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4,612 results for “Labels”
Weakly-Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT
<p>Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing \emph{strong supervision} by directly presenting examples from the different classes, e.g. positive and negative samples. As a result, the adequate training of these models demands the curation of large datasets with high-quality labels. This constitutes a major obstacle for the development of DL models in radiology---in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)---where labels must come from manual annotations by expert radiologists at the image or slice-level (as opposed to the examination level, such as could be obtained using natural language processing of radiology reports). <br> This work studies the question of what kind of labels should be collected for the problem of intracranial hemorrhage detection in brain CT. We investigate whether image-level annotations should be preferred to examination-level ones. By framing this task as a Multiple Instance Learning (MIL) problem, and employing modern attention-based DL architectures, we analyze the degree to which different levels of supervision improve the detection performance. We find that strong supervision (learning with local image-level annotations) and weak supervision (learning with only global examination-level labels) achieve comparable performance in both examination- and image-level hemorrhage detection, as well as in hemorrhage localization at the pixel-level (explainability). Furthermore, we study this behavior as a function of the number of labels available during training. Our results suggest that local labels may not be necessary at all, drastically reducing the time and cost involved in collecting and curating datasets.</p>
Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)
<p><em><strong>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)</strong></em></p> <p>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)</p> <p><strong>Description</strong></p> <p>3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only</p> <p>These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Two data sources have been combined</p> <p><strong>Dataset 1</strong></p> <p>* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571<br> * Labels have been reclassified from 4 classes to 2 classes.<br> * Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.<br> * These images and labels have been made using the Doodleverse software package, Doodler*.</p> <p><strong>Dataset 2</strong></p> <ul> <li>3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******</li> <li>A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water</li> </ul> <p><strong>File descriptions</strong></p> <ul> <li> classes.txt, a file containing the class names</li> <li> images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li> labels.zip, a zipped folder containing the 1-band label images</li> <li> nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images</li> <li> swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images</li> <li> overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)</li> <li> resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li> resized_labels.zip, label images resized to 512x512x1 pixels</li> <li> resized_nir.zip, NIR images resized to 512x512x1 pixels</li> <li> resized_swir.zip, SWIR images resized to 512x512x1 pixels</li> </ul> <p>References</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.</p> <p>**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information</p> <p>****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p>*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/</p> <p>******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.</p>
Labelled data for P/S separation with CNN
<p>This is the part of training labelled Dataset-B for the publication "P/S separation of multi-component seismic data at land surface based on deep learning".</p> <p>There are 500-shot data labels:</p> <p>Train/, Val/ & Test/ are the separated file for Training, Validation & Testing</p> <p>The labelled data size are nx*nz=1001*3001 using a IEEE float format</p> <p>One can use the Seisic Unix command to plot for QC: ximage < shotx_mod_1.dat n1=3001 perc=99 & </p> <p> </p> <p>In each directory, the files are named as follows: </p> <p>The horizontal component:</p> <p> shotx_mod_*.dat </p> <p>The vertical component:</p> <p> shotz_mod_*.dat </p> <p>The P-wave label :</p> <p> shotp_*.dat </p> <p>The S-wave label :</p> <p> shots_*.dat </p>
Figure 1. A, molecular phylogeny from MrBayes. Node labels are posterior probability support values. B in A new lineage of deep-reef gobies from the Caribbean, including two new species and one new genus (Teleostei: Gobiidae: Gobiosomatini)
Figure 1. A, molecular phylogeny from MrBayes. Node labels are posterior probability support values. B, time-calibrated tree from BEAST. Node bars are 95% highest posterior densities for age estimates. Coloured taxa are new species. Colored circles in B refer to the placement of fossil calibration points 1 through 4.
Phototoxicity and Cell Passage Affect Intracellular Reactive Oxygen Species Levels and Sensitivity Towards Non-Thermal Plasma Treatment in Fluorescently-Labeled Cancer Cells
<p>Raw data for the manuscript "Phototoxicity and Cell Passage Affect Intracellular Reactive Oxygen Species Levels and Sensitivity Towards Non-Thermal Plasma Treatment in Fluorescently-Labeled Cancer Cells".</p>
FIGURE 12. Platynaspis wittmeri (Bielawski): a, b. adult, dorsal view (BMNH); c–e. holotype: c. holotype, dorsal view; d. label data; e. abdomen and genitalia (Image credit for c–e: Matthias Borer, NHMB). in A review of Platynaspini (Coleoptera: Coccinellidae) of the Indian subcontinent, including description of a new genus from north-eastern India and Bangladesh
FIGURE 12. Platynaspis wittmeri (Bielawski): a, b. adult, dorsal view (BMNH); c–e. holotype: c. holotype, dorsal view; d. label data; e. abdomen and genitalia (Image credit for c–e: Matthias Borer, NHMB).
Figure 2. Skull with measurements labelled. A, dorsal view. B, ventral view. C, lateral view. D in Cranial morphology of the California vole (Microtus californicus, Cricetidae) in a contact zone
Figure 2. Skull with measurements labelled. A, dorsal view. B, ventral view. C, lateral view. D, mandible. HCB, height of cranium at bullae; IOC, interorbital constriction; IFL, length of incisive foramen; IPL, interparietal length; IPW, interparietal width; MAL, upper molar alveolus; MAN, length of mandible; MAW, mastoid width; NAL, nasal length; ONL, occipital-nasal length; PAL, shelf of bony palate; ZYB, zygomatic breadth. Details of each measurement are provided in the text.
MAESTRO Real - Multi-Annotator Estimated Strong Labels; Evaluation dataset
<p>Evaluation dataset for the DCASE challenge 2023 task 4 B - Sound Event Detection with Soft Labels.</p> <p>The evaluation dataset contains 26 real-life audio files from 5 different acoustic scenes.</p>
Label-free data mining of scientific literature by unsupervised syntactic distance analysis
<p>1. OER.zip: literature resources, mined data and manual annotation of OER</p> <p>2. OLED.zip: literature resources and mined data of OLED</p> <p>3. PVK_ligand.zip: literature resources, mined data and manual annotation of perovskite ligand</p> <p>4. PVK_solvent.zip: literature resources, mined data and manual annotation of perovskite solvent</p> <p>5. syn_vec.part01.rar - syn_vec.part01.rar: word vector model trained by synthesis paragraphs in USPTO</p> <p>6. crossref_vec.part01.rar - crossref_vec.part10.rar: word vector trained by abstracts in Crossref</p>
Figure 4. ITS1 gene tree. Terminal tips have been collapsed for visualization purposes. Nodes are labelled with ultrafast bootstrap support. A in Hidden in the hills: phylogeny of the freshwater mussel genus Alasmidonta (Bivalvia: Unionidae) and description of a new species
Figure 4. ITS1 gene tree. Terminal tips have been collapsed for visualization purposes. Nodes are labelled with ultrafast bootstrap support. A full tree is available in the Supporting Information (File S1). The scale is in substitutions per site.
Uncovering the CO2 Capture Mechanism of NaNO3-Promoted MgO by 18O Isotope Labeling
<p>Raw data for the article "Uncovering the CO<sub>2</sub> Capture Mechanism of NaNO<sub>3</sub>-Promoted MgO by <sup>18</sup>O Isotope Labeling", already published in JACS Au</p> <p>All details concerning conditions and equipment for measurements can be found in the main text, supporting information of the article, and in text file contained in the folder.</p>
Datasets and Trained Models for "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"
<p>Datasets and Trained models for ICSE 2020 "Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning"</p>
Fig. 1. Slides with original labels. A, B in Redescription of Enchodelus species studied by Thorne in 1939 (Dorylaimida: Nordiidae)
Fig. 1. Slides with original labels. A, B: Enchodelus arcuatus; C: E. brevidentatus; D: E. hopedorus; E, F: E. geraldi; G, H: E. macrodorus; I, J: E. striatus.
Datasets for a data-centric image classification benchmark for noisy and ambiguous label estimation
<p>This is the official data repository of the Data-Centric Image Classification (DCIC) Benchmark. The goal of this benchmark is to measure the impact of tuning the dataset instead of the model for a variety of image classification datasets. Full details about the collection process, the structure and automatic download at</p> <p>Paper: https://arxiv.org/abs/2207.06214</p> <p>Source Code: https://github.com/Emprime/dcic</p> <p>The license information is given below as download.</p> <p><strong>Citation</strong></p> <p>Please cite as</p> <pre><code>@article{schmarje2022benchmark, author = {Schmarje, Lars and Grossmann, Vasco and Zelenka, Claudius and Dippel, Sabine and Kiko, Rainer and Oszust, Mariusz and Pastell, Matti and Stracke, Jenny and Valros, Anna and Volkmann, Nina and Koch, Reinahrd}, journal = {36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks}, title = {{Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation}}, year = {2022} }</code></pre> <p>Please see the full details about the used datasets below, which should also be cited as part of the license.</p> <pre><code>@article{schoening2020Megafauna, author = {Schoening, T and Purser, A and Langenk{\"{a}}mper, D and Suck, I and Taylor, J and Cuvelier, D and Lins, L and Simon-Lled{\'{o}}, E and Marcon, Y and Jones, D O B and Nattkemper, T and K{\"{o}}ser, K and Zurowietz, M and Greinert, J and Gomes-Pereira, J}, doi = {10.5194/bg-17-3115-2020}, journal = {Biogeosciences}, number = {12}, pages = {3115--3133}, title = {{Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison}}, volume = {17}, year = {2020} } @article{Langenkamper2020GearStudy, author = {Langenk{\"{a}}mper, Daniel and van Kevelaer, Robin and Purser, Autun and Nattkemper, Tim W}, doi = {10.3389/fmars.2020.00506}, issn = {2296-7745}, journal = {Frontiers in Marine Science}, title = {{Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification}}, volume = {7}, year = {2020} } @article{peterson2019cifar10h, author = {Peterson, Joshua and Battleday, Ruairidh and Griffiths, Thomas and Russakovsky, Olga}, doi = {10.1109/ICCV.2019.00971}, issn = {15505499}, journal = {Proceedings of the IEEE International Conference on Computer Vision}, pages = {9616--9625}, title = {{Human uncertainty makes classification more robust}}, volume = {2019-Octob}, year = {2019} } @article{schmarje2019, author = {Schmarje, Lars and Zelenka, Claudius and Geisen, Ulf and Gl{\"{u}}er, Claus-C. and Koch, Reinhard}, doi = {10.1007/978-3-030-33676-9_26}, issn = {23318422}, journal = {DAGM German Conference of Pattern Regocnition}, number = {November}, pages = {374--386}, publisher = {Springer}, title = {{2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy}}, volume = {11824 LNCS}, year = {2019} } @article{schmarje2021foc, author = {Schmarje, Lars and Br{\"{u}}nger, Johannes and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Kiko, Rainer and Koch, Reinhard}, doi = {10.3390/s21196661}, issn = {1424-8220}, journal = {Sensors}, number = {19}, pages = {6661}, title = {{Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy}}, volume = {21}, year = {2021} } @article{schmarje2022dc3, author = {Schmarje, Lars and Santarossa, Monty and Schr{\"{o}}der, Simon-Martin and Zelenka, Claudius and Kiko, Rainer and Stracke, Jenny and Volkmann, Nina and Koch, Reinhard}, journal = {Proceedings of the European Conference on Computer Vision (ECCV)}, title = {{A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering}}, year = {2022} } @article{obuchowicz2020qualityMRI, author = {Obuchowicz, Rafal and Oszust, Mariusz and Piorkowski, Adam}, doi = {10.1186/s12880-020-00505-z}, issn = {1471-2342}, journal = {BMC Medical Imaging}, number = {1}, pages = {109}, title = {{Interobserver variability in quality assessment of magnetic resonance images}}, volume = {20}, year = {2020} } @article{stepien2021cnnQuality, author = {St{\c{e}}pie{\'{n}}, Igor and Obuchowicz, Rafa{\l} and Pi{\'{o}}rkowski, Adam and Oszust, Mariusz}, doi = {10.3390/s21041043}, issn = {1424-8220}, journal = {Sensors}, number = {4}, title = {{Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment}}, volume = {21}, year = {2021} } @article{volkmann2021turkeys, author = {Volkmann, Nina and Br{\"{u}}nger, Johannes and Stracke, Jenny and Zelenka, Claudius and Koch, Reinhard and Kemper, Nicole and Spindler, Birgit}, doi = {10.3390/ani11092655}, journal = {Animals 2021}, pages = {1--13}, title = {{Learn to train: Improving training data for a neural network to detect pecking injuries in turkeys}}, volume = {11}, year = {2021} } @article{volkmann2022keypoint, author = {Volkmann, Nina and Zelenka, Claudius and Devaraju, Archana Malavalli and Br{\"{u}}nger, Johannes and Stracke, Jenny and Spindler, Birgit and Kemper, Nicole and Koch, Reinhard}, doi = {10.3390/s22145188}, issn = {1424-8220}, journal = {Sensors}, number = {14}, pages = {5188}, title = {{Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks}}, volume = {22}, year = {2022} }</code></pre> <p>Addition: This repository also contains the original data from the paper "Annotating Ambiguous Images" (https://arxiv.org/abs/2306.12189). The data is created based on the original datasets and license from https://osf.io/t98fz/ and https://osf.io/nqjyw/</p>
Deep and fast label-free Dynamic Organellar Mapping - Imaging data
<p>Imaging data from the article "Deep and fast label-free Dynamic Organellar Mapping", published in Nature Communications by Schessner et al., from Fig. 6, 7 and Supp. Fig. 6c.</p> <p>Widefield images were captured on a Leica DMi8 inverted microscope equipped with an iTK LMT200 motorised stage, a 63x/1.47 oil objective (HC PL APO 63x/1.47 OIL) and a Leica DFC9000 GTC Camera, and controlled with LAS X (Leica Application Software X).</p> <p>Fig6_GLG1_TGOLN2_GALNT2_SuppFig6c_LC3B: Widefield imaging of wild-type HeLa cells cultured for 1h in either: 1) full growth medium (Control); 2) EBSS to starve the cells (Starve); 3) full growth medium plus 100 nM BafA (Control + BafA); or 4) EBSS plus 100 nM BafA (Starve + BafA). Cells were labelled with anti-GALNT2 (Alexa Fluor 488), in combination with either anti-GLG1 (Alexa Fluor 568) or anti-TGOLN2 (Alexa Fluor 680), as shown in Fig. 6, or were labelled with anti-LC3B (Alexa Fluor 488), as shown in Supp. Fig. 6c. In all images, cells were stained with DAPI to label nuclei.</p> <p>Fig7_GALNT2_GLG1_TM9SF2_TGOLN2_GOLIM4_SDF4: Widefield imaging of HeLa cells left untreated in full growth medium (0h) or cultured in the presence of 100 nM BafA for 0.5, 1, 2, 4, 6 or 8 hours, before fixation. Cells were labelled with anti-GALNT2 (Alexa Fluor 647) in combination with either anti-GLG1, anti-TM9SF2, anti-TGOLN2, anti-GOLIM4 or anti-SDF4 (Alexa Fluor 555). In all images, cells were stained with DAPI and phalloidin-488 to label nuclei and cytoplasm, respectively.</p>
Fig. 6 in Characterization of the PRODUCTION of ANTHOCYANIN PIGMENT 1 Arabidopsis dominant mutant using DLEMMA dual isotope labeling approach
Fig. 6. Differential analysis of phenylpropanoid pathway metabolites between WT and pap1-D mutant Arabidopsis leaves. (A) Representation of differential metabolites found between WT and pap1-D in leaves. The data represents geometric fold change mean + standard deviation (SD) of four biological replicates. (B) The phenylpropanoid biosynthetic pathway and metabolites detected in the course of this study. The enzymes highlighted in red are those regulated by the PAP1 transcription factor according to the previous report from Tohge et al. (2005). Dash line denotes multiple enzymatic reactions in the pathway. PAL, phenylalanine ammonia-lyase; C4H, cinnamate 4-hydroxylase; 4CL, 4-coumarate-CoA ligase; CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; FLS, flavonol synthase; FGT, flavonol glycosyltransferase; DFR, dihydroflavonol reductase; ANS, anthocyanidin synthase; AGT, anthocyani(di)n glycosyltransferase; AAT, anthocyanin acyltransferase. GST, glutathione S-transferase. The pathway was modified from Tohge et al. (2005).
Fig. 5 in Characterization of the PRODUCTION of ANTHOCYANIN PIGMENT 1 Arabidopsis dominant mutant using DLEMMA dual isotope labeling approach
Fig. 5. Linear correlation between labeling efficiency and metabolite location in the phenylpropanoid pathway. The number next to each point corresponds to metabolite location shown in Table 1. 'L' stands for location, the number after 'L' indicates its location in the phenylpropanoid pathway. The number in the bracket is used to distinguish metabolites that are at the same pathway location; LE: labeling efficiency. The color scale represents different percentage of labeling efficiency.
Fig. 2. A in Characterization of the PRODUCTION of ANTHOCYANIN PIGMENT 1 Arabidopsis dominant mutant using DLEMMA dual isotope labeling approach
Fig. 2. A representative example of metabolite identification using DLEMMA. (A) An isotopologue cluster was identified. All information necessary for metabolite identification such as retention time (RT), m/z values and number of labeled atoms, was depicted in a table (B) The raw MS data was first inspected (i.e. RT and m/z values) to validate the results obtained by the Miso software. (C) Elemental composition was then determined using the Masslynx software based on accurate unlabeled m/z and natural isotope patterns. Next, elemental compositions C15H16O9 was searched in SciFinder, and 108 possible candidates were found. The structural information obtained from the labeling patterns was then used to narrow down the number of candidates. Combined with MS/MS information, this metabolite was identified as sinapoyl malate.
Fig. 1 in Characterization of the PRODUCTION of ANTHOCYANIN PIGMENT 1 Arabidopsis dominant mutant using DLEMMA dual isotope labeling approach
Fig. 1. Overview of the DLEMMA workflow employed in this study. (A) Three differently labeled forms of phenylalanine (Phe) were used as feeding precursors, i.e. unlabeled, Label I (Phe-13C), and Label II (Phe-13C2H) Phe. (B) The two Arabidopsis genotype groups, WT and pap1-D, were fed with three forms of labeling 6 6 5 precursors for 24h. (C) Following feeding, the six treatments were combined (i.e. combinations A to F), in which four combinations were designed for metabolite identification and another two for semi-quantitative differential metabolite analysis. The extracts were analyzed by high resolution LC-MS in both positive and negative ion modes. (D) The LC-MS raw data was preprocessed with the R package XCMS, and next the R package Miso was used to detect and extract all Phe-derived mass features. (E) Phe-derived metabolites were identified based on retention time, m/z, MS/MS spectra, and dual-labeling patterns obtained from LC-MS analysis. (F) A combined sample matrices and label-swap approach was used to semi-quantitatively compare phenylpropanoids content between the WT and pap1- D genotypes.
Fig. 3. A in Characterization of the PRODUCTION of ANTHOCYANIN PIGMENT 1 Arabidopsis dominant mutant using DLEMMA dual isotope labeling approach
Fig. 3. A representative example demonstrating metabolite structure elucidation by DLEMMA in cases where multiple different labeling patterns are detected. An isotopologue cluster was detected with multiple different labeling patterns being observed, i.e. m/z+6, m/z+9, m/z+10, m/z+12, m/z+15, m/z+16 and m/z+19. Two possible elemental compositions were found for this metabolite. SciFinder database search revealed that only one structure matched all the 7 observed labeling patterns and this metabolite identified as Cyanidin 3-O-[2-O-(xylosyl)-6-O-(p-coumaroyl) glucoside] 5-O-malonylglucoside.
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.