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Supplementary files for Machine learning for histological annotation and quantification of cortical layers
<div> <h2>Creators</h2> <ul> <li><a href="https://orcid.org/0009-0000-9093-9385">Meystre Julie</a></li> <li><a href="https://orcid.org/0000-0002-7100-3749">Olivier Burri</a></li> </ul> <h2>Contributors</h2> <ul> <li><a href="https://orcid.org/0009-0002-0029-7951">Jean Jacquemier</a></li> </ul> </div> <h2>Description</h2> <p>This dataset contains 7 <a href="https://qupath.github.io/">QuPath</a> projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well.</p> <p>The raw data contains images of 14 hemispheres from height animals.</p> <ul> <li>Nissl_1 : <ul> <li>animal 1413827 Right Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_2 : <ul> <li>animal 1413829 Right Hemisphere</li> <li>animal 1413828 Right Hemisphere</li> <li>animal 1413827 Left Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_3 : <ul> <li>animal 1413828 Left Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_4 : <ul> <li>animal 1443459 Right Hemisphere</li> <li>animal 1443460 Right Hemisphere</li> <li> </li> </ul> </li> <li>Nissl_5 : <ul> <li>animal 1443459 Left Hemisphere</li> <li>animal 1443460 Left Hemisphere</li> </ul> </li> </ul> <ul> <li>Nissl_6 : <ul> <li>animal 1449920 Left Hemisphere</li> <li>animal 1449921 Left Hemisphere</li> <li>animal 1449921 Right Hemisphere</li> <li>animal 1449922 Left Hemisphere</li> <li>animal 1449922 Right Hemisphere</li> <li> </li> </ul> </li> <li>QuPath_LayerBoundaries_GroundTruth_20220927: <ul> <li>This is the QuPath project that contains S1HL layers annotations done by the experts and which have been used to trained the Random forest Machine Learning method for the S1HL brain classification. It contains some images from all the eight animals.</li> </ul> </li> </ul> <p> </p> <div> <h3>Animals</h3> <p>All animal procedures were approved by the Veterinary Authorities and the Cantonal Commission for Animal Experimentation of the Canton of Vaud, according to the Swiss animal protection laws, under license number VD3516.</p> <p>Outbred Wistar Han rats (Janvier Laboratories, France) were ordered with their litter aged eight postnatal days (P8). Dams were housed individually and allowed to raise their own litters until experimentation on male offspring aged fourteen days (P14; N=8 animals; N=3 litters). Animals were housed in standard plastic laboratory cages, with bedding, nesting material and paper tube and ad libitum access to food (SAFE 150 SP-25) and water, cleaned once per week, and kept on a twelve-hour light-dark schedule with lights turned on at 06:30 AM, in rooms under controlled humidity and temperature. The sample size here is greater than those reported in other open source atlases <a href="https://www.zotero.org/google-docs/?1dkN18">(“Allen Reference Atlas - Mouse,” n.d.; “The Rat Brain in Stereotaxic Coordinates - 7th Edition,” n.d.)</a>.</p> <h3>Sample preparation</h3> <p>On postnatal day fourteen, rats were transferred to the experimental room in the morning to acclimate. The described procedure was conducted within a consistent 3-hour window of the day (09:00-12:00). Initially, the rats were deeply anesthetized using pentobarbital (intraperitoneal dose of 150 mg/kg; concentration of 150 mg/ml). This was succeeded by transcardial perfusion with ice cold 0.1 M phosphate buffer (PB; pH 7.4), followed by cold 4% paraformaldehyde (PFA) in 0.1 M PB. Subsequently, the brain was carefully removed from the skull, postfixed at 4°C in 4% PFA overnight, and then rinsed in 0.1 M PB. The brains underwent a sequential storage process: first in a 15% sucrose solution (in 0.1 M PB) at 4°C for approximately 24 hours, followed by a 30% sucrose solution at 4°C for an additional 24 hours. The hemispheres were carefully divided along the midline, after which both right and left hemispheres were precisely sliced sagittally using a cryostat (Leica, VT-1200S) at 50 µm employing an approximate angle rotation of 4 ± 1 degrees along the anterior-posterior axis to optimize alignment with apical dendrites. These brain slices were stored in a cryoprotectant solution (30% v/v ethylene glycol; 30% m/v sucrose in 0.1 M PB) at -20°C, preserving them until immunohistochemistry assays were executed (within a maximum of two weeks from extraction to immunohistochemistry).</p> <p>In order to determine the cell densities in P14 rat, brain slices were immunostained using cresyl violet, a stain specifically targeting cell bodies, including the endoplasmic reticulum, also known as Nissl substance or Nissl bodies. Free-floating sections of 50 µm thickness were transferred from cryoprotectant into 0.1 M PB to thaw and eliminate any cryoprotectant remnants. Subsequently, they were transferred into 0.01 M PB to minimize salt residues before being meticulously mounted onto SuperFrost© glass slides (Thermo Fisher Scientific Inc., Gerhard Menzel B.V. & Co. KG, GE). This mounting was carried out while considering the brain’s orientation relative to the midline, from its external to internal regions. Slide-mounted sections were processed using an automated slide stainer Tissue-Tek® Prisma Plus (Sakura Finetek-Europe, NL). These sections were incubated for 6 minutes at room temperature (RT = 20°C) in a 0.5% cresyl violet solution in water (with pH adjusted to 2.85 using acetic acid), followed by a brief wash in tap water. The sections underwent dehydration through a series of ethanol concentrations (70%, 70%, 96%, 100%, 100%) with each step lasting one minute at RT. Subsequently cleared with two steps of xylene for one minute each at RT, and the sections were mounted using Pertex (Sakura Finetek-Europe, NL) before being cover-slipped using the automated glass coverslipper Tissue-Tek® Glas™ g2 (Sakura Finetek-Europe, NL). A meticulous assessment of the coloration was conducted and if the staining appeared faint, a repeat staining procedure was carried out.</p> <p><strong>Immunostained slides were scanned using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel. Each brain slice was entirely scanned. Subsequently, the digital images obtained were meticulously organized and subjected to analysis using the open-source software QuPath v0.3.2 <a href="https://www.zotero.org/google-docs/?jnVnIg">(Bankhead et al., 2017)</a>. </strong></p> <p> </p> <h2>Intructions</h2> <p>The projects contained in this dataset have been created with QuPath v0.3.2 but could be opened with new QuPath version.</p> <ol> <li>Download the dataset</li> <li>untar the tar balls included in this dataset</li> <li>install <a href="https://qupath.github.io">QuPath</a></li> <li>Open QuPath</li> <li>Open a project within QuPath (Files->Project...->Open Project...)</li> </ol> </div>
Nissl_4, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains some images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Nissl_3, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Nissl_2, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Nissl_1, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p> <p> </p>
Nissl_6, Raw images for Machine learning for histological annotation and quantification of cortical layers.
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p> <p> </p> <p> </p>
Nissl_5, Raw images for Machine learning for histological annotation and quantification of cortical layers
<p>This dataset contains images (TIFF image data) of <strong>brain juvenile rats Wistar Han (P14)</strong> scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F505 Color camera leading to a pixel size of 0.346 μm/pixel.</p> <p>These raw images are part of another Zenodo dataset <span><a href="https://doi.org/10.5281/zenodo.11544829" target="_blank" rel="noopener noreferrer">https://doi.org/10.5281/zenodo.11544829</a></span>, that contains the QuPath projects that uses this dataset and 5 others (from Nissl_1 to Nissl_6).</p>
Cellpose training data and scripts from "Machine learning for histological annotation and quantification of cortical layers"
<p>This Workflow contains all the material necessary to reproduce the cells detection, thanks to the QuPath performed in the paper</p> <p> "<strong>Machine learning for histological annotation and quantification of cortical layers</strong>"</p> <p>Inside this workflow and dataset, you will find the following folders</p> <ol> <li><strong>QuPath Training Project</strong>: A QuPath 0.5.0 project containing all the manual annotations (ground truths) used to train the cellpose model, as well as the script to start the training</li> <li><strong>Training Images</strong> and <strong>Demo Images</strong>: The raw whole slide scanner images needed by the above QuPath project</li> <li><strong>Model</strong>: The fodler containing the trained cellpose model</li> <li><strong>cellpose-training Folder</strong>: The exported raw and ground truth images that the above cellpose model was trained on</li> <li><strong>Scripts</strong>: The QuPath scripts, also located in their respective QuPath projects, that were created for this whole workflow</li> <li><strong>QC</strong>: A Jupyter notebook, based on ZeroCostDL4Mic that computes quality metrics in order to assess the performance of the trained cellpose model. The folder also contains the resulting metrics.</li> </ol> <p>Installation and Use</p> <p>If you are going to use the QuPath projects, you need a local QuPath Installation https://qupath.github.io/ that is configured to run the QuPath Cellpose Extension https://github.com/BIOP/qupath-extension-cellpose as well as a working Cellpose installation https://github.com/MouseLand/cellpose</p> <p>Instructions for installation are available from the links above.</p> <p>After that, you should be able to open the QuPath project, navigate to the "Automate > Project scripts" menu and locate the script you wish to run.</p> <p><br>1. train a cell segmentation algorithm in the context of the rat brain Layer <br>Boundaries project </p> <p>2. trigger cell segmentation from a QuPath project in a semi-automated pipeline</p>
Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers
<p>All raw data and Matlab code necessary to produce the figures of https://elifesciences.org/reviewed-preprints/86805</p>
Data from: Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit
<p>The computational capabilities of neuronal networks are fundamentally constrained by their specific connectivity. Previous studies of cortical connectivity have been mostly carried out in rodents; however, whether the principles also apply to the evolutionary expanded human cortex is unclear. Here we studied network properties within the human temporal cortex using samples obtained from brain surgery. We analyzed multi-neuron patch-clamp recordings in layer 2-3 pyramidal neurons and identified substantial differences compared to rodents. Reciprocity showed random distribution, synaptic strength was independent from connection probability and connectivity of the supragranular temporal cortex followed a directed and mostly acyclic graph topology. Application of these principles in neuronal models increased the dimensionality of network dynamics suggesting a critical role for cortical computation.</p>
Data related to "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al.
<p>This is Data for "Upper cortical layer-driven network impairment in schizophrenia" paper, by Batiuk, Tyler et al., 2022</p> <p>This repository contains:</p> <p>Supplementary Dataset Tables 1-4 (Supplementary_Dataset_Tables_1-4.xlsx). They contain DE genes and GO terms from snRNA-seq and visium analysis.</p> <p>Single nuclei and Visium spatial transcriptomics sequencing data (snRNA-seq_and_spatial_transcriptomics.zip) containing raw count matrices of snRNA-seq samples; Conos object with aligned snRNA-seq samples; snRNA-seq single nuclei cell subtype annotations; raw count matrices of Visium spatial transcriptomics samples; Visium spatial transcriptomics manual histological cortical layer annotations; and 10x Genomics spaceranger count pipeline output for Visium spatial transcriptomics data.</p> <p>Histological images of H&E stained Visium spatial transcriptomics samples mounted on visium slide capture area (visium_sample_images.zip)</p>
Microscopic Quantification of Oxygen Consumption across Cortical Layers
<p>The cerebral cortex is organized in cortical layers that differ in their cellular density, composition, and wiring. Cortical laminar architecture is also readily revealed by staining for cytochrome oxidase – the last enzyme in the respiratory electron transport chain located in the inner mitochondrial membrane. It has been hypothesized that a high-density band of cytochrome oxidase in cortical layer IV reflects higher oxygen consumption under baseline (unstimulated) conditions. Here, we tested the above hypothesis using direct measurements of the partial pressure of O<sub>2</sub> (pO<sub>2</sub>) in cortical tissue by means of 2-photon phosphorescence lifetime microscopy (2PLM). We revisited our previously developed method for extraction of the cerebral metabolic rate of O<sub>2</sub> (CMRO<sub>2</sub>) based on 2-photon pO<sub>2</sub> measurements around diving arterioles and applied this method to estimate baseline CMRO<sub>2</sub> in awake mice across cortical layers. To our surprise, our results revealed<em> a decrease in baseline CMRO<sub>2</sub> from layer I to layer IV</em>. This decrease of CMRO<sub>2</sub> with cortical depth was paralleled by <em>an increase in tissue oxygenation. </em>Higher baseline oxygenation and cytochrome density in layer IV may serve as an O<sub>2</sub> reserve during surges of neuronal activity or certain metabolically active brain states rather than baseline energy needs. Our study provides the first quantification of microscopically resolved CMRO<sub>2</sub> across cortical layers as a step towards better understanding of the brain energy metabolism.</p>
Responses to axonal current injection in cortical layer 5 pyramidal neurons
<p>Patch-clamp recordings in cortical layer 5 pyramidal neurons performed by Wenqin Hu. A current pulse is either injected in the soma or in an axonal bleb and recorded simultaneously in the axonal bleb or the soma, respectively.</p> <p>The data are related to figure 7 of the paper: Hu, W., & Bean, B. P. (2018). Differential control of axonal and somatic resting potential by voltage-dependent conductances in cortical layer 5 pyramidal neurons. <em>Neuron</em>, <em>97</em>(6), 1315-1326.</p> <p> </p>
Data from: Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit
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Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits, Part 1/3
<p>Associated data and code for Hartung et al. "Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits".</p> <ul> <li>This is repository 1/3 and contains all code generated for and used in the paper.</li> <li>This repository contains all data for figures 1, 2 & 4-6 of the paper, as well as electrophysiological data for figure 3.</li> <li>Histological data for figure 3 can be found in repositories 2/3 (10.5281/zenodo.10938467) and 3/3 (10.5281/zenodo.10938471).</li> <li>The code can alternatively also be accessed via GitHub: https://github.com/janH-21/NDNF-interneurons-cortical-circuits</li> <li>Please consider citing our paper if you use our data or code (see GitHub repository for link).</li> <li>Please refer to the README file for orientation and contact JH or JJL if you have further questions (see paper for contact details).</li> </ul>
Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits, Part 2/3
<p>Associated data for Hartung et al. "Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits".</p> <ul> <li>This is repository 2/3 and contains histological data for figure 3.</li> <li>Repository 1/3 (10.5281/zenodo.10938947) contains all data for figures 1, 2 & 4-6 of the paper, as well as electrophysiological data for figure 3.</li> <li>Repository 1/3 (10.5281/zenodo.10938947) contains all code generated for and used in the paper. </li> <li>Repository 3/3 (10.5281/zenodo.10938471) contains additional histological data for figure 3.</li> <li>The code can alternatively also be accessed via GitHub: https://github.com/janH-21/NDNF-interneurons-cortical-circuits</li> <li>Please consider citing our paper if you use our data or code (see GitHub repository for link).</li> <li>Please refer to the README file for orientation and contact JH or JJL if you have further questions (see paper for contact details).</li> </ul>
Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits, Part 3/3
<p>Associated data for Hartung et al. "Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits".</p> <ul> <li>This is repository 3/3 and contains histological data for figure 3.</li> <li>Repository 1/3 (10.5281/zenodo.10938947) contains all data for figures 1, 2 & 4-6 of the paper, as well as electrophysiological data for figure 3.</li> <li>Repository 1/3 (10.5281/zenodo.10938947) contains all code generated for and used in the paper. </li> <li>Repository 3/3 (10.5281/zenodo.10938467) contains additional histological data for figure 3.</li> <li>The code can alternatively also be accessed via GitHub: https://github.com/janH-21/NDNF-interneurons-cortical-circuits</li> <li>Please consider citing our paper if you use our data or code (see GitHub repository for link).</li> <li>Please refer to the README file for orientation and contact JH or JJL if you have further questions (see paper for contact details).</li> </ul>
Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia
<p>Nature relies on highly distributed computation for the processing of information in nervous systems across the entire animal kingdom. Such distributed computation can be more easily understood if decomposed into the three elementary components of information processing, i.e., storage, transfer and modification, and rigorous information theoretic measures for these components exist. However, the distributed computation is often also linked to neural dynamics exhibiting distinct rhythms. Thus, it would be beneficial to associate the above components of information processing with distinct rhythmic processes where possible. Here we focus on the storage of information in neural dynamics and introduce a novel spectrally-resolved measure of active information storage (AIS). Drawing on intracortical recordings of neural activity in ferrets under anesthesia before and after loss of consciousness (LOC), we show that anesthesia-related modulation of AIS is highly specific to different frequency bands and that these frequency-specific effects differ across cortical layers and brain regions. We found that in the high/low gamma band, the effects of anesthesia result in AIS modulation only in the supergranular layers, while in the alpha/beta band, the strongest decrease in AIS can be seen at infragranular layers. Finally, we show that the increase of spectral power at multiple frequencies, in particular at alpha and delta bands in frontal areas, that is often observed during LOC ('anteriorization') also impacts local information processing – but in a frequency-specific way: Increases in isoflurane concentration induced a decrease in AIS in the alpha frequencies, while they increased AIS in the delta frequency range $<2$Hz. Thus, the analysis of spectrally-resolved AIS provides valuable additional insights into changes in cortical information processing under anaesthesia.</p>
Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia
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METHODS. Bovine ilia were used in the simulations because their histological structure (a fibrolamellar cortex overlying cancellous bone26) was found to match that of the Triceratops ilium. Bone sections 10 x 50 x 縠 3.0 cm with cortices ranging from 0.5 to 5.5 mm in depth (the range of initial cortical-thickness estimates based on gross morphology) were mounted on a servohydraulic mechanical loading frame (MTS Bionix, Minneapolis) and penetrated with an aluminium-bronze T. rex tooth replica. The replica was cast from an actual adult T. rex maxillary tooth, after casts made from some ofthe deeper bite marks revealed the size and shape of the teeth that had impacted the pelvis8 • The replica was penetrated into the ilia sections at 1 mm s-1 to a depth of 11.5 mm, equivalent to the maximum depth of the deepest ilium bite mark8 • Forces were measured with an MTS 25 N strain-gauge-based axial load cell accurate to 0.2%. The forces increased with increasing penetration depth even after the cortical layer had been perforated and the underlying cancellous bone was being crushed. The increase in force with penetration depth is attributed to a greater cortical surface area coming into contact with the semi-conical penetrator tooth as it descended through the ilia. in Bite-force estimation for Tyrannosaurus rex from tooth-marked bones
METHODS. Bovine ilia were used in the simulations because their histological structure (a fibrolamellar cortex overlying cancellous bone26) was found to match that of the Triceratops ilium. Bone sections 10 x 50 x 縠 3.0 cm with cortices ranging from 0.5 to 5.5 mm in depth (the range of initial cortical-thickness estimates based on gross morphology) were mounted on a servohydraulic mechanical loading frame (MTS Bionix, Minneapolis) and penetrated with an aluminium-bronze T. rex tooth replica. The replica was cast from an actual adult T. rex maxillary tooth, after casts made from some ofthe deeper bite marks revealed the size and shape of the teeth that had impacted the pelvis8 • The replica was penetrated into the ilia sections at 1 mm s-1 to a depth of 11.5 mm, equivalent to the maximum depth of the deepest ilium bite mark8 • Forces were measured with an MTS 25 N strain-gauge-based axial load cell accurate to 0.2%. The forces increased with increasing penetration depth even after the cortical layer had been perforated and the underlying cancellous bone was being crushed. The increase in force with penetration depth is attributed to a greater cortical surface area coming into contact with the semi-conical penetrator tooth as it descended through the ilia.
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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.