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240 results for “Tissue imaging”
Mueller matrix imaging combining optical parameters of mice non-melanoma skin cancer tissue
<p>The dataset consists of the Mueller matrix elements and optical parameters acquired from the backscattered light using a CCD camera and Mueller matrix imaging technique.</p><p>This dataset contains 90 samples including 20 feature vectors for SCC, 33 feature vectors for normal and 37 feature vectors for papilloma.</p>
Infrared Chemical Image of a Breast Cancer Tissue Microarray
<p>This data set relates to an open access paper published in Analyst <em>Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets</em> by Jiayi Tang, Alex Henderson* and Peter Gardner. <a href="https://doi.org/10.1039/D0AN02155E"> https://doi.org/10.1039/D0AN02155E</a></p> <p>The files in this archive are mid-infrared spectroscopy chemical images of a breast cancer tissue microarray. The tissue microarray is BR20832 from Biomax. <a href="http://www.biomax.us/tissue-arrays/Breast/BR20832">http://www.biomax.us/tissue-arrays/Breast/BR20832</a></p> <p>Processed versions of these data in MATLAB file format can be found in another Zenodo archive at <a href="https://doi.org/10.5281/zenodo.4730312">https://doi.org/10.5281/zenodo.4730312</a></p> <p>This processed data refers to a paper published in Analyst</p>
Synthetic spherical tissue images
<p>A time-lapse sequence of synthetic cell membrane images along with their segmentations. Cell-lineages associating cell labels from consecutive time points are also provided.</p> <p> </p> <p><strong>File information:</strong></p> <p>Image files are in the .inr.gz format, which can be read for instance using the <strong>timagetk</strong> (<a href="https://gitlab.inria.fr/mosaic/timagetk">https://gitlab.inria.fr/mosaic/timagetk</a>) Python library. </p>
Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes
<p>Raw multichannel and/or Z-stack source data from time series images in TIF format to accompany publication of:</p> <p><strong>Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes</strong></p> <p>Jina Ko<sup>1</sup>, Martin Wilkovitsch<sup>2</sup>, Juhyun Oh<sup>1</sup>, Rainer Kohler<sup>1</sup>, Evangelia Bolli<sup>1,3</sup>, Mikael J. Pittet<sup>1,3,4,5</sup>, Claudio Vinegoni<sup>1</sup>, David B. Sykes<sup>6,7</sup>, Hannes Mikula<sup>2</sup>, Ralph Weissleder<sup>1,8</sup>*, Jonathan C. T. Carlson<sup>1,7</sup>*</p> <p><sup>1 </sup>Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA 02114 </p> <p><sup>2</sup> Institute of Applied Synthetic Chemistry, TU Wien, 1060 Vienna, Austria </p> <p><sup>3</sup> Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland</p> <p><sup>4</sup> Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland</p> <p><sup>5</sup> AGORA Cancer Center, Lausanne, Switzerland</p> <p><sup>6</sup> Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA</p> <p><sup>7 </sup>Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA</p> <p><sup>8 </sup>Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115</p>
Correlative microscopy of mice cerebellar Purkinje cells from 20x confocal tissue imaging to super-resolution 93x 3D STED of dendritic spines
<p>This Dataset concerns the paper entitled "<em>From tissues to segmentation: a modular framework for multi-scale neuron isolation</em>" by Cauzzo et al. <strong>Nature Comm (2024).</strong></p> <p>S.Cauzzo<sup>$</sup>, E. Bruno, D. Boulet, P. Nazac, M. Basile, A. L. Callara, F. Tozzi, A. Ahluwalia, C. Magliaro, L. Danglot<sup>$</sup><sup>*</sup>, N. Vanello<sup>$</sup><sup>*</sup> *shared senior authorship: Lydia.danglot@inserm.fr ; nicola.vanello@unipi.it</p> <p><sup>$</sup> corresponding authors : cauzzo.simone@gmail.com ; Lydia.danglot@inserm.fr ; nicola.vanello@unipi.it</p> <p> </p>
Dataset for "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?"
<p>This dataset contains images that were used in the analysis of the manuscript "Synchrotron-based phase contrast imaging of cardiovascular tissue in mice—grating interferometry or phase propagation?", that was published in Biomedical Physics and Engineering Express in 2018. Images are uploaded in .tif format. Three different synchrotron-based imaging techniques were compared on the same cardiovascular samples: grating interferometry (GI) and absorption-based phase propagation with and without phase retrieval according to Paganins method. An excel file is provided in which the nomenclature of the files is explained.</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>
Vectra Polatis image of human colorectal cancer (CRC1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Two 4 µm thick serial sections were cut from CRC1 FFPE block using a microtome. The first slide was dewaxed and rehydrated before carrying out HIER with Antigen Retrieval Reagent-Basic (R&D Systems). The tissue was then blocked and incubated with the anti-CD3 antibody (Dako, Supplementary Table 2) followed by horseradish peroxidase (HRP) conjugated anti-rabbit antibody (Dako) and stained with 3,3' diaminobenzidine (DAB) substrate (Abcam) and haematoxylin. Areas with CD3<sup>+</sup> infiltration in the proximity of the tumour invasive margin were identified by a clinical pathologist (M. R-J.)</p> <p>The second slide was stained with a panel of six antibodies (CD8, PD1, Ki67, PDL1, CD68, GzB, Supplementary Table 2), Opal fluorophores and 4’,6-diamidino-2-phenylindole (DAPI) on a Ventana Discovery Ultra automated staining platform (Roche). Expected expression and cellular localisation of each marker as well as fluorophore brightness were used to minimise fluorescence spillage upon antibody-Opal pairing. Following a one-hour incubation at a 60°C, the slide was subjected to an automated staining protocol on an autostainer. The protocol involved deparaffinisation (EZ-Prep solution, Roche), HIER (DISC. CC1 solution, Roche) and seven sequential rounds of: one hour incubation with the primary antibody, 12 minutes incubation with the HRP-conjugated secondary antibody (DISC. Omnimap anti-Ms HRP RUO or DISC. Omnimap anti-Rb HRP RUO, Roche) and 16 minute incubation with the Opal reactive fluorophore (Akoya Biosciences). For the last round of staining, the slide was incubated with Opal TSA-DIG reagent (Akoya Biosciences) for 12 minutes followed by Opal 780 reactive fluorophore for our hour (Akoya Biosciences). A denaturation step (100°C for 8 minutes) was introduced between each staining round in order to remove the primary and secondary antibodies from the previous cycle without disrupting the fluorescent signal. The slide was counterstained with DAPI (Akoya Biosciences) and coverslipped using ProLong Gold antifade mounting media (Thermo Fisher Scientific). The Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences) was used to scan the labelled slide. Six fields of view, within the area selected by the pathologist, were scanned at 20x and 40x magnification using appropriate exposure times and loaded into inForm{Kramer, 2018 #23} for spectral unmixing and autofluorescence isolation using the spectral libraries. After spectral unmixing and merging of six 20x fields of view for a total of >5mm<sup>2</sup> ROI (Table 2), one single-tiff image was extracted for each marker and its intensity was rescaled from 0 to 1 with custom R scripts.</p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 2 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the second part of 14 parts of the full dataset (2/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 15ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 3 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the third part of 14 parts of the full dataset (3/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 20ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 12 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the twelfth part of 14 parts of the full dataset (12/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 30ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 5 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fifth part of 14 parts of the full dataset (5/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 30ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 4 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fourth part of 14 parts of the full dataset (4/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 25ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each of simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 11 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the eleventh part of 14 parts of the full dataset (11/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 25ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 10 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the tenth part of 14 parts of the full dataset (10/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 20ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 14 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fourteenth and final part of 14 parts of the full dataset (14/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 40ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 6 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the sixth part of 14 parts of the full dataset (6/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 35ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 8 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the eighth part of 14 parts of the full dataset (8/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 10ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 13 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the thirteenth part of 14 parts of the full dataset (13/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 35ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 7 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the seventh part of 14 parts of the full dataset (7/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 40ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></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.