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2,216 results for “membrane”
Spatial and Temporal Clustering of Anti-Glomerular Basement Membrane Disease
<p><strong>Background and objectives</strong> An environmental trigger has been proposed as an inciting factor in the development of anti-GBM disease. This multicenter, observational study sought to define the national incidence of anti-GBM disease during an 11-year period (2003–2014) in Ireland, investigate clustering of cases in time and space, and assess the effect of spatial variability in incidence on outcome.</p> <p><strong>Design, setting, participants, & measurements</strong> We ascertained cases by screening immunology laboratories for instances of positivity for anti-GBM antibody and the national renal histopathology registry for biopsy-proven cases. The population at risk was defined from national census data. We used a variable-window scan statistic to detect temporal clustering. A Bayesian spatial model was used to calculate standardized incidence ratios (SIRs) for each of the 26 counties.</p> <p><strong>Results</strong> Seventy-nine cases were included. National incidence was 1.64 (95% confidence interval [95% CI], 0.82 to 3.35) per million population per year. A temporal cluster (<em>n</em>=10) was identified during a 3-month period; six cases were resident in four rural counties in the southeast. Spatial analysis revealed wide regional variation in SIRs and a cluster (<em>n</em>=7) in the northwest (SIR, 1.71; 95% CI, 1.02 to 3.06). There were 29 deaths and 57 cases of ESRD during a mean follow-up of 2.9 years. Greater distance from diagnosis site to treating center, stratified by median distance traveled, did not significantly affect patient (hazard ratio, 1.80; 95% CI, 0.87 to 3.77) or renal (hazard ratio, 0.76; 95% CI, 0.40 to 1.13) survival.</p> <p><strong>Conclusions</strong> To our knowledge, this is the first study to report national incidence rates of anti-GBM disease and formally investigate patterns of incidence. Clustering of cases in time and space supports the hypothesis of an environmental trigger for disease onset. The substantial variability in regional incidence highlights the need for comprehensive country-wide studies to improve our understanding of the etiology of anti-GBM disease.</p>
Predicting Nanoparticle Uptake by Biological Membranes: Theory and Simulation - data
<p>Simulation data described in paper entitled "Predicting Nanoparticle Uptake by Biological Membranes: Theory and Simulation"</p>
Long-Term Stable Metal Organic Framework (MOF) Based Mixed Matrix Membranes for Ultrafiltration
<p><strong>Preprint available here:</strong> Al-shaeli, Muayad; Smith, Stefan J. D.; Jiang, Shanxue; Wang, Huanting; Zhang, Kaisong; Ladewig, Bradley P. (2020): Long-Term Stable Metal Organic Framework (MOF) Based Mixed Matrix Membranes for Ultrafiltration. ChemRxiv. Preprint. <a href="https://doi.org/10.26434/chemrxiv.13399367.v1">https://doi.org/10.26434/chemrxiv.13399367.v1</a></p> <p>Published manuscript: Muayad Al-Shaeli, Stefan J.D. Smith, Shanxue Jiang, Huanting Wang, Kaisong Zhang, Bradley P. Ladewig,<br> Long-term stable metal organic framework (MOF) based mixed matrix membranes for ultrafiltration,<br> Journal of Membrane Science, 2021, 119339, <a href="https://doi.org/10.1016/j.memsci.2021.119339">https://doi.org/10.1016/j.memsci.2021.119339</a></p> <p><strong>Abstract</strong></p> <p>In this study, novel mixed matrix membranes (MMMs) were synthesized by adding metal organic frameworks (MOFs) (UiO-66 and UiO-66-NH<sub>2</sub>) to pristine and sulfonated polyethersulfone (PES). The differing synthetic method resulting in MMM where additives were grafted to the matrix polymer, or formed a natural interface, allowing the impact of these MMM features to be investigated. The composite membranes were characterised by FTIR, PXRD, water contact angle, porosity, pore size, etc. Membrane performance was investigated by water permeation flux, flux recovery ratio, fouling resistance and anti-fouling performance. The stability test was also conducted for all the prepared mixed matrix membranes. A higher reduction in the water contact angle was observed after adding both MOFs to the PES and sulfonated PES membranes compared to pristine PES membranes. An enhancement in membrane performance was observed by embedding the MOFs into PES membrane matrix, with flux increased remarkably (565 LMH for PES+UiO-66-NH<sub>2</sub> at 5% loading and 487.1 LMH for SPES-UiO-66(10% binding) while the BSA rejection was still kept at a high level. By adding the MOFs into PES matrix, the flux recovery ratio was increased greatly (more than 99% for most mixed matrix membranes). The mixed matrix membranes showed higher resistance to protein adsorption compared to pristine PES membranes. After immersing the membranes in water for 3 months, 6 months and 12 months, both MOFs were stable and retained their structure. This study indicates that UiO-66 and UiO-66-NH<sub>2</sub> are great candidates for designing long-term stable mixed matrix membranes (MMMs) for applications in water and wastewater treatment.</p>
Pure POPC membrane simulations using Amber Lipid 14 Force Field
<p>Pure POPC membrane simulations using the Amber Lipid 14 force field.</p> <pre>@article{dickson2014lipid14, title={Lipid14: the amber lipid force field}, author={Dickson, Callum J and Madej, Benjamin D and Skjevik, {\AA}ge A and Betz, Robin M and Teigen, Knut and Gould, Ian R and Walker, Ross C}, journal={Journal of chemical theory and computation}, volume={10}, number={2}, pages={865--879}, year={2014}, publisher={ACS Publications} }</pre> <p>The trajectories are centered such that the center of mass of the lipid tails are at the origin. <strong>Please check the imaging again to make sure that there are no problems. </strong></p> <p><strong>The trajectories do not contain water molecules.</strong> </p> <p>Simulation Details:</p> <p>Lipids : 72 POPC lipids, 36 per leaflet</p> <p>Water: 9560 TIP3P water molecules (<strong>water coordinates are not saved</strong>)</p> <p>Temperature: 303 K</p> <p>Pressure: 1 bar</p> <p>Thermostat: Langevin</p> <p>Barostat: Berendsen</p> <p>Pressure coupling: Semi-isotropic</p> <p>Trajectory Length: 100 ns (after 100 ns pre-equilibration)</p> <p>Saving frequency: 100 ps</p> <p>Further details are available at the 04_Run.in file</p> <p>All trajectories started from the same structure but equilibriated for 100 ns independently (using 03_Hold.in)</p>
The utilization of micro-mesoporous carbon-based filler in the P84 hollow fiber membrane for gas separation
<p>This research carried out a unique micro-mesoporous carbon particle incorporation into P84 co-polyimide membrane for improved gas separation performance. The carbon filler was prepared using a hard template method from zeolite and called as zeolite templated carbon (ZTC). This research aims to study the loading amount of ZTC into P84 co-polyimide toward the gas separation performance. The ZTC was prepared using simple impregnation method of sucrose into hard template of zeolite Y. The SEM result showing a dispersed ZTC particle on the membrane surface and cross-section. The pore size distribution of ZTC revealed that the particle consists of two characteristics of micro and mesoporous region. It was noted that with only 0.5 wt% of ZTC addition, the permeability was boosted up from 4.68 to 7.06 and from 8.95 to 13.15 Barrer, for CO2 and H2 respectively as compared to the neat membrane. On the other hand, the optimum loading was at 1 wt%, where the membrane received thermal stability boost of 10% along with the 62.4 and 35% of selectivity boost of CO2/CH4 and H2/CH4, respectively. It was noticed that the position of the filler on the membrane surface was significantly affecting the gas transport mechanism of the membrane. Overall, te results demonstrated that the addition of ZTC with proper filler position has potential candidate to be applicable in the gas separation involving CO2 and H2.</p>
Dataset of Paper: "Evaluation of membranes performance for microplastic removal in a simple and low-cost filtration system"
<p>Dataset of Paper: "Evaluation of membranes performance for microplastic removal in a simple and low-cost filtration system":</p> <ul> <li>Zeta potential curves as a function of pH</li> <li>Results of transmembrane pressure (TMP) and flow rate of the filtration experiments. Average transmembrane pressure drop (ΔTMP) and flux (J) after 10 min of operation.</li> <li>Total number of unremoved microplastic particles per litre (NTMP), average size (DMP), and mass removal efficiency (MRE%) of each membrane when filtering water with 100 mg/L of PA and PS</li> </ul>
Dataset for "Studying the effect of membrane permeability with a GPU-based Bloch-Torrey simulator"
<p>This dataset contains the results of the GPU-based simulations of diffusion in cardiac tissue. The data was used for the work presented at the ISMRM 27th Annual Meeting in 2019.</p>
Data from: Physical, chemical, and functional properties of neuronal membranes vary between species of Antarctic notothenioids differing in thermal tolerance
Disruption of neuronal function is likely to influence limits to thermal tolerance. We hypothesized that with acute warming the structure and function of neuronal membranes in the Antarctic notothenioid fish Chaenocephalus aceratus are more vulnerable to perturbation than membranes in the more thermotolerant notothenioid Notothenia coriiceps. Fluidity was quantified in synaptic membranes, mitochondrial membranes, and myelin from brains of both species of Antarctic fishes. Polar lipid compositions and cholesterol contents were analyzed in myelin; cholesterol was measured in synaptic membranes. Thermal profiles were determined for activities of two membrane-associated proteins, acetylcholinesterase (AChE) and Na+/K+-ATPase (NKA), from brains of animals maintained at ambient temperature or exposed to their critical thermal maxima (CTMAX). Synaptic membranes of C. aceratus were consistently more fluid than those of N. coriiceps (P < 0.0001). Although the fluidities of both myelin and mitochondrial membranes were similar among species, sensitivity of myelin fluidity to in vitro warming was greater in N. coriiceps than in C. aceratus (P < 0.001), which can be explained by lower cholesterol contents in myelin of N. coriiceps (P < 0.05). Activities of both enzymes, AChE and NKA, declined upon CTMAX exposure in C. aceratus, but not in N. coriiceps. We suggest that hyper-fluidization of synaptic membranes with warming in C. aceratus may explain the greater stenothermy in this species, and that thermal limits in notothenioids are more likely to be influenced by perturbations in synaptic membranes than in other membranes of the nervous system.
The oxytocin-prostaglandins pathways in the horse (Equus caballus) placenta during pregnancy, physiological parturition, and parturition with fetal membrane retention
<p>Despite their importance in mammalian reproduction, substances in the oxytocin-prostaglandins pathways have not been investigated in the horse placenta during most of pregnancy and parturition. Therefore, we quantified placental content of oxytocin (OXT), oxytocin receptor (OXTR), and prostaglandin E2 and F2 alpha during days 90–240 of pregnancy (PREG), physiological parturition (PHYS), and parturition with fetal membrane retention (FMR) in heavy draft horses (PREG = 13, PHYS = 11, FMR = 10). We also quantified <i>OXTR</i> and<i> </i>prostaglandin endoperoxide synthase-2 (<i>PTGS2</i>) mRNA expression and determined the immunolocalization of OXT, OXTR, and PTGS2. For relative quantification of OXT and OXTR, we used western blotting with densitometry. To quantify the prostaglandins, we used enzyme immunoassays. For relative quantification of <i>OXTR</i> and <i>PTGS2</i>, we used RT-qPCR. For immunolocalization of OXT, OXTR, and PTGS2, we used immunohistochemistry. We found that OXT was present in cells of the allantochorion and endometrium in all groups. <i>PTGS2</i> expression in the allantochorion was 14.7-fold lower in FMR than in PHYS (<i>p</i> = 0.007). These results suggest that OXT is synthesized in the horse placenta. As PTGS2 synthesis is induced by inflammation, they also suggest that FMR in heavy draft horses may be associated with dysregulation of inflammatory processes.</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>
The Physics of Stratum Corneum Lipid Membranes
<p>Topologies, force-fields and final configurations for the SC lipid systems for which results have been included in the paper "The physics of stratum corneum lipid membranes", by Chinmay Das and Peter D. Olmsted, to be published in Philosophical Transactions A, 2016. (Preprint available at http://arxiv.org/abs/1510.08939 )</p> <p> </p>
POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for openMM simulation engine v7
<p>POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for for openMM simulation engine v7</p> <p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration).</p> <p>Note that the provided trajectories are in Gromacs XTC format, whereas NAMD DCD format was generated by openMM. This required trajectory conversion using Gromacs package (v5.1.2) with binary topology file from https://doi.org/10.5281/zenodo.153944 </p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2</p> <p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field from charmm-gui, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 84 POPC and 36 Cholesterol molecules, 4800 tip3p waters, 100ns trajectory (preceded with equilibration).</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field from charmm-gui, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 100ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field through the use of Gromacs input files, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Specifically, Gromacs file format provided by [1] was specifically used for this simulation.</p> <p>Conditions: T=303, 84 POPC and 36 Cholesterol molecules, 4800 tip3p waters, 100ns trajectory (preceded with equilibration).</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field, simulation files and 100 ns trajectory for GROMACS simulation engine v5
<p>All runs were performed with GROMACS simulation engine v5 and CHARMM36 additive force field parameters obtained from MacKerell lab website (http://mackerell.umaryland.edu/charmm_ff.shtml, also available at</p> <p>https://doi.org/10.5281/zenodo.209080). Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 100ns trajectory (preceded with equilibration). </p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p>
PART-2: Dataset for journal: "On the fluid-structure interaction of flexible membrane wings for MAVs in and out of ground-effect"
<p>Complementary file for the attached files<br> Written 24-04-2017<br> by Robert Bleischwitz (modellwerft@freenet.de)</p> <p>General Comments</p> <p>0.) This specific upload contains PART-2 of the full dataset</p> <p>1.) The attached data relates to experimental windtunnel measurements on passive membrane wings for MAVs. The data was aquired between 2012-2016 at the University of Southampton, involving Robert Bleischwitz as PhD student, who was supervised by Roeland de Kat and Bharathram Ganapathisubramani.</p> <p>2.) The attached data is given time-resolved and time-synchronised at 800Hz over a imaging-period of 5000 images, involving load measurements via a 6-axis load-cell ATI Nano17 /25N, deformation measurements via Digitial Image Processing (DIC) and planar flow measurements via two side-by-side cameras. </p> <p>3.) More setup and processing details can be found in the paper "On the fluid-structure interaction of flexible membrane wings for MAVs in and out of ground-effect" (2017) by the authors R. Bleischwitz, R. de Kat, B. Ganapathisubramani<br> Published in the Journal of Fluids and Structures (http://www.sciencedirect.com/science/article/pii/S088997461630370X)</p> <p>4.) All load/deformation/flow folders contain a README.txt(Use 1st) and Instructions.m (Use 2nd) file, which give further supporting details how to illustrate the data</p> <p>5.) This specific upload contains PART-2 of the full dataset, including introduction file + membrane-wing case (Load+DIC+PIV measurements) </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.