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5,448 results for “Neuron”
Data from: Neuronal function and dopamine signaling evolve at high temperature in Drosophila
<p>Neuronal activity is temperature sensitive and affects behavioral traits important for individual fitness, such as locomotion and courtship. Yet, we do not know enough about the evolutionary response of neuronal phenotypes in new temperature environments. Here, we use long-term experimental evolution of <em>Drosophila simulans</em> populations exposed to novel temperature regimes. Here, we demonstrate a direct relationship between thermal selective pressure and the evolution of neuronally expressed molecular and behavioral phenotypes. Several essential neuronal genes evolve lower expression at high temperatures and higher expression at low temperatures, with dopaminergic neurons standing out by displaying the most consistent expression change across independent replicates. We functionally validate the link between evolved gene expression and behavioral changes by pharmacological intervention in the experimentally evolved <em>D. simulans</em> populations as well as by genetically triggered expression changes of key genes in <em>D. melanogaster</em>. As natural temperature clines confirm our results for <em>Drosophila</em> and <em>Anopheles</em>populations, we conclude that neuronal dopamine evolution is a key factor for temperature adaptation.</p>
Generating high fidelity surface meshes of neocortical neurons using skin modifiers
<h2>Summary</h2> <p>The dataset contained in this repository is the resulting meshes from applying the Skin Modifier in Blender on all neuronal morphological types (mtypes) identified for comastosensory cortical cells by the Blue Brain Project. </p> <p>The papser, <strong>Generating high fidelity surface meshes of neocortical neurons using skin modifiers,</strong> is published in the 2019 <em>EG Computer Graphics & Visual Computing (CGVC) </em>conference<em>. </em></p> <div><a href="https://doi.org/10.2312/cgvc.20191257">https://doi.org/10.2312/cgvc.20191257</a><br><a href="https://diglib.eg.org/handle/10.2312/cgvc20191257">https://diglib.eg.org:443/handle/10.2312/cgvc20191257</a></div> <h2>Paper abstract </h2> <div>We present the results of exploring the capabilities of skinning modifiers to generate high fidelity polygonal surface meshes of neurons from their morphological skeletons that are segmented from optical microscopy slides. Our algorithm is implemented in Blender as an add-on relying on its standard Python API. The implementation is also integrated into an open source domain specific framework, NeuroMorphoVis, that is used to visualize and analyze neuronal morphologies available from the neuroscientific community. Our technique is applied to create meshes for a set of neurons with 55 different morphologies reconstructed from the neocortex of a 14-days-old rat. The generated meshes are used to visualize full compartmental simulations of neocortical activity for analysis purposes and also to create high quality scientific illustrations of in silico neuronal circuits for media production with physically-based path tracers.</div> <h2>BibTex citation</h2> <blockquote> <p><em>@inproceedings {10.2312:cgvc.20191257,</em><br><em> booktitle = {Computer Graphics and Visual Computing (CGVC)},</em><br><em> editor = {Vidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.},</em><br><em> title = {{Generating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiers}},</em><br><em> author = {Abdellah, Marwan and Favreau, Cyrille and Hernando, Juan and Lapere, Samuel and Schürmann, Felix},</em><br><em> year = {2019},</em><br><em> publisher = {The Eurographics Association},</em><br><em> ISBN = {978-3-03868-096-3},</em><br><em> DOI = {10.2312/cgvc.20191257}</em><br><em> }</em></p> </blockquote>
Data from: Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture
<p>Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or 'oscillatoriness' per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for direct quantification of rhythmicity. We applied pACF to human intracerebral stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure 'burstiness' of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.</p>
A phosphate transporter in VIPergic neurons of the suprachiasmatic nucleus gates locomotor activity during the light/dark transition in mice
<p>Raw Data for <strong>An atypical phosphoswitch in specific neurons of the suprachiasmatic nucleus gates locomotor activity at the light/dark transition in mice</strong></p> <ol> <li>Multi-electrode array data from PiT2<sup>+/+</sup> and PiT2<sup>-/-</sup> mice</li> <li>Locomotor activity from PiT2<sup>+/+ </sup>and PiT2<sup>-/-</sup> mice in LD 12:12, DD, LD 4:20, LD 20:4 and DD with light pulses</li> <li>Phosphoproteome data</li> </ol>
A Prefrontal Cortex Map based on Single Neuron Activity
<p>Intermediate results files (after preprocessing) to recreate the figures from:</p> <p><em><strong>A Prefrontal Cortex Map based on Single Neuron Activity, </strong></em>Pierre Le Merre*, Katharina Heining*<em>, Marina Slashcheva, Felix Jung, Eleni Moysiadou, Nicolas Guyon, Ram Yahya, Hyunsoo Park, Fredrik Wernstal, and </em><em>Marie </em><em>Carlén.</em> Equal contribution.</p> <p>Article currently available at: https://www.biorxiv.org/content/10.1101/2024.11.06.622308v2</p> <p>The raw data can be found at: https://dandiarchive.org/dandiset/001260?pos=1</p> <p>Description: Brain-wide high-density extracellular recordings (Neuropixels) in head-fixed mice during distinct passive listening and auditory behavioral tasks. About half of the recordings are in the prefrontal cortex (PFC) and half from other brain regions (3 cortical and 10 subcortical). In PFC we include 11 subregions: secondary motor area (MOs), anterior cingulate area – dorsal and ventral part (ACAd, ACAv), prelimbic area (PL), infralimbic area (ILA), orbital area – medial, lateral, and ventrolateral part (ORBm, ORBvl, ORBl), agranular insular area – dorsal and ventral part (AId, AIv), and frontal pole (FRP).</p> <p>The code generating these intermediate files from the raw data can be found at: https://github.com/hejDMC/pfcmap</p> <p> </p>
The Cellular and Extra-Cellular Proteomic Signature of Human Dopaminergic Neurons Carrying the LRRK2 G2019S Mutation
<p>The provided data sets were generated during the work described in "The Cellular and Extra-Cellular Proteomic Signature of Human Dopaminergic Neurons Carrying the LRRK2 G2019S Mutation" published in Frontiers in Neuroscience, 2024.</p> <p>They contain the results from our differential expression analysis of our raw DIA Protemic Data as well as a list of input data for GO analyses.</p>
Source data for Chen et al (2024) entitled "Motor Cortical Neuronal Hyperexcitability Associated with α-Synuclein Aggregation"
<div> </div> <div>--------------------</div> <div>GENERAL INFORMATION </div> <div>--------------------</div> <div>This readme file was generated on [2024-01-15] by [Liqiang Chen].</div> <div> </div> <div>Title of Dataset:</div> <div>Description of Dataset: </div> <div>Principal Investigator: Hong-Yuan Chu, hc948@georgetown.edu, ORCID: 0000-0003-0923-683X. </div> <div>Date of Data Collection: 2023-04-01 to 2024-11-10 </div> <div>Software Dependencies: Excel and Image J.</div> <div> </div> <div>-------------</div> <div>FILE OVERVIEW </div> <div>-------------</div> <div>Directory of Files: Source data, Electrophysiology trace data, and Microscopy images.</div> <div>Relationship Between Files: Source data is used to make figures in GraphPad. Electrophysiology trace data is used to plot electrophysiology traces. Microscopy images are used for representative images. </div> <div>File Formats: Microsoft Excel Worksheet (.xlsx) and confocal images (.nd2)</div> <div>File Naming Convention: Based on file formats.</div> <div> </div> <div>----------------------------------------</div> <div>DATA SPECIFIC INFORMATION FOR [Source data]</div> <div>Date of Creation: 2024-08-13</div> <div>Description of Data: Source data is used to make figures in GraphPad.</div> <div>A. Missing data are represented n/a.</div> <div>B. Abbreviations (Primary cortex: M1; Secondary cortex: M2; α-Synulein: αSyn; intratelencephalic neurons: ITNs; corticospinal neurons: CSNs).</div> <div>C. Figure 5B data (ITN-Sholl analysis-Intersections-5 µm Radius) can not organized as tidy format because of too many data points in each group. </div> <div> </div> <div>DATA SPECIFIC INFORMATION FOR [Electrophysiology trace data]</div> <div>Date of Creation: 2024-08-13</div> <div>Description of Data: Electrophysiology trace data is used to plot electrophysiology traces.</div> <div>A. Electrophysiology traces can be plotted using Excel.</div> <div>B. Traces are plotted in Electrophysiology trace data file.</div> <div> </div> <div>DATA SPECIFIC INFORMATION FOR [Microscopy images]</div> <div>Date of Creation: 2024-08-13</div> <div>Description of Data: Microscopy images are used to make representative images. </div> <div>A. Microscopy images can be opened using Image J. </div> <div>B. Microscopy images are named based on experimental group, animal ID, and figure number in the manuscript. </div> <div> </div> <div> </div> <div>-----------</div> <div>METHODOLOGY</div> <div>-----------</div> <div>Description of methods used for data collection: Electrophysiology data is collected using MultiClamp 700B amplifier and Digidata 1550B. pClamp 11 software is used. Microscopy images are collected using an confocal microscope. </div> <div> </div> <div>Description of methods used for data processing: </div> <div>A. Electrophysiology data is processed using clampfit software, including measure the peak of EPSC, count the number of action potentials, measure the width/rise time/decay time of action potential.</div> <div>B. Microscopy images are processed using Image J software, including measure the α-Synulein pathologic area in motor cortex, quantify the TH staining, and verify the co-localization of pS129 and biocytin. </div> <div>C. All data after processed through clampfit and Image J is put into GraphPad to make figures.</div> <div> </div> <div>-----------------------</div> <div>DATA ACCESS AND SHARING</div> <div>-----------------------</div> <div>This research was funded in part by </div> <div>1. Aligning Science Across Parkinson’s (ASAP-020572) through the Michael J. Fox Foundation for Parkinson’s Research (MJFF).</div> <div>2. National Institute of Neurological Disorders and Stroke (R01NS121374).</div> <div>3. Congressionally Directed Medical Research Programs (W81XWH-21-1-0943).</div> <p> </p>
Fig 3 eYFP sustained inactivation controls, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla
<p>Sleep recording data eYFP controls. Associated with Figure 3. for "Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla". Associated with Figure 3.</p>
Raw recordings for electrophysiological characterization of CNO-DREADD mediated responses in noradrenergic locus coeruleus (LC) neurons from lines RR1(P), RR2(P), and RC::FPDi (P).
<p>Whole-cell recordings were performed as described previously by the Jiang lab. Briefly, patch pipettes (2-7 MΩ) were filled with an internal solution containing 120 mM potassium gluconate, 10 mM HEPES, 4 mM KCl, 4 mM MgATP, 0.3 mM Na<sub>3</sub>GTP, 10 mM sodium phosphocreatine and 0.5% biocytin (pH 7.25). Whole-cell recordings from up to 8 LC neurons were performed using two Quadro EPC 10 amplifiers (HEKA Electronic, Germany). PatchMaster (HEKA) and custom-written Matlab-based programs (Mathworks) were used to operate the recording system and perform online and offline data analysis. In current-clamp recordings, neurons were first current clamped at ~-40pA to prevent spontaneous firing. Prior to investigating the effect of drugs, we calculated spike thresholds and recorded firing patterns in response to sustained depolarizing currents by injecting increasing current steps (+10pA). Continuous recordings were obtained from LC neurons current clamped at -40 to 0 pA during drug wash-on experiments. We also calculated other intrinsic electrophysiological parameters, such as the input resistance, membrane time constant, spike amplitude, after-hyperpolarization (AHP) etc.</p>
Medial prefrontal cortex and anteromedial thalamus interaction regulates motivation related behavior and dopaminergic neuron activity: Animal Behavior
<p>The excel Source DATA file contains the data described in Figures 2c, 2d, 2f, and 3b and Supplementary Figure 3b and 3c. The fiber photometry data described in Supplementary Figure 9 are found in the CSV files. The CSV file names reflect animal IDs. </p>
Task-induced modulations of neuronal activity along the auditory pathway
<p><span>Sensory processing varies depending on behavioral context. Here, we ask how task-engagement modulates neurons in the auditory system. We train mice in a simple tone-detection task, and compare their neuronal activity during passive hearing and active listening. Electrophysiological extracellular recordings in the inferior colliculus, medial geniculate body, primary auditory cortex and anterior auditory field reveals widespread modulations across all regions and cortical layers, and in both putative regular and fast-spiking cortical neurons. Clustering analysis unveils ten distinct modulation patterns that can either enhance or suppress neuronal activity. Task-engagement changes the tone-onset response in most neurons. Such modulations first emerge in subcortical areas, ruling out cortical feedback as the only mechanism underlying subcortical modulations. Half the neurons additionally display late modulations associated with licking, arousal or reward. Our results reveal the presence of functionally distinct subclasses of neurons, differentially sensitive to specific task-related variables but anatomically distributed along the auditory pathway.</span></p>
Activity of individual neurons in the Aplysia buccal ganglion before and after in vitro operant conditioning
<p>Dataset for Costa RM, Baxter DA, Byrne JH (2021) Neuronal population activity dynamics reveal a low-dimensional signature of operant learning in Aplysia. Commun Biol. BioRxiv preprint 2021.12.06.471434</p> <p>Data collected by using voltage-sensitive dye imaging to capture population-wide, single neuron resolution activity in the isolated <em>Aplysia</em> buccal ganglion before and after <em>in vitro</em> operant conditioning. Data for 7 contingently trained and 7 yoked control animals includes population spike trains, timing of buccal motor patterns, ganglia images, and pixel coordinates for each neuron (kernels). In addition, the data includes results of the non-negative matrix factorization analyses described in the article.</p> <p>The dataset is organized as a Matlab structure variable.</p>
Datasets for neuronal imaging, extracellular recordings and behavioral rig code
<p>Rod and cone photoreceptors degenerate in retinitis pigmentosa (RP). While downstream neurons survive, they undergo physiological changes, including accelerated spontaneous firing in retinal ganglion cells (RGCs). Retinoic acid (RA) is the molecular trigger of RGC hyperactivity, but whether this interferes with visual perception is unknown. Here we show that inhibiting RA synthesis with disulfiram, a deterrent of human alcohol abuse, improves behavioral image detection in vision-impaired mice. <i>In vivo</i> Ca<sup>2+</sup> imaging shows that disulfiram sharpens orientation-tuning of visual cortical neurons and strengthens fidelity of responses to natural scenes. An RA receptor inhibitor also reduces RGC hyperactivity, sharpens cortical representations, and improves image detection. These findings suggest that photoreceptor degeneration is not the only cause of vision loss in RP. RA-induced corruption of retinal information processing also degrades vision, pointing to RA synthesis and signaling inhibitors as potential therapeutic tools for improving sight in RP and other retinal degenerative disorders.</p>
Supplementary materials of Brain Extract of Subacute Traumatic Brain Injury Promotes Neuronal Differentiation of Human Neural Stem Cells Via Autophagy
<p>Figure S1: Characterization of hNSCs. In proliferate medium, the embryo derived hNSCs could form the neurospheres (A). Markers of NSCs (NES, SOX2, SOX1) were detected (B). The SOX2<sup>+</sup> cells accounted for >95% of all cells (C). Bar scale: 60μm;</p> <p>Figure S2: The proliferation of hNSCs in TBI brain extracts with different phases. Differentiated hNSCs could be labeled with EdU in TBI brain extracts with different phases (A). Ratio of EdU<sup>+</sup> cells was decreased with the progression of days post-injury (B). Bar scale: 75μm; ****: P<0.0001;</p> <p>Figure S3: The GO annotations and KEGG pathway analysis of different expressed pro-teins in acute brain extract and subacute brain extract;</p> <p>Table S1: The protein profiles of brain extract in acute phase and subacute phase after TBI.</p>
Somatic mutations alter the differentiation outcomes of iPSC-derived neurons (Metadata and AnnData/H5AD files)
<p><strong>Data S1:</strong> Metadata information for the 828,937 processed cells from the DN dataset: donor identity, cell type annotation, pool identifier, 10x sample, time point and replicate information. Related to STAR Methods: Reanalysis of pooled single-cell data (DA).</p> <p><strong>Data S2-S4:</strong> AnnData/H5AD files containing the single-cell gene expression matrices and the metadata for day 11, day 30 and day 52, respectively. The gene expression is normalised and log-transformed, but not scaled. md5 files are also included. Related to STAR Methods: DE analysis between failed and successful lines.</p> <p>#File names:</p> <p><strong>File-Data S1: </strong>suppData1.RDS</p> <p><strong>File-Data S2: </strong>allpools.scanpy.D11.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p> <p><strong>File-Data S3: </strong>allpools.scanpy.D30.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p> <p><strong>File-Data S4: </strong>allpools.scanpy.D52.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p>
Confocal images of rDRG neurons overexpressing human GDAP1 and its R120W and H123R variants
<p>Original confocal images for panels in Figure 6 in Sutinen et al. (2022). The images correspond to rDRG primary neurons overexpressing RFP or human GDAP1 (wild-type, R120W, H123R variants). Included is a Zip file with 16 TIFF files, one for each panel. </p>
DA Diffusion Simulation for: A fluorescent nanosensor paint reveals the heterogeneity of dopamine release from neurons at individual release sites
<p>Code developed for the revision of:</p> <p>Elizarova, S., Chouaib, A., Shaib, A., Mann, F., Brose, N., Kruss, S., & Daniel, J. A. (2021). A fluorescent nanosensor paint reveals the heterogeneity of dopamine release from neurons at individual release sites. BioRxiv, 2021.03.28.437019. https://doi.org/10.1101/2021.03.28.437019</p>
Data from: Neuron numbers link innovativeness with both absolute and relative brain size in birds
<p>A long-standing issue in biology is whether the intelligence of animals can be predicted by absolute or relative brain size. However, progress has been hampered by an insufficient understanding of how neuron numbers shape internal brain organization and cognitive performance. Based on estimations of neuron numbers for 111 bird species, we show here that the number of neurons in the pallial telencephalon is positively associated with a major expression of intelligence: innovation propensity. The number of pallial neurons, in turn, is greater in brains that are larger in both absolute and relative terms, and positively co-varies with longer post-hatching development periods. Thus, our analyses show that neuron numbers link cognitive performance to both absolute and relative brain size through developmental adjustments. These findings help unify neuro-anatomical measures at multiple levels, reconciling contradictory views over the biological significance of brain expansion. The results also highlight the value of a life history perspective to advance our understanding of the evolutionary bases of the connections between brain and cognition.</p>
Neuronal Transfer Networks (Trainings)
<p>Training versions of the AlexNet Convolutional Neural Network (CNN), related to the evaluation of eye diseases. </p> <p> </p> <p>-The following datasets were used in this work:</p> <ul> <li><strong>LAG. </strong>Contains fundus images with positive and negative glaucoma samples obtained from Beijing Tongren Hospital. Each fundus image is diagnosed by qualified glaucoma specialists, taking into consideration of both morphologic and functional analysis. https://arxiv.org/abs/1903.10831</li> <li><strong>APTOS. </strong>Contains images of diabetic retinopathy that were used in the APTOS 2019 blindness screening competitions. Each image has been resized and cropped to have a maximum size of 1024px. A certified clinician rated each image according to the severity of diabetic retinopathy on a scale of 0 to 4. https://www.kaggle.com/c7934597/resized-2015-2019-diabetic-retinopathy-detection/metadata/</li> <li><strong>HRF. </strong>Contains 15 images of healthy patients, 15 images of patients with diabetic retinopathy and 15 images of glaucomatous patients. They were captured by a Canon CR-1 fundus camera with a field of view of 45 degrees with a resolution of 3504×2336 px. https://www5.cs.fau.de/research/data/fundus-images/</li> <li><strong>ODIR. </strong>Contains images of 5000 patients with various eye diseases collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. The fundus images are captured with various cameras on the market, resulting in varied image resolutions. They classify patients into eight labels based on the images of both eyes. https://odir2019.grand-challenge.org/dataset/</li> <li><strong>sjchoi86-HRF. </strong>This dataset contains 601 fundus images divided into 4 groups: normal (300 images), glaucoma (101 images), cataract (100 images) and retina disease (100 images). https://github.com/cvblab/retina_dataset</li> </ul> <p>-Specifications of the data used for training in each version of the Convolutional Neural Network (CNN):</p> <ul> <li>netTransfer: Based on glaucoma and non-glaucoma imag-es cases existing in the LAG-database.</li> <li>netTransfer2: Based on glaucoma and non-glaucoma imag-es cases existing in the LAG-database and the sjchoi86-HRF database (this data-base also contains images on other pathologies, but only the glaucoma samples were used for this training)</li> <li>netTransfer3: Based on glaucoma, diabetic retinopathy and non-disease images cases existing in the LAG-database, sjchoi86-HRF database and the HRF database.</li> <li>netTransfer4: Based on glaucoma, diabetic retinopathy and non-disease images cases existing in the LAG-database, sjchoi86-HRF database, HRF database and the APTOS database. However, data from the fourth iteration was corrupted and then there for lost.</li> <li>netTransfer5: Based on glaucoma, diabetic retinopathy and non-disease images cases existing in the LAG-database, sjchoi86-HRF database, HRF database and the APTOS database (Images were cropped slightly before training).</li> <li>netTransfer6: Based on glaucoma, diabetic retinopa-thy and non-disease images cases existing in the LAG-database, sjchoi86-HRF database, HRF database, APTOS database and ODIR database (this database also contains images on several other diseases, but only the glaucoma and diabetic retinopathy samples were used for this training; furthermore, cropping was used for the images in order the eliminate black borders).</li> </ul> <p><strong>PLEASE CITATE AS:</strong></p> <p>Arias-Serrano I, Velásquez-López PA, Avila-Briones LN <em>et al.</em> Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural network [version 1; peer review: 1 approved with reservations]. <em>F1000Research</em> 2023, <strong>12</strong>:14 (<a href="https://doi.org/10.12688/f1000research.122288.1">https://doi.org/10.12688/f1000research.122288.1</a>)</p>
Basal Ganglia Neurons in Healthy and Parkinsonian Primates Generate Recurring Sequences of Spikes
<p>These data are the source data for the manuscript entitled "Basal Ganglia Neurons in Healthy<br> and Parkinsonian Primates Generate Recurring Sequences of Spikes", authored by Adriana Galvan<br> and Thomas Wichmann (Emory University, Atlanta, GA). </p> <p>The data are interspike interval measurements generated from electrophysiological recordings<br> of spontaneous spiking activities of single neurons in the basal ganglia of awake monkeys, before<br> and after rendering them parkinsonian by treatment with MPTP. THe animals were not engaged in any<br> task or other structured activity.</p> <p>The data format is a one-column listing of successive interspike intervals, and are presented in<br> milliseconds. The data are structured into different folders:</p> <p>GPe normal - data collected in the external pallidal segment, under normal conditions<br> GPe MPTP - data collected in the external pallidal segment, under parkinsonian conditions<br> STN normal - data collected in the subthalamic nucleus, under normal conditions<br> STN MPTP - data collected in the subthalamic nucleus, under parkinsonian conditions<br> GPi normal - data collected in the internal pallidal segment, under normal conditions<br> GPi MPTP - data collected in the internal pallidal segment, under parkinsonian conditions</p> <p>A 'Read Me' file is included, containing these explanations.</p> <p>For questions, please contact the corresponding author, Dr. Thomas Wichmann (twichma@emory.edu).</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.