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77 results for “transport proteins”
Data in: Aging power spectrum of membrane protein transport and other subordinated random walks
<p>Datasets generated in the report "Aging power spectrum of membrane protein transport and other subordinated random walks". Included data are:</p> <p><strong>Numerical simulations </strong><br> RWdata1.mat: 10,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.3 and <span class="math-tex">\(\alpha\)</span>=0.4.<br> RWdata3.mat: 10,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.7 and <span class="math-tex">\(\alpha\)</span>=0.4.<br> RWdata8.mat: 5,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.75 and <span class="math-tex">\(\alpha\)</span>=0.8.<br> RWdataCTRW.mat: 10,000 realizations, continuous time random walk (CTRW), <span class="math-tex">\(\alpha\)</span>=0.7.</p> <p><strong>Spectra of simulations</strong><br> PSDdata1.mat: Power spectral density (PSD) of a subordinated random walk with Hurst exponent, <em>H</em>=0.3 and <span class="math-tex">\(\alpha\)</span>=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.<br> PSDdata3.mat: PSD of a subordinated random walk with Hurst exponent, <em>H</em>=0.7 and <span class="math-tex">\(\alpha\)</span>=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.<br> PSDdata8.mat: PSD of a subordinated random walk with Hurst exponent, <em>H</em>=0.75 and <span class="math-tex">\(\alpha\)</span>=0.8. Four different realization times are used to compute the PDS: 2^15, 2^16, 2^17, and 2^18.<br> PSDs_CTRW.mat: PSD of a continuous-time random walk (CTRW), <span class="math-tex">\(\alpha\)</span>=0.7. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.</p> <p><strong>Experimental data of Nav1.6 channels in the soma of hippocampal neurons</strong><br> NavMSDtimes.csv: ensemble-averaged (EA) MSD and time-averaged (TA) MSD. The TA-MSD is measured for three observation times, 64, 128, and 256 frames (3.2, 6.4, and 12.8 s).<br> NavPSD.csv: Power spectral density (PSD) measured for three observation times, 64, 128, and 256 frames.</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>
Host-derived viral transporter protein for nitrogen uptake in infected marine phytoplankton
<p>Dataset for the article "Host-derived viral transporter protein for nitrogen uptake in infected marine phytoplankton", Monier et al.</p> <p>Data for all phylogenetic tree reconstructions (raw and masked protein sequence alignments in fasta format, tree file in newick format) and placement file (jplace format) of two environmental sequences are available.</p> <p>Data for all assay experiments are available: ammonium and urea assays, Omnilog phenotype screening (Nitrogen substrates).</p>
Water will find its way: transport through narrow tunnels in hydrolases (Epx protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (Epx protein)</strong></p> <p><br>The input files and results used for the paper “<em>Water will find its way: transport through narrow tunnels in hydrolases</em>” are separated in the different folders depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the minimum sphere event is present. Also the txt files with the results from the H-bond and contact analyses are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>Epx.dat</strong> Parsed database of transport events for Epx system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/Epx_tt.ini -o Epx.dat</em></li> </ol>
Water will find its way: transport through narrow tunnels in hydrolases (hEpx protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (hEpx protein)</strong></p> <p>The input files and results used for the paper "<em>Water will find its way: transport through narrow tunnels in hydrolases</em>" are separated in the different files depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the "<em>Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results for hEpx and E470G systems together, since we employed the comparative approach to analyze the results with TransportTools (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the *minimum sphere event* is present. Also the txt files with the results from the H-bond analysis are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>hEpx_E470G.dat</strong> Parsed database of transport events for hEpx_E470G system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/hEpx_E470G_tt.ini -o hEpx_E470G.dat</em></li> </ol> <p> </p>
Water will find its way: transport through narrow tunnels in hydrolases (Lip protein)
<p><strong>Water will find its way: transport through narrow tunnels in hydrolases (Lip protein)</strong></p> <p><br>The input files and results used for the paper “<em>Water will find its way: transport through narrow tunnels in hydrolases</em>” are separated in the different folders depending the stage they belong to.</p> <ol> <li><strong>01_Simulations.tar.gz:</strong> All the files used to get the data employing Molecular Dynamics simulations.</li> <li><strong>02_Caver.tar.gz:</strong> Caver config used together with the Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.</li> <li><strong>03_Aquaduct.tar.gz:</strong> Aquaduct results for all the MD trajectories.</li> <li><strong>04_TransportTools.tar.gz:</strong> TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).</li> <li><strong>05_WaterAnalysis.tar.gz:</strong> The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the minimum sphere event is present. Also the txt files with the results from the H-bond analysis are here.</li> </ol> <p><strong>Files</strong></p> <ol> <li><strong>01_build_database.py</strong> Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</li> <li><strong>Lip.dat</strong> Parsed database of transport events for Lip system. Command used: <em>python3 01_build_database.py -c 04_TransportTools/Lip_tt.ini -o Lip.dat</em></li> </ol>
Water will find its way: transport through narrow tunnels in hydrolases (Hal protein with different MD simulation settings)
<h1>Hal_2fs.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_2fs protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_2fs.dat</em>: Parsed database of transport events for Hal system. Command used: <br><em>python3 01_build_database.py -c 04_TransportTools/Hal_2fs.ini -o Hal_2fs.dat</em></p> <p> </p> <h1>Hal_300K.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_300K protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_300K.dat:</em> Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_300K.ini -o Hal_300K.dat</em></p> <p> </p> <h1>Hal_TIP3P.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (TIP3P protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</p> <p> 2. <em>Hal_TIP3P.dat</em>: Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_TIP3P.ini -o Hal_TIP3P.dat</em></p>
Supporting data for Bryłka et al., 2023 Gene duplication, shifting selection, and functional diversification of silicon transporter proteins in marine and freshwater diatoms.
<p>These are supporting files used in the analysis in Bryłka et al., 2023 "<em>Gene duplication, shifting selection, and functional diversification of silicon transporter proteins in marine and freshwater diatoms". </em> All data newly generated from the Alverson lab.</p> <p> </p>
Water will find its way: transport through narrow tunnels in hydrolases (water-protein interaction analysis)
<p>Repository including information on the analyses of water-protein interactions by MM/GBSA.</p> <p><strong>hal_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Hal.<br><strong>epx_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Epx.<br><strong>lip_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results<strong> </strong>for Lip<strong>.</strong></p> <p>In each archive structure is:<br> {supercluster_id}: - supercluster directory<br> {event_nr}_{rounded_radius}: - transport event directory<br> renum.pdb -pdb file containing protein, water of interest and 8 Angstrom radius sphere of surrounding waters<br> water.txt - file denoting water ID for water of interest in the renum.pdb file<br> binding_[implicit|explicit].dat - file containing MMGBSA results for full implicit and partial explicit system<br> decomposition_[implicit|explicit].dat - file containing MMGBSA decomposition results for full implicit and partial explicit system<br> info_{supercluster_id}.csv - file containing descriptions of transport events in supercluster (columns: event_name, radius, frame, simulation, water_id)</p> <p><strong>results.tar.gz </strong>- contains csv files generated by <em>script collect_data_minimal.py</em> for each protein for full implicit and partial explicit solvent. Columns in those csv file are: event_name, supercluster, radius, frame, simulation, water_id, delta_total, delta_van_der_waals, delta_electrostatic, delta_G_solv</p> <p><strong>scripts.tar.gz</strong> - contains following scripts<br> read_transport_events.py<br> prep_inputs.py<br> run_MMGBSA_single.sh<br> collect_data_minimal.py<br> plot_bin.py</p> <p>To recreate MMGBSA calculations with provided inputs one should:<br>$ python read_transport_events.py [Hal.dat|Epx.dat|Lip.dat] > selection.txt<br>Prepare transport events information in a compatible format. Binary transport events databases are available in the repository.</p> <p>$ python prep_inputs.py selection.txt - {absolute output path} {supercluster_id} 1 <br>Prepare parm and coordinate file for each supercluster id included in computation. Last argument 1 point script to resume preparations from renum.pdb files.</p> <p>$ bash run_MMGBSA_single.sh {residue_count}<br>Runs MMGBSA for both partial explicit and fully implicit systems. Needs to be run for each event in each supercluster.<br>residue_count should be set to 293 for hal, 319 for epx, and 534 for lip.</p> <p>$ python collect_data_minimal.py [hal|epx|lip]/{supercluster_id} [hal|epx|lip]/info_{supercluster_id}.csv [0|1] >> output.csv<br>Gathers data from MMGBSA calculations for a single supercluster into csv file. <br>Last argument should be 0 for fully implicit system, 1 for partial explicit system.</p> <p>$ python plot_bin.py {hal_implicit_csv} {hal_explicit_csv} {epx_implicit_csv} {epx_explicit_csv} {lip_implicit_csv} {lip_explicit_csv}<br>Results visualization. Produces "combined.png".</p>
A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2
<p>Data produced and analyzed in the manuscript "A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2" by Pasquadibisceglie et al.</p> <p><br> If you include these data in your manuscript, please cite: Pasquadibisceglie A, Leccese A and Polticelli F (2022) A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2. <em>Front. Chem.</em> 10:1004815. doi: 10.3389/fchem.2022.1004815</p>
A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction - buil and Analysis
<p><strong>Title publication: </strong><em>A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction</em></p> <p><strong><strong>Year publication:</strong></strong><strong> </strong>2018</p> <p><strong>DOI publication: </strong>10.1021/acscentsci.7b00582</p> <p><strong>Description:</strong> All scripts, jupyter notebooks, and data files used to building and analyzing systems in the presented in the paper. Also related experimental data.</p>
Data from: Detailed characterization of the UMAMITs proteins provides insight into their evolution, amino acid transport properties, and role in the plant
<p>Amino acid transporters play a critical role in distributing amino acids within the cell compartments and between the plant organs. Despite this importance, relatively few amino acid transporter genes have been characterized and their role elucidated with certainty. Two main families of proteins encode amino acid transporters in plants: the Amino Acid-Polyamine-Organocation superfamily, containing mostly importers, and the Usually Multiple Acids Move In and out Transporter family, apparently encoding exporters, totaling 63 and 44 genes in Arabidopsis, respectively. Knowledge on UMAMITs is scarce, based on six Arabidopsis genes and a handful of genes from other species. To get insight into the role of the members of this family and provide data to be used for future characterization, we studied the evolution of the UMAMITs in plants, and determined the functional properties, the structure, and the localization of the 47 Arabidopsis UMAMITs. Our analysis showed that the AtUMAMITs are essentially localized at the tonoplast or the plasma membrane, and that most of them are able to export amino acids from the cytosol, confirming a role in intra- and inter-cellular amino acid transport. As an example, this set of data was used to hypothesize the role of a few AtUMAMITs in the plant and the cell.</p>
Data for "Ceramide-1-phosphate transfer protein enhances lipid transport by disrupting hydrophobic lipid–membrane contacts"
<p>Data for "Ceramide-1-phosphate transfer protein enhances lipid transport by disrupting hydrophobic lipid–membrane contacts" by Julia R Rogers and Phillip L Geissler (<a href="https://doi.org/10.1371/journal.pcbi.1010992">Rogers, J. R.; Geissler, P. L. <em>PLoS Comput. Biol.</em> <strong>2023</strong>, <em>19</em>, e1010992</a>; bioRxiv DOI: https://doi.org/10.1101/2022.09.10.507427). All input coordinates, topologies, and parameter files in addition to equilibrium simulation trajectories and analysis results are provided.</p>
Characterization and cellular localization of a SFT-2/Got-1- like transporter protein of Leishmania infantum
<p><strong>Abstract</strong>: Background: Visceral leishmaniasis affects approximately 12 million people worldwide. Current existing drugs cause several side effects and resistance, the identification of new therapeutic targets for the development of new drugs is necessary. The vesicular trafficking in the parasite flagellar pocket is essential for parasite survival and proliferation, but little is known about the proteins involved in the endocytic/secretory pathways. In this work, we are describing the characterization of a transporter cargo protein SFT-2/Got-1 like in <em>Leishmania infantum</em>. <strong>Material and Methods:</strong> Using bioinformatics tools, the putative Got-1 protein A4HSK3 was characterized structurally and phylogenetically. Molecular biology was used to clone and express the protein and confocal fluorescence to determine its subcellular localization. <strong>Results:</strong> The Got-1 like protein presents four alpha helix TM conserved domains (aa38-aa147) characteristics of the Got-1/SFT-2 family proteins and an important PEN-2 conserved motif LGDYXXF located in the extracellular loop. Confocal microscopy suggests that Got-1/SFT-2 like is localized in the flagellar pocket, and is partially co-localized with the <em>L. infantum</em> presenilin-like protein. Phylogenetically, the <em>Leishmania sp</em> Got-1 like is contained in a subgroup of proteins clearly detached from the other Trypanosomatidae, such as <em>T. cruzi</em> and <em>T. brucei</em>. <strong>Conclusions:</strong> Our data show the presence of an important transported proteins, and its colocalizatoin with the presenilin aspartyl protease in the flagelar pocket of the <em>L. infantum</em> open new perspectives to identify novel targets, which can be an alternative therapeutic strategy against leishmaniasis</p>
Reduced gene expression and missense mutations in the transporter protein SLC45A2 in a hypopigmented Egyptian rousette fruit bat (<em>Rousettus aegyptiacus</em>)
Open the record for dataset details and reuse information.
Data from: Detailed characterization of the UMAMITs proteins provides insight into their evolution, amino acid transport properties, and role in the plant
Open the record for dataset details and reuse information.
Data from: Selective clearance of the inner nuclear membrane protein emerin by vesicular transport during ER stress
<p>The inner nuclear membrane (INM) is a subdomain of the endoplasmic reticulum (ER) that is gated by the nuclear pore complex. It is unknown whether proteins of the INM and ER are degraded through shared or distinct pathways in mammalian cells. We applied dynamic proteomics to profile protein half-lives and report that INM and ER residents turn over at similar rates, indicating that the INM's unique topology is not a barrier to turnover. Using a microscopy approach, we observed that the proteasome can degrade INM proteins in situ. However, we also uncovered evidence for selective, vesicular transport-mediated turnover of a single INM protein, emerin, that is potentiated by ER stress. Emerin is rapidly cleared from the INM by a mechanism that requires emerin's LEM domain to mediate vesicular trafficking to lysosomes. This work demonstrates that the INM can be dynamically remodeled in response to environmental inputs.</p>
Genetic mutations and decreased protein expression of ABCG2 urate transporters are associated with susceptibility to gout, disease severity and renal-overload hyperuricemia
<p><strong>We are releasing the data and code of our study of clinically defined gouty patients. </strong></p>
In silico dataset of features for multidrug resistant efflux transporter protein familes
<p>This dataset constitutes in silico features as amino acids fractions, dipeptide count, and other basic features information for efflux transporter family proteins.</p>
The conformational plasticity of structurally unrelated lipid transport proteins correlates with their mode of action
<p>Supporting DATA files for "Conformational plasticity of structurally unrelated lipid transport proteins correlates with their mode of action".</p> <ol> <li><strong>zenodo_simulation_starting_files_and_result_pdbs.zip</strong> contains input files for atomistic and coarse-grained MD simulations, membrane binding interface results from coarse-grained simulations, and apo-like and holo-like conformations of lipid transfer domains arising from clustering of atomistic simulations</li> <li><strong>zenodo_data_and_codes.zip </strong>contains the raw data and/or scripts used for the analysis of the figures of the paper as well as a README file explaining the contents. The figure caption in the manuscript indicates the folder name that contains data relevant to that particular figure. <p>The trajectories have not been uploaded here, in case you are interested, please ask the corresponding author (stefano.vanni@unifr.ch).</p> </li> </ol> <p> </p>
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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.