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282 results for “image segments”
Text-fig. 45. Scanning electron microscope (SEM) images of monocolpate pollen of Dinisia portugallica gen. et sp. nov. from a fragmentary stamen; Torres Vedras locality, Portugal. a) Holotype; stamen fragment showing elongated pollen sacs that yielded the pollen in this Text-figure; b) Two pollen grains showing poorly defined distal aperture (arrowhead) and distinctive vermiform reticulum forming luminae of variable shapes and sizes; note especially the irregularly and incomplete reticulum in the grain on the left; c) Reticulum showing smooth, vermiform muri attached to the smooth surface of the foot layer by long columellae; note that columellae often terminate segments of muri that are not closed; d, e) Pollen grains showing proximal surface (d), poorly defined distal aperture (e, arrowhead) and distinctive vermiform reticulum supported by long columellae; note dense covering of small, spherical orbicules on the inner surface of the anther wall. Specimen, TV44-S148216 (holotype). Scale bars 300 Μm (a), 6 Μm (b, d, e), 3 Μm (c). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 45. Scanning electron microscope (SEM) images of monocolpate pollen of Dinisia portugallica gen. et sp. nov. from a fragmentary stamen; Torres Vedras locality, Portugal. a) Holotype; stamen fragment showing elongated pollen sacs that yielded the pollen in this Text-figure; b) Two pollen grains showing poorly defined distal aperture (arrowhead) and distinctive vermiform reticulum forming luminae of variable shapes and sizes; note especially the irregularly and incomplete reticulum in the grain on the left; c) Reticulum showing smooth, vermiform muri attached to the smooth surface of the foot layer by long columellae; note that columellae often terminate segments of muri that are not closed; d, e) Pollen grains showing proximal surface (d), poorly defined distal aperture (e, arrowhead) and distinctive vermiform reticulum supported by long columellae; note dense covering of small, spherical orbicules on the inner surface of the anther wall. Specimen, TV44-S148216 (holotype). Scale bars 300 Μm (a), 6 Μm (b, d, e), 3 Μm (c).
Text-fig. 8. Scanning electron microscope (SEM) images of spores from clumps of spores and sporangia with affinities to polypodiopsids (a–c) and of uncertain affinity (d–k); Torres Vedras locality, Portugal. a) Folded Cyathidites minor spores in proximal view showing trilete mark, from clump of spores; b) Cyathidites minor spores in proximal view showing trilete mark, from group of sporangia; c) Cyathidites australis spores in proximal view showing trilete mark, from group of sporangia; d–f) Linear group of spore masses (d; probable sporangial contents) composed of Taurocusporites segmentatus spores showing distal surface (e, middle) with concentric regions and proximal surface with segmented laesurae of elongated granules (e, right; f); in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 8. Scanning electron microscope (SEM) images of spores from clumps of spores and sporangia with affinities to polypodiopsids (a–c) and of uncertain affinity (d–k); Torres Vedras locality, Portugal. a) Folded Cyathidites minor spores in proximal view showing trilete mark, from clump of spores; b) Cyathidites minor spores in proximal view showing trilete mark, from group of sporangia; c) Cyathidites australis spores in proximal view showing trilete mark, from group of sporangia; d–f) Linear group of spore masses (d; probable sporangial contents) composed of Taurocusporites segmentatus spores showing distal surface (e, middle) with concentric regions and proximal surface with segmented laesurae of elongated granules (e, right; f);
Outputs from new methods for 3D+time cell image segmentation and tracking
<p>Segmentation and tracking of 3D+time microscopy images of cell nuclei within the zebrafish pectoral fin.</p> <p>The file named 7_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named 7_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for seven cells (colored black) moving in time, and the file _tracking_of_7_cell_in_70_frames.mp4 has the tracking of these seven cells. </p> <p>Additionally, the file named group_of_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named group_of_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for the group of cells (colored black) moving in time and the file _tracking_of_group_of_cell_in_70_frames.gif has the tracking of this group of cells. </p>
Dataset for "Root Length Estimation: Automated Minirhizotron Image Analysis with Convolutional Networks without Segmentation"
<p>This data contains 4015 root images, splitted into 4 datasets, acquired using two minirhizotron (MR) system types - manual (Dataset 1 & Dataset 4) and automated (Dataset 2 & Dataset 3). It includes four crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. The data was acquired by researchers from Ben-Gurion University of the Negev, Beer Sheva, Israel, and used for research of automated TRL estimation with Convolutional Neural Networks.</p> <p>The annotations were conducted manually using the Rootfly software (Wells and Birchfield, Clemson University, South Carolina, USA), and data were transformed as CSV formats. In this software, the annotator must draw a root by marking points along the selected root. These points usually correspond to the coordinates at the start and the end of the root, and curving points along the root. These points are then connected in a line, the length of which reflects the real length of the selected root. The annotations has been done for all roots within an image, and for all images in the provided dataset.</p> <p>The provided annotations include the total root length (TRL) per image (mm) and the coordinates of annotated points.</p> <p>The annotations are given in two types of files:</p> <p>"TRL.csv" files: contain the image names and corresponding TRL values (mm).</p> <p>"pointsOutput.csv" files: contain the annotated image names and the coordinates of the points of the roots in the image (if the image contains roots) in the form of x1, y1, x2, y2, x3, y3, etc. It the image doesn't have roots, the file contains only its name.</p>
Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation
<p>Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference, integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.</p>
VegAnn: Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image segmentation
<p> VegAnn - Vegetation Annotation - dataset, a collection of 3795 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. </p>
WE3DS: An RGB-D image dataset for semantic segmentation in agriculture
<p>Here, we introduce a novel RGB-D image database (WE3DS) for semantic segmentation in crop farming. It contains 2,568 RGB-D images (color image and distance map) and hand-annotated ground-truth masks for semantic segmentation and is the first RGB-D image dataset for multi-class plant species semantic segmentation task. Images were taken under natural light conditions using an RGB-D sensor consisting of two RGB cameras in a stereo setup.</p> <p> </p> <p><strong>Please cite the original source when using this dataset.</strong></p> <p>Kitzler, F.; Barta, N.; Neugschwandtner, R.W.; Gronauer, A.; Motsch, V. WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture. <em>Sensors</em> <strong>2023</strong>, <em>23</em>, 2713. <a href="https://doi.org/10.3390/s23052713">https://doi.org/10.3390/s23052713 </a></p>
Instance Segmentation of Dislocations in TEM Images
<p>This is the dataset and software for the IEEE publication <em>Instance Segmentation of Dislocations in TEM Images </em>to be published in 2023.</p>
Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Zoo
<p># Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Gym</p> <p>## Overview<br> * Test datasets and files for testing the [segmentation gym](https://github.com/Doodleverse/segmentation_gym) program for image segmentation<br> * Data set made by Daniel Buscombe, Marda Science LLC. This is version 5.0<br> * Dataset consists of a time-series of Landsat-8 images of Cape Hatteras National Seashore, courtesy of the U.S. Geological Survey.<br> * Imagery spans the period February 2015 to September 2021.<br> * Labels were created by Daniel Buscombe, Marda Science, using the labeling program [Doodler](https://github.com/Doodleverse/dash_doodler).</p> <p>Download this file and unzip to somewhere on your machine (although *not* inside the `segmentation_gym` folder), then see the relevant page on the [segmentation gym wiki](https://github.com/Doodleverse/segmentation_gym/wiki) for further explanation.</p> <p>This dataset and associated models were made by Dr Daniel Buscombe, Marda Science LLC, for the purposes of demonstrating the functionality of Segmentation Gym. The labels were created using [Doodler](https://github.com/Doodleverse/dash_doodler/).</p> <p>Previous versions:</p> <p>1.0. https://zenodo.org/record/5895128#.Y1G5s3bMIuU original release, Oct 2021, conforming to Segmentation Gym functionality on Oct 2021</p> <p>2.0 https://zenodo.org/record/7036025#.Y1G57XbMIuU, Jan 23 2022, conforming to Segmentation Gym functionality on Jan 23 2022</p> <p>This is version 5.0, created 7/20/23, and has been tested with Segmentation Gym using doodleverse-utils 0.0.33 https://pypi.org/project/doodleverse-utils/0.0.33/</p> <p> </p> <p>## file structure</p> <p>```{sh}<br> /Users/Someone/my_segmentation_zoo_datasets<br> │ ├── config<br> │ | └── *.json<br> │ ├── capehatteras_data<br> | | ├── fromDoodler<br> | | | ├──images<br> │ | | └──labels<br> | | ├──npzForModel<br> │ | └──toPredict<br> │ └── modelOut<br> │ └── *.png<br> │ └── weights<br> │ └── *.h5</p> <p>```</p> <p>## config<br> There are 4 config files:<br> 1. `/config/hatteras_l8_resunet.json`<br> 2. `/config/hatteras_l8_vanilla_unet.json`<br> 3. `/config/hatteras_l8_resunet_model2.json`</p> <p>4. `/config/hatteras_l8_segformer.json`<br> </p> <p> </p> <p>The first two are for res-unet and unet models respectively. The third one differs from the first only with specification of kernel size. It is provided as an example of how to conduct model training experiments, modifying one hyperparameter at a time in the effort to create an optimal model. The last one is based on the new Segformer model architecture.</p> <p>They all contain the same essential information and differ as indicated below</p> <p>```<br> {<br> "TARGET_SIZE": [768,768], # the size of the imagery you wish the model to train on. This may not be the original size<br> "MODEL": "resunet", # model name. Otherwise, "unet" or "segformer"<br> "NCLASSES": 4, # number of classes<br> "KERNEL":9, # horizontal size of convolution kernel in pixels<br> "STRIDE":2, # stride in convolution kernel<br> "BATCH_SIZE": 7, # number of images/labels per batch<br> "FILTERS":6, # number of filters<br> "N_DATA_BANDS": 3, # number of image bands<br> "DROPOUT":0.1, # amount of dropout<br> "DROPOUT_CHANGE_PER_LAYER":0.0, # change in dropout per layer<br> "DROPOUT_TYPE":"standard", # type of dropout. Otherwise "spatial"<br> "USE_DROPOUT_ON_UPSAMPLING":false, # if true, dropout is used on upsampling as well as downsampling<br> "DO_TRAIN": false, # if false, the model will not train, but you will select this config file, data directory, and the program will load the model weights and test the model on the validation subset<br> if true, the model will train from scratch (warning! this will overwrite the existing weights file in h5 format)<br> "LOSS":"dice", # model training loss function, otherwise "cat" for categorical cross-entropy<br> "PATIENCE": 10, # number of epochs of no model improvement before training is aborted<br> "MAX_EPOCHS": 100, # maximum number of training epochs<br> "VALIDATION_SPLIT": 0.6, #proportion to use for validation<br> "RAMPUP_EPOCHS": 20, # [LR-scheduler] rampup to maximim<br> "SUSTAIN_EPOCHS": 0.0, # [LR-scheduler] sustain at maximum<br> "EXP_DECAY": 0.9, # [LR-scheduler] decay rate<br> "START_LR": 1e-7, # [LR-scheduler] start lr<br> "MIN_LR": 1e-7, # [LR-scheduler] min lr<br> "MAX_LR": 1e-4, # [LR-scheduler] max lr<br> "FILTER_VALUE": 0, #if >0, the size of a median filter to apply on outputs (not recommended unless you have noisy outputs)<br> "DOPLOT": true, #make plots<br> "ROOT_STRING": "hatteras_l8_aug_768", #data file (npz) prefix string<br> "USEMASK": false, # use the convention 'mask' in label image file names, instead of the preferred 'label'<br> "AUG_ROT": 5, # [augmentation] amount of rotation in degrees<br> "AUG_ZOOM": 0.05, # [augmentation] amount of zoom as a proportion<br> "AUG_WIDTHSHIFT": 0.05, # [augmentation] amount of random width shift as a proportion<br> "AUG_HEIGHTSHIFT": 0.05,# [augmentation] amount of random width shift as a proportion<br> "AUG_HFLIP": true, # [augmentation] if true, randomly apply horizontal flips<br> "AUG_VFLIP": false, # [augmentation] if true, randomly apply vertical flips<br> "AUG_LOOPS": 10, #[augmentation] number of portions to split the data into (recommended > 2 to save memory)<br> "AUG_COPIES": 5 #[augmentation] number iof augmented copies to make<br> "SET_GPU": "0" #which GPU to use. If multiple, list separated by a comma, e.g. '0,1,2'. If CPU is requested, use "-1"<br> "WRITE_MODELMETADATA": false, #if true, the prompts `seg_images_in_folder.py` to write detailed metadata for each sample file<br> "LOSS_WEIGHTS": false, #if true, apply per-class weights to loss function</p> <p> "SET_PCI_BUS_ID": true, #if true, make keras aware of the PCI BUS ID (advanced or nonstandard GPU usage)</p> <p> "TESTTIMEAUG": true, #if true, apply test-time augmentation when model in inference mode</p> <p> "WRITE_MODELMETADATA": true,# if true, write model metadata per image when model in inference mode</p> <p> "OTSU_THRESHOLD": true# if true, and NCLASSES=2 only, use per-image Otsu threshold rather than decision boundary of 0.5 on softmax scores</p> <p>}<br> ```</p> <p>## capehatteras_data<br> Folder containing all the model input data</p> <p>```{sh}<br> │ ├── capehatteras_data: folder containing all the model input data<br> | | ├── fromDoodler: folder containing images and labels exported from Doodler using [this program](https://github.com/dbuscombe-usgs/dash_doodler/blob/main/utils/gen_images_and_labels_4_zoo.py)<br> | | | ├──images: jpg format files, one per label image<br> │ | | └──labels: jpg format files, one per image<br> | | ├──npz4gym npz format files for model training using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/train_model.py) that have been created following the workflow [documented here](https://github.com/dbuscombe-usgs/segmentation_zoo/wiki/Create-a-model-ready-dataset) using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/make_nd_dataset.py)<br> │ | └──toPredict: a folder of images to test model prediction using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/seg_images_in_folder.py)<br> ```</p> <p>## modelOut<br> PNG format files containing example model outputs from the train ('_train_' in filename) and validation ('_val_' in filename) subsets as well as an image showing training loss and accuracy curves with `trainhist` in the filename. There are two sets of these files, those associated with the residual unet trained with dice loss contain `resunet` in their name, and those from the UNet are named with `vanilla_unet`.</p> <p>## weights<br> There are model weights files associated with each config files.</p>
Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset
<p>This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain:</p> <ol> <li>3D binary masks of biological cells obtained through Cellpose [1] and <a href="https://github.com/biopto/ODT-SAS.git">ODT-SAS</a>;</li> <li>3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS.</li> </ol> <p>All files are .*mat files.</p> <p>The files <em>REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat</em> and<em> REC_SH-SY5Y_3.mat</em> consist of 7 variables:</p> <p>RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [<span class="math-tex">\(\mu m\)</span>];<br> rayXY – xy-coordinates of illumination vectors;</p> <p>maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.</p> <p>File <em>REC_SH-SY5Y_4.mat</em> includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.<br> <br> Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example:</p> <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <p><br> [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.</p> <p> </p>
WhiteRoadLines: Dataset of 27,025 images (256x256 pixeles at 0.15 m/ pixel) containing representative road lines and markings labelled for multi-class semantic segmentation
<p>The dataset consists of 27,025 PNG images (256x256 pixels) of high resolution aerial orthoimages at 0,15 m/pixel of resolution. The images contain information related to representative road lines and markings found on highway pavement and is labelled for multi-class semantic segmentation with tree classes of white road<br>lines and markings: (1) continuous line (black color), (2) dashed line (dark gray color) and (3) separation of entry and exit lanes (light gray color), together with (4) the background (white color). <br> </p><p>The dataset has been created in the framework of the SROADEX project to train a multiclass semantic segmentation process based on Deep Learning.<br>The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the three different types of white road lines. This cartography has been obtained from Spanish official sources (National Geographic Institute) that we have<br>revised and edited in a meticulous and systematic way to verify that the road lines are represented on the cartography according to the orthoimages, available on January 1, 2022 in the download center of the National Center of Geographic Information (CNIG). </p><p>In the digitisation process, 46 homogeneously distributed areas of Spain have been selected. The orthoimages used have been resampled from the original resolution of 0,25m/pixel to 0,15m/pixel, as this is closer to the width of two of the three classes of white lines in the dataset. It resulted in 80% of the images for training (21622), 10% for validation (2702) and 10% for testing (2701). The following table summarises the number of pixels of each category included in each of the three sub-datasets</p><p> </p><p>Set Nº images Class_1 (continuous line) Class_2 (discontinuous line) Class_3 (line defining highway entrance or exit) Class_4 (background)</p><p>Train 21,622 27,633,537 4,543,552 3,284,380 1,381,557,923</p><p>Validation 2,702 3,433,103 570,741 395,646 172,678,782</p><p>Test 2,701 3,435,072 536,838 429,527 172,611,299</p><p>Total 27,025 34,501,712 5,651,131 4,109,553 1,726,848,004</p>
Code and example images from: recolorize: An R package for flexible color segmentation of biological images
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Segmentations of 3D electron microscopy image volume from an albino mouse dorsal lateral geniculate nucleus
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Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network
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Data from: imageseg: An R package for deep learning-based image segmentation
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Segmented high-resolution transmission electron microscopy images of nanoparticles
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The different effects in medium-field and far-field of the Pacific plate subduction: revealed by magnetotelluric imaging at the eastern segment of the Central Asian Orogenic Belt
<p>The long-period magnetotelluric sounding profile with the length of 1500 km crossed major geological tectonic units in Northeast China, including the Erguna block, the Xing'an block, the Songnen block, and the Jiamusi block. MT data were recorded at 81 stations, with an average site spacing of 20 km</p>
Amazon and Atlantic Forest image datasets for semantic segmentation
<p>This database contains images from<strong> Amazon </strong>and <strong>Atlantic Forest </strong>brazilian biomes used for training a fully convolutional neural network for the semantic segmentation of forested areas in images from the Sentinel-2 Level 2A Satellite.</p> <p>The images refer to the composition of bands 4, 3, 2 and 8. Each band was converted to a byte type (0-255).</p> <p>The images are still divided into three main sets: training, validation and testing:</p> <ol> <li><strong>Training dataset: </strong>it contains 499 and 485 GeoTIFF images (Amazon and Atlantic Forest, respectively) with 512x512 pixels and associated PNG masks (forest indicated in white and background in black color).</li> <li><strong>Validation dataset</strong>: it contains 100 GeoTIFF images for each biome with 512x512 pixels and associated PNG masks used for validation step.</li> <li><strong>Test dataset: </strong>it contains 20 GeoTIFF images for each biome with 512x512 pixels for testing.</li> </ol>
Test Dataset for 3D semantic image segmentation of the various organs from CT and MR scans
<p>These test cases are for the <a href="https://github.com/MIC-DKFZ/nnUNet/releases/tag/v1.7.1">nnUnet v1</a> models trained on the following datasets:<br><br></p> <table> <tbody> <tr> <td>Dataset </td> <td>Task</td> <td>Model Details on Zenodo</td> </tr> <tr> <td> <a href="../record/6802614">TotalSegmentator</a> and <a href="../record/5903672">FLARE21</a> datasets</td> <td>Segment Liver from CT scans</td> <td>https://zenodo.org/record/8274976</td> </tr> <tr> <td><a href="https://kits-challenge.org/kits23/">KiTS23</a> datasets and a subset of the<a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=5800386#5800386566e265abf95408aa64c4917f0cbe5d9"> TCGA-KIRC </a>dataset</td> <td>Segment Kidney, Cyst, and Tumors from CT Scans</td> <td>https://zenodo.org/records/8277846</td> </tr> <tr> <td><a href="http://ji%20yuanfeng.%20(2022).%20amos%20a%20large-scale%20abdominal%20multi-organ%20benchmark%20for%20versatile%20medical%20image%20segmentation%20[data%20set].%20zenodo.%20https">AMOS</a> and <a href="http://macdonald,%20jacob%20a.,%20zhu,%20zhe,%20konkel,%20brandon,%20mazurowski,%20maciej,%20wiggins,%20walter,%20&%20bashir,%20mustafa.%20(2020).%20duke%20liver%20dataset%20(mri)%20v2%20(2.0.0)%20[data%20set].%20zenodo.%20https//doi.org/10.5281/zenodo.7774566">DUKE Liver</a> datasets</td> <td>Segment Liver from the MR scans</td> <td>https://zenodo.org/record/8290124</td> </tr> <tr> <td>Data from m <a href="../record/6624726">pi-cai</a></td> <td>Segment Prostate region from MR scans</td> <td>https://zenodo.org/record/8290093</td> </tr> </tbody> </table>
Neural Image Segmentation for Redacted Text Detection Dataset
<p>This dataset was created for the "<span>Redacted Text Detection Using Neural Image </span><span>Segmentation Methods</span>" project, and contains roughly 1000 pages with manually annotated redactions in Dutch documents released under the WOO, with the trained model files and the model outputs also included in the dataset. More details on the usage of the dataset and models can be found on Github: https://github.com/RubenvanHeusden/NeuralRedactedTextDetection/</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.