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35 results for “segmental labeling”
The structural determinants of PH domain-mediated regulation of Akt revealed by segmental labeling
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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>
Coastal Satellite Image Segmentation (Water and Land) Labels: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)
<p>Contained here are jpegs containing coastal RGB satellite images along with a water vs. land mask. Each image is 256 pixels by 256 pixels. </p> <p>Geographic scope: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)</p> <p>Temporal range: 1984 to 2022</p> <p>Satellites: Landsat 5, 7, 8 and Sentinel-2</p> <p>All images were downloaded from Google Earth Engine using CoastSat download tools.</p> <p>The datasets are arranged into 'train', 'val', and 'test' folders. Within each of those folders are two folders 'a' and 'b'. 'a' contains the images (RGB), whereas 'b' contains the labels (land vs. water mask).</p> <p>All images were augmented with the four following augmentations: horizontal flip, vertical flip, 90 degree clockwise rotation, 90 degree counterclockwise rotation, and a horizontal+vertical flip. </p> <p>For training a new segmentation model, it is advised to not do any of these rotational or flip augmentations since they have already been performed. Instead, possibly experiment with other augmentations like introducing noise into the imagery.</p> <p>These images were used to train an image-to-image translation generative adversarial network. The code and model weights (generator and discriminator) are available at <a href="https://github.com/mlundine/Shoreline_Extraction_GAN">https://github.com/mlundine/Shoreline_Extraction_GAN</a>.</p> <p>To get to the files locally, you can download the .zip from Zenodo and then unzip the .zip file.</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>
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>
An Open-Label Phase 2 Study of N-Acetyl-D-Mannosamine (ManNAc) in Subjects With Primary Focal Segmental Glomerulosclerosis
ClinicalTrials.gov study NCT06664814. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
Ground truth labels for "BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation In Patients with Cerebrovascular Disease"
<p>Manual, voxel-wise segmentation ground truth labels for 20 healthy volunteers (4 from each age group) from the publicly available MIDAS data collection website under:<br> https://www.insight-journal.org/midas/community/view/21‌</p> <p> </p>
LIVECell A large-scale dataset for label-free live cell segmentation - organised by cell type
<p>This data was originally published in </p><p><a href="https://doi.org/10.1038/s41592-021-01249-6">Edlund, C., Jackson, T.R., Khalid, N. <i>et al.</i> LIVECell—A large-scale dataset for label-free live cell segmentation. <i>Nat Methods</i> <strong>18</strong>, 1038–1045 (2021). https://doi.org/10.1038/s41592-021-01249-6</a></p><p>The instance segmentation annotation data has been downloaded and organised in folders for each cell type to ease its use in demos and courses. Please, refer to the original publication and <a href="https://sartorius-research.github.io/LIVECell/">original data source</a> for further research studies. </p><p><strong>IF YOU USE THIS DATASET, PLEASE ACKNOWLEDGE THE ORIGINAL AUTHORS (Edlund et al., Nat Methods 2021)</strong></p>
Images and Labels for Segmentation Studies in Microscopy
Open the record for dataset details and reuse information.
Labelled Arabidopsis dataset for testing membrane segmentation models
<p>Labels of 3D dataset of confocal imaged arabidopsis for testing segmentation models.</p>
An Open-label Single Center, Proof of Concept Study Evaluating the Efficacy of Roflumilast Foam 0.3% in Pediatric Patients With Non-Segmental Vitiligo (NSV)
ClinicalTrials.gov study NCT07105254. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Multi-Centre, Open-Label, Two Segment, Study Comparing Plasma Pharmacokinetics of Ketoprofen in Diractin® Alone to Diractin® Applied Concomitant With Heat or Moderate Exercise
ClinicalTrials.gov study NCT00745264. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Open Label Study of Everolimus (RAD001) in Patients With Segmental Overgrowth Syndrome
ClinicalTrials.gov study NCT02569125. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Open Label Trial Studying the Safety and Effectiveness of ILUVIEN® (190μg) in Children and Adolescents, Who Have Recurrent Non-infectious Uveitis Affecting the Posterior Segment of the Eye.
ClinicalTrials.gov study NCT06539481. IPD Sharing: NO. Countries: 3. Publications: 0.
The intracranial tumor segmentation challenge: ICTS 2021-image labels
<p>This dataset was retrieved from clinical work of radiosurgery. It contains brain tumor labels of 150 patients.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.