Gwyddion software or other scanning probe image processing software.</p>
Plasmodesmata Act as Unconventional Membrane Contact Sites Regulating Inter-Cellular Molecular Exchange in Plants.
<p>This table contains peaks aera values from LC-MS for lipidomic quantification of PIP and PIP2. These data were used for Pérez-Sancho, Jessica and Smokvarska, Marija and Glavier, Marie and Sritharan, Sujith and Dubois, Gwennogan and Dietrich, Victor and Platre, Matthieu and Li, Ziqiang Patrick and Paterlini, Andrea and Moreau, Hortense and Fouillen, Laetitia and Grison, Magali S. and Cana-Quijada, Pepe and Moraes, Tatiana Sousa and Immel, Françoise and Wattelet, Valerie and Ducros, Mathieu and Brocard, Lysiane and Chambaud, Clément and Zabrady, Matej and Luo, Yongming and Busch, Wolfgang and Tilsner, Jens and Helariutta, Yrjö and Russinova, Jenny and Taly, Antoine and Jaillais, Yvon and Bayer, Emmanuelle, Plasmodesmata Act as Unconventional Membrane Contact Sites Regulating Inter-Cellular Molecular Exchange in Plants. </p>
Dataset and code for "Adjoint-aided homogenization for flows through heterogeneous membranes"
<p>This dataset and code support the manuscript 'Adjoint-aided homogenization for flows through heterogeneous membranes' by Kevin Wittkowski, Edouard Boujo, François Gallaire and Giuseppe A. Zampogna, under revision in the <em>Journal of Fluid Mechanics</em>, 2024. The data are Stokes flow simulations around an elliptical solid inclusion with periodic boundary conditions. Direct computations of the average flow quantities for several geometry parameters are compared with shape-sensitivity-based predictions obtained by solving a set of direct and adjoint equations around a circular solid inclusion. Please refer to the manuscript for further details.</p> <p><strong>Contents:</strong></p> <ul> <li><em>comsol_code/Mnn.mph, Mtt.mph:</em> COMSOL Multiphysics 6.0 codes used to generate the datasets;</li> <li><em>README.txt</em>: a .txt file describing the code usage;</li> <li><em>Mnn.txt,Mtt.txt</em>: dataset of the direct evaluations of these two average microscopic quantities, input of the .m files;</li> <li><em>S1_Mnn.txt, S2_Mnn.txt, S1_Mtt.txt, S2_Mtt.txt</em>: dataset containing the first and second order shape sensitivities computed on the reference circular geometry, input of the .m files;</li> <li><em>Mnn.m,Mtt.m:</em> MatLab code used to compare the direct evaluations and adjoint predictions of the microscopic quantities;</li> <li><em>ellipse.m</em>: Matlab function to generate the elliptical deformation;</li> <li><em>.fig</em> files: Matlab figures, output of the .m files. They are used to compare the direct evaluation with the prediction based on the adjoint computations.</li> </ul>
Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death
<p>This dataset contains the raw data that were used for the publication entitled, "Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death" published in Nanomaterials on 4 August 2021.</p> <p>Abstract:</p> <p>DNA nanotechnology offers to build nanoscale structures with defined chemistries to precisely position biomolecules or drugs for selective cell targeting and drug delivery. Owing to the negatively charged nature of DNA, for delivery purposes DNA is frequently conjugated with hydrophobic moieties, positively charged polymers/peptides, cell surface receptor recognizing molecules or antibodies. Here, we designed and assembled cholesterol-modified DNA nanotubes to interact with cancer cells and conjugated them with cytochrome c to induce cancer cell apoptosis. By flow cytometry and confocal microscopy, we observed that DNA nanotubes efficiently bound to the plasma membrane as a function of the number of conjugated cholesterol moieties. The complex was taken up by the cells and localized to the endosomal compartment. Cholesterol-modified DNA nanotubes, but not unmodified ones, induced increased membrane permeability, caspase activation and cell death. Irreversible inhibition of caspase activity, with Z-VAD-FMK, however, only partially prevented cell death. Cytochrome c conjugated DNA nanotubes were also efficiently taken up but did not increased the rate of cell death. These results demonstrate that cholesterol-modified DNA nanotubes induce cancer cell death associated with increased cell membrane permeability and only partially dependent on caspase activity, consistent with a combined form of apoptotic and necrotic cell death. DNA nanotubes may be further developed as primary cytotoxic agents, or drug delivery vehicles, through cholesterol mediated cellular membrane interactions and uptake.</p>
Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"
<p>Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2021_Sippy_FUNCTION.pdf</strong>" is the Open Access pdf of the online publication in FUNCTION.</p> <p>2. The file named "<strong>Sippy_data_code.zip</strong>" (~5 GB) is a zipped version of a folder ‘<em>Sippy_data_code</em>’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘<em>Sippy_data_code</em>’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘<em>Data</em>’ contains the data structure ‘<em>Data.mat</em>’ to be analyzed, as well as a Matlab file called ‘<em>p_value_colormap.mat</em>’ used to plot the p value color bars in some figures.</p> <p>The subfolder ‘<em>Functions</em>’ contains functions called by the main codes.</p> <p>The subfolder ‘<em>Codes</em>’ contains the following codes:</p> <p><em>‘AnalyzeDataStructure.m’: </em>computes the results and saves them as a new data structure called ‘<em>Analyzed_Data</em>’, in the subfolder ‘<em>Results</em>’.</p> <p><em>‘Figure_1.m’: </em>computes and plots the results for the panels D, E and F of Figure 1.</p> <p><em>‘Figure_2.m’: </em>computes and plots the results for the panels D-G and I-K of Figure 2.</p> <p><em>‘Figure_3.m’: </em>computes and plots the results for the panels A-F of Figure 3.</p> <p><em>‘SuppFigure_2.m’: </em>computes and plots the results for the panels B, D and F of Supplementary Figure 2.</p> <p><em>‘SuppFigure_3.m’: </em>computes and plots the results for the panels A-D of Supplementary Figure 3.</p> <p><em>‘SuppFigure_4.m': </em>computes and plots the results for the panels A-C of Supplementary Figure 4.</p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>‘Mouse_Name’</em>: name of the mouse.</p> <p><em>‘Mouse_RecordingDate’</em>: date of recording (YMD).</p> <p><em>‘Mouse_DateOfBirth’</em>: date of birth of the mouse (YMD).</p> <p><em>‘Mouse_Sex’</em>: sex of the mouse (F or M).</p> <p><em>‘Mouse_Genotype’</em>: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice.</p> <p><em>‘Mouse_Level’</em>: Training level (NAÏVE or EXPERT).</p> <p><em>‘Cell_Counter’</em>: cell recorded in a given mouse.</p> <p><em>‘Cell_Type’</em>: type of the recorded cell (dSPN, iSPN or TAN).</p> <p><em>‘Cell_TargetedBrainArea’</em>: Brain area targeted (DLS).</p> <p><em>‘Cell_Recovered’</em>: Indicate cells that have been labelled and anatomically recovered (TRUE).</p> <p><em>‘Cell_Coordinates’</em>: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal)</p> <p><em>‘Cell_Fluorescence’</em>: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN </em>(cf <em>Cell_Type</em>).</p> <p><em>‘Sweep_Counter’</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s).</p> <p><em>‘Sweep_Type’</em>: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task).</p> <p><em>‘Sweep_MembranePotential’</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>‘Sweep_CurrentInjected’</em>: current injected into the cell (pA).</p> <p><em>‘Sweep_PiezoLick’</em>: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps.</p> <p><em>‘Sweep_Trial’</em>: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps.</p> <p><em>‘Sweep_WhiskerStim’</em>: voltage command triggering the onset of each whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_Valve’</em>: voltage command triggering the opening of the valve delivering the reward in Hit trials.</p> <p><em>‘Sweep_SamplingRate’</em>: sampling rate (sample.s<sup>-1</sup>) of the recorded signals for each sweep.</p> <p><em>‘Sweep_TimeStamp’</em>: time at the beginning of the recorded sweep (H/min/s).</p> <p><em>‘Sweep_Reward’</em>: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_APThresh’</em>: Threshold (V) used to detect action potentials (AP) during current injection.</p> <p> </p>
Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods"
<p>Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods". </p> <p>This supplement includes the following files:</p> <p> </p> <p>1. Structures of the most stable Protein-Aptamer complexes predicted from HADDOCK</p> <p>Haddock_Cluster1.pdb <br> Haddock_Cluster2.pdb <br> Haddock_Cluster3.pdb </p> <p>2. Structure of the most stable conformation aligned with Protein-transferring complex PDB</p> <p>cluster1_aligned.pdb <br> transferrin_aligned.pdb </p> <p>3. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer</p> <p>aptamer_new_rep01_Decomp.dat <br> aptamer_new_rep02_Decomp.dat <br> aptamer_new_rep03_Decomp.dat </p> <p>4. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer-Conjugate<br> conjugate_new_rep01_Decomp.dat <br> conjugate_new_rep02_Decomp.dat <br> conjugate_new_rep03_Decomp.dat <br> </p> <p><br> <br> </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>
Pure POPC Membrane Simulation Using Charmm-Drude Force Field with OpenMM
<p>400 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 72 POPC lipids and 2809 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>This dataset does not contain the water molecules.</p> <p><strong>Please note that</strong> the trajectories might need to be realigned.</p>
Edge mode engineering for optimal ultracoherent SiN membrane designs
<p>Raw dataset for all the figures of the article entitled</p> <p>'Edge mode engineering for optimal ultracoherent SiN membrane designs',</p> <p>and corresponding scripts for data analysis.</p>
Input Files for Peptide Translocation Across Phospholipid Membranes Using Various Collective Variables and Martini Coarse-Grained Simulations
<p>Input files for publication: Ivo Kabelka, Radim Brožek, and Robert Vácha: Selecting Collective Variables and Free Energy Methods for Peptide Translocation Across Membranes, Journal of Chemical Information and Modeling, submitted</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.