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328 results for “segmentation dataset”
Dataset supporting the manuscript dedicated to lignin precursor incorporation analysis, bioorthogonal labeling, parametric and AI based segmentation.
<p>This dataset aims to test the algorithms presented in an article submitted by the authors and untitled:</p> <p><strong>The combination of chemical reporter-, segmentation- and ratiometric-methods enables high-quality mapping of lignification dynamics in plant cell walls</strong></p> <p><strong>Are available:</strong></p> <p>-the algorithm with graphical user interface for imageJ and its installation procedure ("Cell_Wall_Segmentation " and "Tutorial Cell_Wall_Segmentation")</p> <p>-a folder comprising a classifier and a data set compatible with the machine learning part of the algorithm "data and classifier for weka"</p> <p>- representative images adapted for testing “representative images”</p> <p>- the macro corresponding to the parametric segmentation procedure (see imageJ documentation for installation instructions) “parametric_segmentation”</p>
Dataset of application to "Fondos de Cultura" in Chile for 2017. Segmented by production phase.
<p>Dataset of application to "Fondos de Cultura" in Chile for 2017. Segmented by production phase. </p>
DepthMars Dataset for Semantic Segmentation of the Martian Surface from Rover Images
<p>This dataset is resulted from a research article, "DepthFormer: Depth-Enhanced Transformer Network for Semantic Segmentation of the Martian Surface from Rover Images", which includes surface images on Mars collected by the Zhurong rover along its traverse, depth images generated from stereo images, and corresponding manually labeled images.</p>
Materials in Vessels Dataset, Annotated images of materials in transparent vessels for semantic segmentation
<p> Data set of materials in vessels<br> The handling of materials in glassware vessels is the main task in chemistry laboratory research as well as a large number of other activities. Visual recognition of the physical phase of the<br> materials is essential for many methods ranging from a simple task such as fill-level evaluation to the<br> identification of more complex properties such as solvation, precipitation, crystallization and phase<br> separation. To help train neural nets for this task, a new data set was created. The data set contains a<br> thousand images of materials, in different phases and involved in different chemical processes, in a<br> laboratory setting. Each pixel in each image is labeled according to several layers of classification, as<br> given below:</p> <p>a. Vessel/Background: For each pixel assign value of one if it is part of the vessel and zero otherwise.<br> This annotation was used as the ROI map for the valve filter method.</p> <p>b. Filled/Empty: This is similar to the above, but also distinguishes between the filled and empty<br> regions of the vessel. For each pixel, one of the following three values is assigned:0 (background); 1<br> (empty vessel); or 2 (filled vessel).</p> <p>c. Phase type: This is similar to the above but distinguishes between liquid and solid regions of the<br> filled vessel. For each pixel, one of the following four values: 0 (background); 1 (empty vessel); 2<br> (liquid); or 3 (solid).</p> <p>d. Fine-grained physical phase type: This is similar to the above but distinguishes between specific<br> classes of physical phase. For each pixel, one of 15 values is assigned: 1 (background); 2 (empty<br> vessel); 3 (liquid); 4 (liquid phase two, in the case where more than one phase of the liquid appears in<br> the vessel); 5 (suspension); 6 (emulsion); 7 (foam); 8 (solid); 9 (gel); 10 (powder); 11 (granular); 12<br> (bulk); 13 (solid-liquid mixture); 14 (solid phase two, in the case where more than one phase of solid<br> exists in the vessel): and 15 (vapor).<br> The annotations are given as images of the size of the original image, where the pixel value is the<br> class number. The annotation of the vessel region (a) is used in the ROI input for the valve filter net .</p> <p>4.1. Validation/testing set<br> The data set is divided into training and testing sets. The testing set is itself divided into two subsets;<br> one contains images extracted from the same YouTube channels as the training set, and therefore was<br> taken under similar conditions as the training images. The second subset contains images extracted<br> from YouTube channels not included in the training set, and hence contains images taken under<br> different conditions from those used to train the net.</p> <p>4.2. Creating the data set<br> The creation of a large number of images with a variety of chemical processes and settings could have<br> been a daunting task. Luckily, several YouTube channels dedicated to chemical experiments exist<br> which offer high-quality footage of chemistry experiments. Thanks to these channels, including<br> NurdRage, NileRed, ChemPlayer, it was possible to collect a large number of high-quality images in a<br> short time. Pixel-wise annotation of these images was another challenging task, and was performed by<br> Alexandra Emanuel and Mor Bismuth.</p> <p>For more details see: <a href="https://arxiv.org/pdf/1708.08711.pdf">Setting attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels</a></p> <p>This dataset was first published in 2017.8</p> <p>For newer and Bigger datasets see</p> <p>https://zenodo.org/record/4736111#.YbG-RrtyZH4</p> <p>https://zenodo.org/record/3697452#.YbG-TLtyZH4</p> <p> </p>
Procedurally generated simulation/animation of liquids in transparent containers with depth map/ segmentation map (part of transproteus dataset)
<p>Procedurally generated simulation/animation of liquids in transparent containers with depth map/ segmentation map (part of transproteus dataset)</p> <p>https://arxiv.org/ftp/arxiv/papers/2109/2109.07577.pdf</p>
Segmentation datasets used in https://arxiv.org/abs/2112.12955
<p>The segmentation datasets (both training and test sets) used in https://arxiv.org/abs/2112.12955 . The training set is in the folder SKIN\SKIN_ECUTr.rar , it already contains data augmented images (augmented data have size 352*352, instead the original images have the original size), we have used this dataset for training all the networks (also ELoss101). </p> <p>For the polyp dataset, only the training set without data augmentation is available in this page. Notice that we use training images of size 352*352 before to apply data augmentation and to feed neural networks (even with Eloss101).</p> <p>The test sets for the polyp segmentation problem are available here: https://zenodo.org/record/5579392#.YdzC1f7MKUk</p> <p>The training sets, coupled with data augmentation, for the polyp segmentation problem are available here: https://zenodo.org/record/5579392#.YdzC1f7MKUk</p> <p> </p>
artificial dataset of small engine parts for object detection-segmentation
<p>This dataset contains images and masks of parts used in engine assembly. The images were generated artificially from CAD models using gazebo simulator. The dataset consists of three classes: Large Bolt, Small Bolt and Rocker Arm. Annotations are represented as mask images with the same name as corresponding RGB images. 1080 images for each class, 3240 images total. Resolution of each image: 640x480 pixels. CAD models for each part are also included.</p>
RIGA+ Dataset for Unsupervised Domain Adaptation in Medical Image Segmentation
<p>Different from the previous combined multi-domain dataset for unsupervised domain adaptation (UDA) in medical image segmentation, this multi-domain fundus image dataset contains annotations made by the same group of ophthalmologists. Hence the annotator bias among different datasets can be mitigated. Therefore, this dataset can provide a relatively fair benchmark for evaluating UDA methods in fundus image segmentation.</p> <p>This dataset is based on the RIGA[1] dataset and MESSIDOR[2] dataset. We appreciate their efforts devoted by the authors of [1] and [2].</p> <p>The six duplicated cases in the RIGA dataset are filtered out according to the <a href="https://www.adcis.net/en/third-party/messidor/">Errata</a>. We also remove the duplicated cases that exist in both the RIGA dataset and the MESSIDOR dataset by hash value matching.</p> <table align="center"> <caption>Details of the RIGA+ dataset</caption> <thead> <tr> <th scope="row">Domain</th> <th scope="col">Dataset</th> <th scope="col"> <p>Labeled Samples</p> <p>(Train+Test)</p> </th> <th scope="col"> <p>Unlabeled</p> <p>Samples</p> </th> </tr> </thead> <tbody> <tr> <th scope="row">Source</th> <td>BinRushed</td> <td>195 (195+0)</td> <td>0</td> </tr> <tr> <th scope="row">Source</th> <td>Magrabia</td> <td>95 (95+0)</td> <td>0</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE1</td> <td>173 (138+35)</td> <td>227</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE2</td> <td>148 (118+30)</td> <td>238</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE3</td> <td>133 (106+27)</td> <td>252</td> </tr> </tbody> </table> <p>[1] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonics, 2018, 10579: 105790B.</p> <p>[2] Decencière E, Zhang X, Cazuguel G, et al. Feedback on a publicly distributed image database: the Messidor database[J]. Image Analysis & Stereology, 2014, 33(3): 231-234.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code>@inproceedings{hu2022domain, title={Domain Specific Convolution and High Frequency Reconstruction based Unsupervised Domain Adaptation for Medical Image Segmentation}, author={Shishuai Hu and Zehui Liao and Yong Xia}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2022}, organization={Springer} }</code></pre> <p> </p>
Dataset: Gauze detection and segmentation in minimally invasive surgery video using convolutional neural networks
<p>Dataset of the <strong>Gauze detection and segmentation in minimally invasive surgery video using convolutional neural networks</strong> article.</p> <p>Further information is available in the README file.</p>
HoVer-NeXt: A Fast Nuclei Segmentation and Classification Pipeline for Next Generation Histopathology - Datasets
<p>This repository contains training and validation data for</p> <p><strong>HoVer-NeXt: A Fast Nuclei Segmentation and Classification Pipeline for Next Generation Histopathology </strong></p> <p><strong>Accepted for Oral Presentation at MIDL2024: <a href="https://openreview.net/pdf?id=3vmB43oqIO">https://openreview.net/pdf?id=3vmB43oqIO</a> <br></strong></p> <p><strong>More information and code are available at <a href="https://github.com/digitalpathologybern/hover_next_inference" target="_blank" rel="noopener">https://github.com/digitalpathologybern/hover_next_inference</a></strong></p> <p>Modified Lizard dataset to include mitosis (lizard_mitosis.zip), mitosis dataset (mitosis_ds.zip) and a holdout eosinophil validation set (eos_eval.zip)</p> <p>mitosis_ds.zip also contains the hold-out H&E mitosis test set.</p> <p>The original lizard dataset was createdy by Simon Graham et al. and was shared under CC BY-NC-SA 4.0. The tile-based dataset can be downloaded from <a href="https://conic-challenge.grand-challenge.org/Data/">https://conic-challenge.grand-challenge.org/Data/</a> after registering for the challenge. We modify the dataset by including an additional mitosis class, however note that there are a number of mitosis which are still not (correctly annotated).</p>
IVD-SEG:Standardized Datasets for Industrial Vision Defect Segmentation
<h1> </h1> <h1>Code[<a href="https://github.com/KLIVIS/IVD-SEG">Github</a>]</h1> <h1>abstract</h1> <p>We introduce IVD-SEG, a dataset encompassing defect images from 43 different industrial products, totaling 5686 images, spanning tasks that include binary and multiclass segmentation. Within IVD-SEG, we meticulously propose 12 sub-datasets, including two newly developed datasets by our team. Serving as a large-scale standardized industrial defect image dataset, all images are unified to a 256 × 256 size, accompanied by semantic segmentation annotations. This standardization facilitates users unfamiliar with industrial product defects to utilize the dataset, allowing them to focus on exploring algorithmic performance on the IVD-SEG dataset. To our knowledge, the dataset we propose is currently the most comprehensive and voluminous compilation of industrial defect images, thereby contributing to the advancement of relevant research in industrial image analysis. Simultaneously, our dataset supports research and education in various fields, including computer vision and machine learning. We conducted benchmark tests on IVD-SEG using several baseline methods, including representative CNN and ViT networks.<br><br></p>
Train and Test Dataset for a Neural Network for the task of RSA Segmentation in the context of a Master thesis 2024
<p>The data corresponds to the work of the master thesis "3D Segmentation of Plant Roots from MRI Images for Enhanced Automated Root Tracing using Deep Neural Networks" by Daniel Weißen from the 24/07/2024</p> <p>train_data.tar<br>- contains the training and validation data used for the DNN<br> - data_gmm contains the soil noise data based on a Gaussian Mixture Model<br> - data_FS contains the soil noise data based on the Fourier Synthesis approach described in the thesis</p> <p>test_data.tar<br>- contains the test data which is manually labeled</p> <p>segmentations.tar<br>- contains the segmentations of the trained SwinUNETR-V2+ on the real test MRIs<br> - fs_model_output: contains the Fourier Synthesis trained NNs segmentation<br> - fmm_model_output: contains the GMM trained NNs segmentation<br> - threshold_output: contains the Dice optimized threshold labeling</p> <p>FS_checkpoint.ckpt, GMM_checkpoint.ckpt<br>- contains the checkpoint for the trained SwinUNETR-V2+ on the Fourier synthesis, and GMM soil noise respectively</p>
Dataset with segmentations of 75 stained cell images over P3HBV polymer films
<div> <p>We segmented 75 images of stained cells, divided in 5 folders according the thickness of the <strong>Poly-3-hydroxyvalerate</strong> films.</p> <p>P3HBV polymer films of different thicknesses were prepared by casting solution technique.</p> <p>For this, P3HBV was dissolved in chloroform to obtain homogeneous solutions with concentration of <strong>1.0, 1.5, 2.0, 2.5 and 3.0%</strong>. The films were dried at room temperature for 48 hours in a dust-free environment, allowing the chloroform to evaporate completely and resulting in solid films. Upper surfaces of the films were used for cell cultivation.</p> <p>Linear culture of mice fibroblasts NIH 3T3 was used to obtain the visual data of cell adhesion on P3HBV film samples. To assess the cytocompatibility of PHA samples, cells were seeded onto sterile polymer films samples at a density of 2 × 104𝑐𝑒𝑙𝑙𝑠/𝑐𝑚2 and cultured for 72 hours. After the incubation, the samples were washed with phosphate-buffered saline and cells were fixed with 4% paraformaldehyde solution.</p> <p>Cell membranes were permeabilized with 0.2% Triton-X, and the cytoplasm was stained with fluorescein isothiocyanate, FITC (green) (Sigma-Aldrich, USA), for 1 hour in the dark at room temperature. The nuclei were visualized using 4’,6-diamidino-2-phenylindole, DAPI (blue) (Sigma-Aldrich, USA).</p> <p>Cells were visualized using a Leica DMI8 fluorescent microscope with corresponding LAS X software.</p> <p>Per each sample, we include: A raw image (.tif) with 2 subfolders containing the segmented masks of their cells and nuclei respectively.</p> </div>
Segment and Support: a dual-purpose Deep Learning solution for Limited Angle Holographic Tomography - dataset
<p>This dataset constains training data of two types:</p> <ol> <li><strong>Synthetic volumes:<br></strong>Containing randomly generated and placed spheres of various sizes. The dataset was augmented by adding noise, rotation, holes, flipping, blurring and shifting. Data generated this way is treated as the input data. An output pair for each datapoint has been generated by inducing missing cone artifacts, by masking the 3D Fourier spectrum of each volume (to mimic an experimental scenario). </li> <li><strong>Experimental</strong> <strong>data: <br></strong>This part of the dataset contains erroneous Direct Inversion reconstructions of cells [1], organoids [2], phantoms [3-4]. The mask has been generated through a thresholding step of a GTVIC [5] algorithm.</li> </ol> <p>Each volume has the shape (128,128,128), and is of <em>float32</em> precision. They are saved as <em>.tiff </em>files, which can be easily read via the <em>tifffile</em> Python library.</p> <p>Additionally, the dataset contains:</p> <ul> <li><em>SnSNet_best_loss_model.pth</em> - <em>PyTorch </em>file containing the weights of the model, whose loss was the lowest during training. </li> <li><em>SnSNet_best_metric_model.pth </em>- <em>PyTorch </em>file containing the weights of the model, whose metrics were the highest during training. <em><br></em></li> <li><em>SnSNet_config.yml - </em>YAML file containing all config information about the model, its training and dataset. It is necessary for inference and model recreation.</li> <li><em>Inference.py </em>- Short <em>Python </em>script for quick inference. The input should be a floating point, erroneous DI reconstruction. It produces a binary mask of the object with corrected geometry and saves it into the same directory as the input file, with the prefix <em>PRED_</em>.</li> <li><em>reqs.txt </em>- text file, containing the library requirements necessary for running <em>Inference.py</em>. It is compiled, such that the script performs the inference on GPU, using CUDA 11.8 on Windows 10. If the configuration does not enable running on GPU, it will run on CPU and will be indicated in the logs.</li> <li><em>training.xlsx </em>- Excel file, containing data about the training process.</li> </ul> <p> </p> <p>[1] M. Baczewska, W. Krauze, A. Kuś, P. Stępień, K. Tokarska, K. Zukowski, E. Malinowska, Z. Brzózka, and M. Kujawińska, “On-chip holographic tomography for quantifying refractive index changes of cells’ dynamics,” in Quantitative Phase Imaging VIII, vol. 11970 Y. Liu, G. Popescu, and Y. Park, eds., International Society for Optics and Photonics (SPIE, 2022), p. 1197008.<br>[2] P. Stępień, M. Ziemczonok, M. Kujawińska, M. Baczewska, L. Valenti, A. Cherubini, E. Casirati, and W. Krauze, “Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging,” Biomed. Opt. Express 13, 5709–5720 (2022).<br>[3] M. Ziemczonok, A. Kuś, P. Wasylczyk, and M. Kujawińska, “3d-printed biological cell phantom for testing 3d quantitative phase imaging systems,” Sci. Reports 9, 1–9 (2019).<br>[4] M. Ziemczonok, A. Kuś, and M. Kujawińska, “Optical diffraction tomography meets metrology — measurement accuracy on cellular and subcellular level,” Measurement 195, 111106 (2022).<br>[5] W. Krauze, P. Makowski, M. Kujawińska, and A. Kuś, “Generalized total variation iterative constraint strategy in limited angle optical diffraction tomography,” Opt. Express 24, 4924–4936 (2016).</p>
Electronic Devices Dataset for 2-Class Semantic Segmentation
<p><span>This dataset contains images used in the monograph titled <em>Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej</em> (Application of Selected Deep Learning Methods in Computer Vision) to build the U-Net model. The full collection consists of 600 image files of resolution 512x512 pixels showing small electronic devices and office accessories (<a title="Electronic Devices Dataset" href="https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link</a>). The set was randomly divided into a training part (50% of the full set), validation and test part (each accounted for 25% of the full set). As a result, the training part contains 300 files, validation part – 150 and test part - 150. The collection was created by augmenting the original set of 100 images with vertical and horizontal flip, random rotation from -45 to 45 degrees, and a combination of both flips and random rotation. The images are labeled with masks representing 2 kind of objects – REMOTES and BATTERIES. Therefore, the dataset can be used to build models for multiclass semantic segmentation.</span></p>
A comprehensive dataset of magnetic resonance enterography images with bowel segment annotations
<p>Inflammatory bowel disease (IBD) is a recurrent bowel disease that usually requires magnetic resonance enterography (MRE) for diagnosis and monitoring. However, recognition of bowel segments from MRE images by a radiologist is challenging and time-consuming. Deep learning-based medical image segmentation has shown the potential to reduce manual effort and provide automated tools to assist in disease management; however, it requires a large-scale fine<span>-</span>annotated dataset for training. To address this gap, we collected MRE data, including HASTE(half-Fourier acquisition single-shot turbo spin-echo) sequences with coronal orientation, from 114 patients with IBD. The bowel images per patient were contoured and annotated into ten segments (stomach, duodenum, small intestine, appendix, cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum), with fine pixel-level annotations labeled by experienced radiologists. Furthermore, we <span>validated</span> the efficiency of several state-of-the-art segmentation methods using this dataset. This study established a high-quality, publicly available whole-bowel segment MR dataset with benchmark results and laid the groundwork for AI research on IBD.</p>
STS-Tooth: A multi-modal dental dataset for semi-supervised deep learning image segmentation
<p>In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions. However, the scarcity of publicly available PXI and CBCT datasets hampers their use in tooth segmentation tasks. Therefore, this paper presents a multimodal dataset for semi-supervised deep learning in dental PXI and CBCT, named STS-2D-Tooth and STS-3D-Tooth. STS-2D-Tooth includes 4,000 images and 900 masks, categorized by age into children and adults. Moreover, we have collected CBCTs providing more detailed and three-dimensional information, resulting in the STS-3D-Tooth dataset comprising 148,400 unlabeled scans and 8,800 masks. To our knowledge, this is the first multimodal dataset combining dental PXI and CBCT, and it is the largest tooth segmentation dataset, a significant step forward for the advancement of tooth segmentation.</p>
NasalSeg Dataset for Nasal Cavity and Paranasal Sinuses Segmentation from CT Images
<p>NasalSeg is the first large-scale, open-access dataset for nasal cavity and paranasal sinus segmentation from 3D CT images. The dataset comprises 130 CT scans with pixel-wise annotation of five anatomical structures including the left nasal cavity, right nasal cavity, nasal pharynx, left maxillary sinus, and right maxillary sinu.</p>
GazeMining: A Dataset of Video and Interaction Recordings on Dynamic Web Pages. Labels of Visual Change, Segmentation of Videos into Stimulus Shots, and Discovery of Visual Stimuli.
<p><strong>Recording setup</strong><br> Recordings have been taken place on 12th March 2019. Gaze data has been recorded with a Tobii 4C eye tracker with Pro license at 90 Hz. Resolution of the viewport was set to 1024x768. The display had a size of 24 inches and a resolution of 1680x1050 pixels. We polled the DOM tree every 50 milliseconds for fixed elements. We recorded the Web browsing of four participants, who followed the protocol as stored under "Dataset_visual_change/Instructions.doc".</p> <p><strong>Description of the dataset</strong><br> The dataset consists of following three subsets.</p> <p><em>1. Dataset_visual_change</em><br> The recordings of each participant p1-p4 on twelve Web sites are in the corresponding directories. For each Web site, there are nine to eleven files:</p> <ul> <li><site>.json: datacast</li> <li><site>.webm: video recording</li> <li><site>.features.csv: computer-vision features per observation</li> <li><site>.features_meta.csv: meta information about features</li> <li><site>.labels-l<X>.csv: labels of observations</li> <li><site>_meta.csv: meta information about recording</li> <li><site>_scroll_cache.csv: cache of estimated scrolling</li> <li><site>_scroll_cache_map.csv: mapping of observations to scroll cache entries</li> <li><site>_times.csv: timestamps of frames in the video recording</li> <li><site>_layer_pixels.csv: first row is the pixel count of root layer, second row is pixel count of all fixed elements</li> </ul> <p><em>2. Dataset_stimuli</em><br> Stimulus shots and visual stimuli computed with the framework. Value-based, edge-based, signal-based, and SIFT-based features have been used. The labels of the first participant's session had been used to train a random forest classifier with 100 trees for visual change classification, using the named features. The discovery has been performed on each Web site from the dataset and<br> the results are placed in the respective directories. Inside each directory, there is one directory for the detected shots and one for the discovered stimuli. In the shots directory, there is one overview as <participant>_<site>.csv file. For each shot, there are four further files:</p> <ul> <li><participant>_<site>_<shot>.png: stitched frame of the stimulus shot</li> <li><participant>_<site>_<shot>-blind.csv: frames from animations that are not contributing to the stitched frame</li> <li><participant>_<site>_<shot>-gaze.csv: gaze data (in stitched frame space)</li> <li><participant>_<site>_<shot>-mouse.csv: mouse data (in stitched frame space)</li> </ul> <p>The shots have been merged to stimuli, which are placed in the stimuli directory. The stimuli are grouped per layer (scrollable, fixed elements, etc.) and meta information is available in <layer_index>-<xpath>-meta.csv files. Furthermore, there are directories per layer, storing the discovered stimuli. Each discovered visual stimulus is represented by four files:</p> <ul> <li><stimulus_id>.png: stitched frame of the visual stimulus</li> <li><stimulus_id>-gaze.csv: gaze data (in stitched frame space)</li> <li><stimulus_id>-mouse.csv: mouse data (in stitched frame space)</li> <li><stimulus_id>-shots.csv: contained stimulus shots</li> </ul> <p><em>3. Dataset_evaluation</em><br> We have performed two evaluations of the visual stimuli discovery. One computational estimating the quality of stimuli. One case-study of an expert's task. There are two respective directories with the annotation data.</p> <p><strong>Changelog</strong><br> [1.0.2] Add counts of layer pixels per participant.<br> [1.0.1] Change to CC0 license.<br> [1.0.1] Add labels of third annotator "l3".<br> [1.0.0] Initial release.</p>
Flywing (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</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.