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979 results for “image dataset”
Datasets of Vascular Networks Extracted from Retinal Images of Hypertensive Retinopathy and Glaucoma Patients
<p>This dataset contains high resolution images of blood vessels extracted from Digital Retinal Images for Optic Nerve Segmentation Database (DRIONS-DB). It contains 110 blood vessels of individuals of which 23.1% are patients with chronic glaucoma while the remaining 76.9% are hypertensive retinopathy patients. </p>
The ReDraw Dataset: A Set of Android Screenshots, GUI Metadata, and Labeled Images of GUI Components
<p>This is the dataset used to train and evaluate the CNN and KNN machine learning techniques for the ReDraw paper, published in IEEE Transactions on Software Engineering in 2018.</p> <p>Link to ReDraw Paper: https://arxiv.org/abs/1802.02312 </p>
Dataset of 100 simulated root images, with different resolutions
<p>All images were analysed with WinRhizo<sup>TM</sup> 2013, Regent method (Régent Instruments Inc 2013) and ImageJ (1.51j8, Schneider, Rasband, & Eliceiri, 2012) with the macro IJ_Rhizo (IJ_Rhizo_v0beta). We conducted batch analyses with the grey level thresholds set to automatic at all four resolutions (200, 400, 800 and 1200 DPI). We further analysed the images at 800 dpi and 1200 dpi with the threshold value manually set to 144. We considered this value suitable after visual inspection of 10 images at different thresholds using the threshold slider of WinRhizo<sup>TM</sup>. Since no debris was simulated, we used no automatic debris correction. For WinRhizo<sup>TM</sup>, the boundaries of diameter classes were set from 0.1 to 1.9 mm (with an increment of 0.1 mm).</p> <p>The dataset contains:</p> <p>- the root images in jpg format (images.zip)</p> <p>- the root architecture data in rsml format (rsml.zip)</p> <p>- the ground-truth value for each image (all_root_data_2D.csv)</p> <p>- the ground-truth value for each root in each image (ground_truth_data_2D.csv)</p> <p>- the estimation given by WinRhizo and IJ_Rhizo (all_analyses_2D.txt and all_analyses_fixed.txt)</p> <p>- the different codes used for the analysis (*.R)</p> <p>Dataset used in : Accuracy of image analysis tools for functional root traits: A comment on Delory et al., 2017 from Rose and Lobet (2019), Methods in Ecology and Evolution. </p> <p>The images in this dataset are a subset of the dataset here: https://doi.org/10.5281/zenodo.208214</p>
Image Synthesis with a Convolutional Capsule Generative Adversarial Network- Dataset
<p><strong>Dataset of 152 two-photon images (512x512) of axons with segmentation labels. </strong></p> <p>We combined data from two published sources (Bass et al., 2017; Canty et al., 2018) to get 152 (512×512) 2D images (produced from 3D image stacks), and manually produced the corresponding labels. These images were collected using in-vivo two-photon microscopy from the mouse somatosensory cortex. To generate the 2D images, we used a max projection over the 3D stack. The labels are binary segmentation maps of the axons.</p> <p>This dataset is split into a train (132 images) and test (20 images) sets. The raw 2D images of axons are in /original folder, and the segmentation labels are in /mask folder.</p> <p><strong>Please cite the following paper when using this dataset:</strong></p> <p>Bass, C., Dai, T., Billot, B., Arulkumaran, K., Creswell, A., Clopath, C., De Paola, V., and Bharath, A. A., 2019. “Image synthesis with a convolutional capsule generative adversarial network,” <em>Medial Imaging with Deep Learning.</em></p> <p><strong>This dataset was complied from the following publications:</strong><br> Bass, C., Helkkula, P., De Paola, V., Clopath, C. and Bharath, A.A., 2017. Detection of axonal synapses in 3D two-photon images. PloS one, 12(9), p.e0183309.<br> Canty, A.J., Jackson, J.S., Huang, L., Trabalza, A., Bass, C., Little, G. and De Paola, V., 2018. Single-axon-resolution intravital imaging reveals a rapid onset form of Wallerian degeneration in the adult neocortex. <em>bioRxiv</em>, p.391425.</p>
AEROARMS - Image Dataset for the Crawler Indirect Detection through its Cage
<p>Dataset containing images and ground-truth position of the crawler's cage used in the AEROARMS project experiments.</p>
AEROARMS - Crawler Direct Detection Image Dataset
<p>Dataset containing images and ground-truth position of the crawler used in the AEROARMS project experiments.</p>
Imaging dataset for "Angiotensin II infusion into ApoE-/- mice: a model for aortic dissection rather than abdominal aortic aneurysm?"
<p>This dataset contains imaging files of the manuscript "Angiotensin II infusion into ApoE-/- mice: a model for aortic dissection rather than abdominal aortic aneurysm?", published in Cardiovascular Research in 2017. All published files are to be opened in Mimics (Materialise, Leuven, Belgium). All files are named as follows: "technique_id_timepoint_abd.mcs". The technique can be either in vivo micro-CT, or ex-vivo synchrotron-based PCXTM. The mouse ID corresponds to the ID that was used during the experiments. Mice were distributed at random into 2 groups (G1 and G2) and sacrificed after 1 of 4 possible timepoints. TP0 corresponds to baseline (prior to Ang II infusion), TP1 corresponds to 3 days of Ang II infusion, TP2 corresponds to 10 days of Ang II infusion, TP3 corresponds to 18 days of Ang II infusion and TP4 corresponds to 28 days of Ang II infusion. Image files from PCXTM are ex vivo and always correspond to the latest timepoint available with micro-CT of that animal. Histology data are zipped and stored in .vsi format. They can be opened with the Olympus software package OlyVIA or with the BIOP-plugin "vsi-reader" to the open source software package fiji.</p>
(06)-He2019A-DS0003 – Tribolium castaneum AGOC #6 subline × foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(06)-He2019A-DS0003 – <em>Tribolium castaneum</em> AGOC #6 subline × foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(06)-He2019A-DS0002 – Tribolium castaneum foxQ2-5' line × AGOC #6 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(06)-He2019A-DS0002 – <em>Tribolium castaneum</em> foxQ2-5' line × AGOC #6 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents [HisIR19] Dataset
<p>This dataset contains the training and test set used in the ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents.</p> <p>This competition investigates the performance of large-scale retrieval of historical document images based on<br> writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing<br> a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval.</p> <p>The training data set encompasses images from (i) Letters A, where each writer contributed one or three images; (ii) Manuscripts, where each writer was represented by five consecutive images from a single book.<br> In total, it contains 300 writers contributing one page, 100 writers contributing three pages, and 120 writers contributing five pages resulting in 1200 images of 520 writers.</p> <p>The test data set contains 20 000 images: About 7 500 pages stem from isolated documents (partially anonymous writers, contributing one page each), and about 12 500 pages are from writers that contributed three or five pages.</p> <p> </p> <p>If you use this dataset, please cite:</p> <p>V. Christlein, A. Nicolaou, M. Seuret, D. Stutzmann, A. Maier: "ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents", in 15th International Conference on Document Analysis and Recognition, 2019, Sydney, Australia</p> <p> </p>
Image dataset
<p>This is the e-commerce image dataset for classification purpose.</p> <p><strong>Data Set Characteristics: </strong>Real</p> <p><strong>Number of Instances: </strong>20000</p> <p><strong>Area:</strong> Computer science</p> <p><strong>Number of classes: </strong>2 (Speakers and apparels)</p> <p><strong>Number of Attributes: </strong>1</p> <p><strong>Associated Tasks:</strong> Classification</p> <p>This dataset is collected from an Indian e-commerce platform.<br> </p>
Lumbar Spine Vertebral Compression Fractures (VCFs) Dataset: MRI T1-Weighted Images for Benign and Malignant Classification
<p>This dataset was prepared for the study of classification of benign vertebral compression fractures (VCFs) secondary to osteoporosis and malignant VCFs secondary to neoplastic infiltration. The original study in which it was used aimed to assist in differentiating these conditions using three-dimensional radiomic techniques and artificial neural networks.</p> <p>This dataset was assembled from sagittal T1-weighted magnetic resonance imaging (MRI) scans of the lumbar spine obtained from consecutive patients diagnosed with benign or malignant VCFs at the University Hospital of the Ribeirão Preto Medical School (HCFMRP) between the years 2010 and 2019. The images were acquired using the Philips Achieva 1.5 T and 3 T MRI systems and were stored in the DICOM (Digital Imaging and Communications in Medicine) format.</p> <p>The compilation of the dataset followed a rigorous selection and filtering process. From the initial set of cases of vertebral fractures in the lumbar region, patients who had received prior treatment (such as chemotherapy, radiotherapy, or surgery), those with fractures of traumatic etiology, old fractures, patients under 18 years old, and cases of malignant fractures without biopsy confirmation were excluded. With these exclusions, the final set consists of 91 patients (36 men and 55 women, with a mean age of 64.24 ± 11.75 years), of which 47 have benign VCFs and 44 have malignant VCFs.</p> <p>For the segmentation of fractured vertebrae, the images were pre-processed by normalizing the intensity to 256 gray levels (0 to 255), and histogram equalization was applied to improve contrast. The vertebrae were semi-automatically segmented using the 3D Slicer software. Each segmentation was saved in the "nrrd" format, native to 3D Slicer. The entire segmentation process, as well as the definition and application of exclusion criteria, were supervised by a senior radiologist with 20 years of experience in musculoskeletal radiology.</p> <p>The structure of this dataset includes, in addition to the DICOM exams, a directory containing the requantized images to 256 gray levels in the "nrrd" format and another with the segmentation files in the "seg.nrrd" format. A spreadsheet with detailed information about each patient's class, sex, age, and which vertebral bodies were segmented is also available. All DICOM files in this dataset have been anonymized to ensure patient privacy.</p> <p>This dataset was structured to provide a robust basis for the training and validation of machine learning models focused on the classification of vertebral compression fractures. It was designed to aid in the massive three-dimensional extraction of radiomic features, enabling the search for radiomic signatures capable of assisting radiologists in the accurate characterization of these fractures.</p> <p>For more information on how the dataset was created and used, please refer to the original article: <a href="https://link.springer.com/article/10.1007/s10278-023-00847-4" target="_blank" rel="noopener"><em>Chiari-Correia NS, Nogueira-Barbosa MH, Chiari-Correia RD, Azevedo-Marques PM. A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI. J Digit Imaging. 2023;36(4):1565-1577. doi:10.1007/s10278-023-00847-4</em></a></p>
Cercospora Leaf Spot in Chili Pepper Leaves Image Dataset
<p>A custom dataset consisting of 1,738 preprocessed images of chili pepper leaves affected by Cercospora leaf spot for research purposes related to lesion detection using artificial intelligence algorithms.</p>
3D super-resolution datasets associated with the paper "Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet"
<p>3D single-molecule super-resolution datasets corresponding to reconstructions shown in <em>Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet</em> by Saliba & Gagliano, Gustavsson et. al.</p>
Macroscopic, histological and stereological image dataset of Four-Spot megrim (Lepidorhombus boscii) ovaries from the ICES Celtic Seas and Bay of Biscay Ecoregions
<p><strong>Contents: </strong></p> <p>This dataset contains the macroscopic and histological images of the ovaries of 68 Four-spot megrim (female, <em>Lepidorhombus boscii</em> (Risso, 1810)) collected from the ICES Celtic Seas or Bay of Biscay Ecoregions (Eco) in November 2019 (n=25; Eco=7j), November 2020 (n=16, Eco=7h & 7j), October 2021 (n=12, Eco=8a,b,c) and November 2021 (n=15, Eco=7j) during the annual scientific campaign EVHOE (Evaluation Halieutique Ouest de l'Europe).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 139 pictures (.JPG; 2Mo-6Mo; JPG; 350pp) from 51 female Four-spot megrim dissected during this study. Each photo was taken with a digital camera (no flash). For each individual, up to three pictures were taken when possible (Le Meleder <em>et al.</em>, 2022) with : </li> <ul> <li>one picture of the entire fish with its abdominal cavity open with the ovaries in view</li> <li>one picture of the whole fish with the ovaries outside of the abdominal cavity</li> <li>one picture of the ovaries</li> <li>the name of the picture is the same as the fish's ID number.</li> </ul> <li><strong>Histology_slides.zip :</strong> archive in zip format containing the ovarian histological slides digitized using an Aperio CS (Scan Scope Console software, v.10.2.0.2352), x20 lens. The whole slide images (.svs) are of the 218 histological slides acquired during this study. </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>QuPath.zip :</strong> archive in zip format containing the reading grids for the stereology readings of the histological cross sections of the ovaries under QuPath. For more information on QuPath stereology readings, see Dubroca <em>et al.</em> (2023) in <strong>References</strong>.</li> <ul> <li><strong>Stereo_BOS_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the stereology reading grids under QuPath, as well as their meaning</li> </ul> <li><strong>Macro_BOS_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_BOS.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_BOS.csv</strong> : Data file (.csv) containing measurements of macroscopic parameters for all 68 fish sampled during this study. The information contained in this table is as follows: </li> <ul> <li>Fish_id: identification of the fish. This id is identical to the name given to the pictures of the full ovaries (<strong>Macroscopic_pictures_Data</strong>)</li> <li>ICES _Division: International Council for the Exploration of the Sea (ICES) division where the fish was sampled in the Food and agricultural Organization of the United nations (FAO) fishing area 27</li> <li>ICES_statistical_rectangle : Statistical rectangle where the fish was sampled within the FAO fishing area 27</li> <li>Date: date the fish was caught (dd/mm/yyyy)</li> <li>Total_fish_length: total length of the fish (cm)</li> <li>Ungutted_fish_weight: total weight of the fish (g)</li> <li>Otolith_ID: unique identification number given to each sampled fish through the Imagine (Ellebode <em>et al.</em>, 2022) software used by IFREMER</li> <li>Parasite: presence (Y) or absence (N) of parasite in or on the fish</li> <li>age: age (in years) of the fish after analysis of the fish's otolith. The IFREMER laboratory of Boulogne-sur-Mer (FRANCE) executed this analysis</li> <li>Visual_maturity : visually estimated maturity, after observation macroscopic criteria of the fish's gonad with the naked eye, following the WKASMSF (ICES, 2018) scale</li> <li>Liver_weight: liver weight (g)</li> <li>Droite_gonad_weight : gonad weight (g) of right ovary</li> <li>Gauche_gonad_weight : gonad weight (g) of left ovary</li> <li>Sections: number of cross sections sampled for the individual</li> </ul> </ul> <p> </p> <p><strong>Contact :</strong></p> <p>This dataset was established under the MATO (MATurité Objectif des poissons par l'histologie quantitative) project, during the PhD of Carine Sauger (October 2021-2023), financed by France Fillière Pêche (FFP/2020/AM/MF/109), under the supervision of IFREMER (Institut Français de Recherche pour l'Exploitation de la Mer) and BOREA (Biologie des Organismes et Ecosystèmes Aquatiques), and with the collaboration of a research facility from the University of Caen-Normandie : CMABIO3 (Centre de Microscopie Appliquée à la Biologie). For any enquiries, please contact: carine.sauger@gmail.com or laurent.dubroca@ifremer.fr</p>
CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M04 Sample
<h3><strong>Dataset Description</strong></h3> <p>This dataset contains high-resolution CT-scan images that capture the residual fracture surface within a core sample of Molasse de Villarlod Sandstone. The core sample was extracted after conducting a radial hydraulic fracture experiment on a 25 × 25 × 25 cm cubic block of sandstone (M04 Smaple). The experiment was designed to simulate fluid-driven fracture propagation and closure, and the resulting fracture path was preserved in the core sample.</p> <p><strong>Core Location in the M04 Cube Sample:</strong></p> <ul> <li><strong>Z:</strong> 12.5 cm</li> <li><strong>South-North:</strong> 12.5 cm</li> <li><strong>West-East:</strong> 13.5 cm to 23.3 cm (Coring direction)</li> </ul> <p>This spatial information specifies the exact location and orientation of the core extraction within the M04 cube sample.</p> <h4><strong>CT-scan instrument details:</strong></h4> <p>The M04 sample was analyzed using an X-ray micro-CT scanner (RX-Solutions Ultratom) under consistent scanning protocols and parameters. A reflective 230 kV microfocus X-ray source (Hamamatsu L10801) equipped with a 0.2 mm thick copper filter, a tungsten cathode, and a tungsten target was employed for the imaging process. The scans were conducted with a voltage of 120 kV and a current intensity of 80 mA.</p> <p>The volume data acquisition was performed in continuous helical mode, ensuring complete coverage of the sample’s height. For sample M04, 5 full rotations were executed, with 1312 projections captured for each 360° rotation, allowing for highly precise volume reconstruction. The X-ray beam attenuation was recorded by an XL Varex Paxscan 2530HE plane detector with a resolution of 2176 x 1792 pixels, and an exposure time of 0.50 seconds per projection.</p> <p>The acquired projections were processed using RX-Solutions X-act software with Filtered Backprojection to reconstruct a corrected volume. This reconstruction yielded approximately 9000 slices in 16-bit TIFF format, with voxel dimensions of 10 x 10 x 10 microns, providing detailed insights into the internal structure of the sample.</p> <h4><strong>Key Features:</strong></h4> <ul> <li> <p><strong>Fracture Characteristics</strong>: The fracture observed in the CT-scans represents a residual opening that remains post-fracturation. It is entirely contained within the core, showcasing the internal fracture geometry resulting from the hydraulic fracturing process.</p> </li> <li> <p><strong>CT-Scan Details</strong>: The CT-scans were taken perpendicular to the fracture surface, offering a detailed cross-sectional view of the fracture at different depths. This orientation is critical for accurately capturing the fracture morphology and allows for the reconstruction of the fracture surface in 3D.</p> </li> <li> <p><strong>Material Information</strong>: The core sample is composed of Molasse de Villarlod Sandstone, a sedimentary rock which is porous (18% porosity) and permeable. This material choice is relevant for studying fracture closure subjected to the leak-off of the fluid inside the porous medium.</p> </li> <li> <p><strong>Experimental Context</strong>: The radial hydraulic fracture experiment aimed to simulate the propagation of hydraulic fracture and its closure due to the leakage of fluid inside fracture into the porous medium. The dataset provides valuable insights into fracture propagation patterns, surface roughness, and the effects of fluid-driven fractures in porous media.</p> </li> </ul> <h4><strong>Applications:</strong></h4> <p>This dataset is particularly valuable for researchers and engineers involved in:</p> <ul> <li>Fracture mechanics and surface characterization</li> <li>3D reconstruction and visualization of fracture surfaces</li> <li>Surface roughness analysis</li> <li>Hydraulic fracturing studies</li> <li>Geomechanical modeling</li> </ul> <h4><strong>File Structure:</strong></h4> <p>The dataset is organized into zip-folder contains .tif images corresponding to different depths within the core. Each tif-image is a CT-scan for that specific depth, labeled according to their position along the fracture path.</p> <h4><strong>Processing code:</strong></h4> <p>Follow the <strong>URL repository</strong> in the software section to access to the code for processing these images and reconstructing the fracture surfaces.</p> <p><strong>Acknowledgment:</strong></p> <p>We would like to extend our deepest thanks to Gary Perrenoud, Albert Taureg, and Lionel Pittet, the technical specialists of the PIXE platform at École Polytechnique Fédérale de Lausanne (EPFL). Their expertise and support in operating the CT-scan machine were important to the success of this research. We greatly appreciate their dedication and the high-quality work they provided.</p> <p><strong>Contact and Support:</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
Dataset related to article "Distribution of pamiparib, a novel inhibitor of poly(ADP-ribose)-polymerase (PARP), in tumor tissue analyzed by multimodal imaging"
<p>This record contains data related to article "Distribution of pamiparib, a novel inhibitor of poly(ADP-ribose)-polymerase (PARP), in tumor tissue analyzed by multimodal imaging"</p> <p><span>Pamiparib is a potent and selective oral PARP1/2 inhibitor (PARPi). Pamiparib has good bioavailability and showed greater cytotoxic potency and similar DNA-trapping capacity compared to olaparib. It is not affected by ATP-binding cassette transporters. Consequently, pamiparib may be useful in overcoming drug resistance caused by poor drug distribution in tumor due to overexpression of these efflux pump [1]. Mass spectrometry imaging (MSI) is a powerful technology that allows to study drugs distribution in tissues while maintaining spatial information [2]. Here, MSI was applied to visualize pamiparib in tumor in combination with spatial metabolomics and lipidomics, LC-MS/MS analysis, immunofluorescence analysis, and histological staining to gain a comprehensive understanding of how pamiparib is distributed. The results show that pamiparib was evenly distributed in ovarian tumor models, including those that overexpress P-glycoprotein (P-gp). In contrast, olaparib was not detected by MSI in any of the analyzed tumors, despite the comparable sensitivity of the analytical method. This difference in tumor distribution was confirmed by LC-MS/MS analysis. </span></p>
CHOWNET: An Image Dataset of Nigerian Food
<p>CHOWNET-V1 is a high-quality dataset consisting of 118 human-annotated food images, specifically curated for multi-label classification, food object detection, and food captioning tasks. The dataset includes 99 unique labels, serving as a valuable resource for a range of computer vision challenges within the food domain.</p> <p><br>Github Link: <a href="https://github.com/AISaturdaysLagos/chownet">https://github.com/AISaturdaysLagos/chownet</a></p> <p>Data Annotation for CHOWNET-V1 was led by: <a href="https://www.linkedin.com/in/tejumadeafonja/">Tejumade Afonja</a> and <a href="https://www.linkedin.com/in/george-igwegbe/">George Igwegbe</a></p> <p>This dataset was contributed by the AI Saturdays Lagos community in 2018.</p> <p> </p> <blockquote> <p>The dataset structure is described in About.txt</p> </blockquote>
(09)-Pereyra2021A-DS0001--DS0003 – Three Tribolium castaneum long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy
<p>(09)-Pereyra2021A-DS0001--DS0003 – Three <em>Tribolium castaneum</em> long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy</p>
Dataset for the publication "Identification of plasticity-induced crack closure by using high-resolution digital image correlation"
<p>This repository publishes the data generated in the article "Identification of plasticity-induced crack closure by using high-resolution digital image correlation" (see arxiv preprint <a href="https://arxiv.org/html/2409.02560v1">Plasticity-induced crack closure identification during fatigue crack growth in AA2024-T3 by using high-resolution digital image correlation (arxiv.org)</a>)</p> <p>This repository is structured with the following subfolders:</p> <ul> <li><strong>0_fe_data: </strong>contains the displacement field of the free surface of the 3D finite element model that were used to determine the crack opening curves and, in the following, the crack opening value Kop</li> <li><strong>1_hrdic_data: </strong>contains the high-resolution DIC displacement field data at a crack length of 27.8 mm at different load levels, starting from minimum load 1.5 kN to maximum load 15 kN</li> </ul>
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.