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2,474 results for “Segmentation”
Segmentations of the core of the acoustic radiation in HCP data
<p><strong>Description of the repository</strong>:</p> <p>The goal of our paper (<a href="https://doi.org/10.3389/fneur.2022.934650">https://doi.org/10.3389/fneur.2022.934650</a>) was to segment the acoustic radiation (AR), one of the most important white matter fiber bundles of the hearing system. This repository contains the segmentations masks of the AR we created from 105 subjects of the Human Connectome Project (HCP) young adult dataset (<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>). These subjects are exactly the same used by Wasserthal et al. (2018) <a href="https://doi.org/10.1016/j.neuroimage.2018.07.070">https://doi.org/10.1016/j.neuroimage.2018.07.070</a>.</p> <p>In the file "data_training.tar.gz", one directory was created per HCP subject. Every directory contains the file "bundle_masks_AR.nii.gz" that contains the binary masks for the left and right AR.</p> <p>In our paper, we used these masks to train TractSeg. The file best_weights_ep110.npz contains the weights after training TractSeg that can be used in inference for targeting the AR. For using these weights on new data, one can use TractSeg with the option "--exp_name best_weights_ep110.npz". Please read the documentation of TractSeg and our paper for more information.</p> <p> </p> <p>If you use the training data or the pre-trained network, please cite our publication:</p> <p>Malin Siegbahn, Cecilia Engmér Berglin, Rodrigo Moreno. Automatic segmentation of the core of the acoustic radiation in humans. Frontiers in Neurology (2022) 13:934650. doi: 10.3389/fneur.2022.934650</p>
Shifts Multiple Sclerosis Lesion Segmentation Dataset Part 2
<p>This archive contains part 2 of Shift Benchmark on Multiple Sclerosis lesion segmentation data. This dataset is provided by the Shifts Project to enable assessment of the robustness of models to distributional shift and the quality of their uncertainty estimates. This part is contains data collected from several different sources and distributed under a CC BY NC SA 4.0 license. Part 1 of the data is available <a href="https://zenodo.org/record/7051658">here</a>. A full description of the benchmark is available in https://arxiv.org/pdf/2206.15407. To find out more about the Shifts Project, please visit https://shifts.ai .</p>
Segmentation of oocyte zona pellucida in transmitted light images (mouse and human)
<p>This dataset has been presented in our paper "An interpretable and versatile machine learning approach for oocyte phenotyping", in bioRxiv.</p> <p>It contains images acquired in transmitted light with different settings of mouse and human oocytes, with the corresponding ground-truth of the zona pellucida segmentation. Mouse oocyte images were taken before and during oocyte maturation (meiosis I). Some human oocyte images were taken during oocyte maturation (meiosis I), and some are M-II oocytes just after fertilization.</p>
DOORS: Dataset fOr bOuldeRs Segmentation
<p>The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations and navigation. This task is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets for these applications damps the research about data-driven algorithms. To tackle these challenges, the Dataset fOr bOuldeRs Segmentation (DOORS) has been designed. The dataset is thought to be useful for (but not limited to) boulders recognition, centroid regression, segmentation, and navigation applications. The dataset is divided into two sets:</p> <ul> <li><strong>Regression: </strong>Contains images, masks, and labels for 4 splits of single boulders positioned on the surface of a spherical mesh. It can be used to perform navigation, boulder recognition, segmentation, and centroid regression.</li> <li><strong>Segmentation: </strong>Contain images, masks, and labels of 2 datasets: DS1 and DS2. DS1 is made of the same images of the Regression dataset but is specifically designed for segmentation. DS2 is made of images with multiple instances of boulders appearing on the surface of the Didymos asteroid model</li> </ul> <p>A detailed characterization of the statistical properties of the DOORS dataset and the description of the Blender setup used to generate it is visible in "DOORS: Dataset fOr bOuldeRs Segmentation. Statistical properties and Blender setup", by Mattia Pugliatti and Francesco Topputo, arXiv pre-print, Oct 2022. </p> <p> </p>
Platynereis dumerilii - Aligned serial sections of the posterior segments
<p>Aligned serial semi-thin sections (1µm) of the posterior most segments of <em>Platynereis dumerilii.</em></p>
WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data
<p>The WAW-TACE dataset contains multiphase abdominal CT images from N=233 treatment-naive patients with HCC treated with TACE in monotherapy, annotated with N=377 hand-crafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data.</p> <p> </p>
The Monk Line Segmentation (MLS) Dataset
<p>Overview</p> <p>The MLS dataset available from this page consists of 31 handwritten page scans. The dataset contains medieval, historical and contemporary manuscripts, and has the purpose of testing line-segmentation algorithms. The collection contains a wide variation of the common problems in handwriting recognition: lines with overlapping ascenders/descenders, slightly rotated scans and curved base lines. <br> </p> <p>Download</p> <p>The MLS dataset was collected from the Monk system as of Friday May 17 14:15:04 CEST 2013. It was collected by Lambert Schomaker in May 2013 at the Institution of Artificial Intelligence and Cognitive Engineering (ALICE), University of Gronigen. </p> <p>The tar.gz file contains the image dataset for historical manuscripts. For more details please refer to the README file in the tar.gz file. The dataset downloaded for research use only. © 2013 Copyright. <br> </p> <p>@INPROCEEDINGS{Surinta:2014:ICFHR,<br> author = {O. Surinta and M. Holtkamp and M. F. Karaaba and JP. van Oosten and L. R. B. Schomaker and M. A. Wiering},<br> title = {A* Path Planning for Line Segmentation of Handwritten Documents},<br> booktitle = {Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on},<br> year = {2014},<br> month = {Sep},<br> pages = {175-180},<br> numpages = {6},<br> isbn = {978-1-4799-4335-7},<br> issn = {2167-6445},<br> publisher = {IEEE},<br> doi = {http://dx.doi.org/10.1109/ICFHR.2014.37},<br> }</p>
MICCAI 2016 MS lesion segmentation challenge: supplementary results
<p>This package contains supplementary material for our article prepared for publication and under revision. It contains omitted results due to space limits of the article as well as detailed, patient per patient and team per team results for all metrics. Additional figures redundant with those of the article are also provided. </p> <p>The readme file Readme_SupplementalMaterial.txt provides details about each individual file content.</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: dataset
<p>This dataset contains images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation, 3D visualization and geometric analysis is presented in the corresponding manuscript. Files are uploaded in 16bit .tif format and are named: mouseid_pressurelevel_stacknumber, with mouseid consisting of either Apoe (Apoe-deficient) or Bl (wild-type) and the mouse number, pressurelevel varies from P0 to P120 and stacknumber indicates which image from the stack has been uploaded.</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: 2D segmentations
<p>This dataset contains 2D segmentations of images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation algorithm, 3D visualization and geometric analysis are presented in the corresponding manuscript. Files are uploaded in .jpg format and are named: lamella_slicenumber, with slicenumber varying from 1 to 8100. There is also a Matlab file, UndulationData_Zenodo.mat, in which all the relevant variables post analysis are stored. This file contains a variable called "myFiles", which contains the link between the slicenumbers used here and the original dataset that is published in Zenodo (.tif synchrotron images).</p>
Code and Data from: Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets
<p>This record contains code and data for segmentation using a three-dimensional level-set method, written by Amy Tabb in C++. The record also contains two datasets of root systems in media imaged with X-Ray CT, and the results of running the code on those datasets. The code will also perform a pre-processing task in three-dimensional image sets, and a dataset for that purpose is included as well. This work is a companion to the paper : "Segmenting root systems in X-ray computed tomography images using level sets" (WACV 2018) by the authors or this record, and and open-access version of the paper is here -- https://arxiv.org/abs/1809.06398 . The code is also available from Github: https://github.com/amy-tabb/tabb-level-set-segmentation , using a DOI and stable releases https://doi.org/10.5281/zenodo.3344906.</p> <p>Format of the data:</p> <p>Three input datasets are provided; two for the segmentation functionality of the code, and one to test the pre-processing functionality. The two segmentation sets are the same as were used in the paper, and are CassavaDataset, and SoybeanDataset. The pre-processing set is CassavaSlices. The output set for Soybean is SoybeanResultsJul11. The Cassava result set is large, so I broke it into three compressed folders, CassavaResultsJul12_A, _B, _C. _B is the largest, and only contains the results overwritten on the original X-Ray images. Unless your connection to Zenodo is extremely fast, it will be faster to compute the result than to download it.</p> <p> </p> <p> </p><p> </p><p> </p> <p></p> <p></p>
Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD
<p><strong>Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD</strong></p> <p>For a detailed description of this dataset, following the <em>datasheet for the datasets</em> recommendation proposed by <a href="https://arxiv.org/abs/1803.09010">Gebru et al.</a>, check the <strong>README.md</strong> file.</p> <p><strong>Motivation for Dataset Creation</strong></p> <p><em>Why was the dataset created?</em></p> <p>Embrapa WGISD (<em>Wine Grape Instance Segmentation Dataset</em>) was created to provide images and annotation to study <em>object detection and instance segmentation</em> for image-based monitoring and field robotics in viticulture. It provides instances from five different grape varieties taken on field. These instances shows variance in grape pose,<br> illumination and focus, including genetic and phenological variations such as shape, color and compactness.</p> <p><em>What (other) tasks could the dataset be used for?</em></p> <p>Possible uses include relaxations of the instance segmentation problem: classification (Is a grape in the image?), semantic segmentation (What are the "grape pixels" in the image?), and object detection (Where are the grapes in the image?). The WGISD can also be used in grape variety identification.</p> <p> </p>
Representative Sample Dataset for Resolution-Agnostic Tissue Segmentation in Whole-Slide Histopathology Images
<p>This is a representative sample from the dataset that was used to develop resolution-agnostic convolutional neural networks for tissue segmentation1 in whole-slide histopathology images.</p> <p>The dataset is composed of two parts: <strong>development set</strong> and <strong>dissimilar set</strong>.</p> <p>Sample images from the development set:</p> <ul> <li>breast_hne_00.tif</li> <li>breast_lymph_node_hne_00.tif</li> <li>tongue_ae1ae3_00.tif</li> <li>tongue_hne_00.tif</li> <li>tongue_ki67_00.tif</li> </ul> <p>Sample images from the dissimilar set:</p> <ul> <li>brain_alcianblue_00.tif</li> <li>cornea_grocott_00.tif</li> <li>kidney_cab_00.tif</li> <li>skin_perls_00.tif</li> <li>uterus_vonkossa_00.tif</li> </ul>
Development and validation of statistical shape models of the primary functional bone segments of the foot.
<p>This dataset comprises manually segmented three-dimensional point clouds (.STL) of magnetic resonance images of the primary functional segments of the foot - first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus. These data were used to create statistical shape models of the foot bones, utilising the GIAS2 toolbox (https://pypi.org/project/gias2/).</p>
CLDF dataset derived from Walworth's "Polynesian Segmented Data" from 2018
<p>Cite the source of the dataset as:</p> <blockquote> <p>Walworth, Mary. (2018). Polynesian Segmented Data (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1689909</p> </blockquote>
SFS-A68-16: A dataset for the segmentation of space functions in apartment buildings
<p> </p> <p>SFS-A68-16: "A dataset for the segmentation of space functions in apartment buildings"</p> <p>Authors: "Amir Ziaee, Georg Suter, Mihael Barada, Laura Keiblinger"</p> <p>Copyright: "Design Computing Group TU Wien, 2023"</p> <p>Credits: "Design Computing Group TU Wien"</p> <p>License: "GNU GENERAL PUBLIC LICENSE Version 3"</p> <p>Version: "1.0.3"</p> <p>Maintainer: "Amir Ziaee"</p> <p>Email: "amir.ziaee@tuwien.ac.at"</p> <p>Url: <a href="https://github.com/A2Amir/SFS-A68-16">https://github.com/A2Amir/SFS-A68-16</a></p> <p>Description: "We present the <strong>SFS-A68-16</strong> dataset for space function segmentation in apartment buildings. The dataset consists of 16 multi-viewpoint space layout input and corresponding ground truth images for 68 floor plans of apartment buildings designed or built between 1952 and 2019. It addresses a limitation of the SFS-A68 dataset (version 1.0.1) we created in our previous work, which consists of single-viewpoint projection images only. Each pixel in a ground truth image of the SFS-A68-16 dataset is assigned to a space function class. Space function classes in apartment buildings that are classified by a space function segmentation network are shown below under ground truth classes. We have identified 22 space function classes for the apartment buildings in our dataset. Each element in an input image of the SFS-A68-16 dataset is colored according to a unique class color (below, under input classes). Space elements, such as doors and furnishing elements, are contextual features in input images that may help determine the function of a space. To measure whether excluding space elements in input images affects the accuracy of a space function segmentation network, we create a new dataset, <strong>SFS-A68-16-SEE</strong>, where space elements are excluded in input images of the SFS-A68-16-SEE dataset. The defined class hierarchy of the dataset with the unique RGB color code of each class can be seen below."</p> <p> </p> <p><strong>Input classes</strong></p> <p>[Root]</p> <p>├──[Space]</p> <p>│ ├── (102, 102, 122)[InternalSpace]</p> <p>│ └── (161, 162, 155)[ExternalSpace]</p> <p>└──[SpaceElement]</p> <p> ├── [SpaceContainedElement]</p> <p> │ ├── [CirculationElement]</p> <p> │ │ ├── (230, 184, 175)[FlightOfStairs]</p> <p> │ │ └── (107, 74, 101)[Landing]</p> <p> │ ├── [FurnishingElement]</p> <p> │ │ ├── (0, 191, 255)[KitchenElement]</p> <p> │ │ └── (70, 130, 180)[SanitaryElement]</p> <p> │ └── [EquipmentElement]</p> <p> │ └── [HomeAppliance]</p> <p> │ └── (159, 140, 81)[TextileCareAppliance]</p> <p> └── [SpaceEnclosingElement]</p> <p> ├── (109, 189, 110)[Opening]</p> <p> ├── (0, 250, 154)[Partition]</p> <p> ├── (255, 215, 0)[Window]</p> <p> └── [Door]</p> <p> ├── (200, 255, 0)[InternalDoor]</p> <p> ├── (72, 112, 39)[UnitDoor]</p> <p> ├── (187, 244, 154)[ElevatorDoor]</p> <p> ├── (47, 79, 79)[BalconyDoor]</p> <p> └── (195, 210, 192)[SideEntrance]</p> <p> </p> <p><strong>Ground truth classes</strong></p> <p>[Space]</p> <p>├── [ResidentialSpace]</p> <p>│ ├── [CommunalSpace]</p> <p>│ │ ├── (255, 218, 185)[DiningRoom]</p> <p>│ │ ├── (166, 206, 227)[FamilyRoom]</p> <p>│ │ └── (255, 0, 0)[LivingRoom]</p> <p>│ └── [PrivateSpace]</p> <p>│ ├── (0, 255, 0)[Bedroom]</p> <p>│ │ ├── (0, 128, 128)[MasterBedroom]</p> <p>│ │ └── (0, 128, 255)[BoxRoom]</p> <p>│ └── (160, 82, 45)[HomeOffice]</p> <p>├── [ServiceSpace]</p> <p>│ ├── (255, 192, 203)[Shaft]</p> <p>│ ├── (245, 245, 220)[StorageRoom]</p> <p>│ │ └── (0, 206, 209)[WalkInCloset]</p> <p>│ └── [SanitarySpace]</p> <p>│ ├── (128, 0, 0)[Bathroom]</p> <p>│ ├── (75, 0, 130)[Toilet]</p> <p>│ ├── (255, 255, 0)[Kitchen]</p> <p>│ └── (0, 128, 0)[LaundryRoom]</p> <p>├── [CirculationSpace]</p> <p>│ ├── [VerticalCirculationSpace]</p> <p>│ │ ├── (0, 0, 128)[Elevator]</p> <p>│ │ └── (0, 0, 255)[Stairway]</p> <p>│ └── [HorizontalCirculationSpace]</p> <p>│ ├── (255, 0, 255)[Entrance]</p> <p>│ └── (255, 100, 0)[Hallway]</p> <p>│ ├── (255, 165, 0)[MainHallway]</p> <p>│ └── (0, 255, 255)[InternalHallway]</p> <p>└── [ExternalSpace]</p> <p> ├── (128, 128, 0)[AccessBalcony]</p> <p> └── (225, 138, 96)[Loggia]</p>
Blaze Fire Classification – Segmentation Dataset
<p>The dataset is destined to be used for wildfire image classification and burnt area segmentation tasks for Unmanned Aerial Vehicles. It is comprised of 5,408 frames of aerial views taken from 56 videos and 2 public datasets. From the D-Fire public dataset, 829 photographs were used; and from the Burned Area UAV public dataset 34 images were used. For the classification task, there are 5 classes (‘Burnt’, ‘Half-Burnt’, ’Non-Burnt’, ‘Fire’, ‘Smoke’). As for the segmentation task, 404 segmentation masks on a subset have been created, which assign to each pixel of the image the class ‘burnt’ or the class ‘non-burnt’.</p> <p>Details on acquiring the dataset can be found <strong><a href="https://aiia.csd.auth.gr/blaze-fire-classification-segmentation-dataset/" target="_blank" rel="noopener">here</a></strong>. </p> <p> </p>
Generated Metastatic CT 3D Femurs with lesions segmentation 1/3
<p>This file regroups the 5675 synthetic femurs used and described in the paper: "Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models" Saillard et al. </p> <p><strong>Three repositories are needed </strong>to form the full archive. When all are downloaded, they can be deflated with the following bash command : </p> <pre><code>cat Generated_3DCT_Metastatic_Femurs.tar.gz* | tar faxv -</code></pre> <p> </p> <p>This is the first part (1/3)</p> <p>part 2/3: <a title="Opens in new tab" href="https://doi.org/10.5281/zenodo.13824177" target="_blank" rel="noopener"> 10.5281/zenodo.13824177 </a></p> <p>part 3/3: <a title="Opens in new tab" href="https://doi.org/10.5281/zenodo.13824179" target="_blank" rel="noopener"> 10.5281/zenodo.13824179 </a></p> <p> </p> <p>Generated CT Scans are organized in two directories: one for files generated with DDPM this one) and one without DDPM.</p> <p>├── Generated_3DCT_Metastatic_Femurs_with_DDPM <br>│ ├── img<br>│ ├── lbl<br>│ └── msk</p> <p>└── Generated_3DCT_Metastatic_Femurs_NO_DDPM<br> ├── img<br> ├── lbl<br> └── msk</p> <p>The content of each directory is as follows: </p> <ul> <li>img : the generated 3D CT scan (nii.gz format) from the 26 healthy femurs</li> <li>lbl : the lesions segmentation (nii.gz format) of the generated 3D CT scan</li> <li>msk : the segmentations of the 26 healthy femurs (nii.gz)</li> </ul> <p>A given filename corresponds to in img and lbl directories :</p> <ul> <li>./lbl/MEK03les0MEK14.nii.gz is the lesions segmentation of ./img/MEK03les0MEK14.nii.gz.</li> <li>./lbl/MEK03les197MEK28.nii.gz is the lesions segmentation of ./img/MEK03les197MEK28.nii.gz</li> </ul> <p>But in msk, the corresponding femur mask for these examples is ./msk/MEK03.nii.gz</p> <p> </p>
FeM dataset – An iron ore labeled images dataset for segmentation training and testing
<p>This dataset is composed of 81 pairs of correlated images. Each pair contains one image of an iron ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).</p> <p>The sample came from an itabiritic iron ore concentrate from Quadrilátero Ferrífero (Brazil) mainly composed of hematite and quartz, with little magnetite and goethite. It was classified by size and concentrated with a dense liquid. Then, the fraction -149+105 μm with density greater than 3.2 was cold mounted with epoxy resin and subsequently ground and polished.</p> <p>Correlative microscopy was employed for image acquisition. Thus, 81 fields were imaged on a reflected light microscope with a 10× (NA 0.20) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 999×756 pixels with a resolution of 1.05 µm/pixel. Finally, the images from SEM were thresholded to generate the reference images.</p> <p>Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).</p> <p>This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model that reached mean values of 91.43% and 93.13% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.</p> <p>For further questions and suggestions, please do not hesitate to contact us.</p> <p> </p> <p><strong>Contact email</strong>: ogomes@gmail.com</p> <p> </p> <p>If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5014700</p> <p> </p> <p>Please also cite this paper, which provides additional details about the dataset:</p> <p>Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.</p> <p> </p>
Semantic Segmentation Vineyard Rows
<p>Test dataset for semantic segmentation.<br> The datasets includes 500 RGB - images with the relative single-channel binary masks.</p> <p>Images are taken from the vineyards in Grugliasco - Turin - Piedmont Region -Italy</p> <p> </p> <p><strong>For more info please check out our work <a href="https://arxiv.org/abs/2107.00700">here</a></strong></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.