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162 results for “Test Images”
CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading
<p>Set of CZI test images created by using a simulated microscope with a test grayscale camera (no LSM or AiryScan or RGB). The filename indicates the used dimension(s) for the acquisition experiment. The files can be used to test the basic functionality of libraries reading CZI files.</p> <p>Examples:</p> <ul> <li>S=2_T=3_CH=1.czi = 2 Scenes, 3 TimePoints and 1 Channel <ul> <li>Z-Stack <strong>was not</strong> activated inside acquisition experiment</li> </ul> </li> <li>S=2_T=3_Z=5_CH=2.czi = 2 Scenes, 3 TimePoints, 5-Z-Planes and 1 Channels <ul> <li>Z-Stack <strong>was </strong>activated inside acquisition experiment</li> </ul> </li> </ul> <p>The test files (so far) contain not any data with more "advanced" dimensions like AiryScan rawdata, illumination angles etc. Also no CZI files with pixel type RGB are included yet.</p> <p> </p> <p> </p> <p> </p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Whole slide images of mouse liver serial sections - Test registration dataset
<p>15 H&E serial section of mouse liver and small intestine.</p> <p>Sampled prepared in the <a href="https://www.epfl.ch/research/facilities/histology-core-facility/">EPFL histology core facility</a> by Nathalie Müller, Gian-Filippo Mancini, and Agnès Hautier.</p> <p>All slides where imaged with a VS200 Evident slide scanner from the<a href="http://biop.epfl.ch/"> EPFL BIOP imaging facility</a>.</p>
Dataset of Cervical Cell Images for the Study of Changes Associated with Malignancy in Conventional Pap Test
<p>This data set was approved by CEP, a human-research ethics committee {Comitê de Ética em Pesquisa de Campinas}, Brazil <em>(CAAE approval number: 71277217.6.0000.5404) </em></p> <p>Cervical cancer prevention campaign was carried out at the University of Campinas (Brazil), where 71 women were treated. For this study, six women were chosen: non-pre-menopausal, non-pregnant and between the ages of 25 and 40 years. Samples diagnosed with normal squamous lesions, atypia in squamous cells of undetermined significance (AC-US), low-grade squamous intraepithelial lesion LEI (changes associated with HPV infection or light dislocation (NIC 1)), and immature squamous metaplasia, were chosen. All samples were collected before treatment. The patients were anonymized and assigned a unique identifier. The criterion to limit the number of plates used in this study is related to the large amount of data to be processed and the computational cost necessary for this process.</p> <p>Raw data sets are available as CSV files. Each data was numbered to include the identification of the meta-data, along with the tag of the images following a sequence. For example, the data file<em> “ 01-CAP091868-MORF.csv ”</em> has the prefix 01 that refers to the sequential numbering of the files with the data from the digitized slide.</p> <p>The data set contains 102 digitized images of the human papilloma examination of 7 different patients. They were segmented 962</p>
Global Wetlands: Luderick Seagrass Dataset - Test Set Image Patches
<p>This dataset is a test dataset of image patches created from the 'novel-test' split of the Global Wetlands Luderick-Seagrass dataset. The original images were divided as a grid into 50 image patches. The image patches were manually labeled into 'Background', 'Fish' and 'Seagrass' sets. The images were otherwise unaltered. </p> <p>We contribute this test dataset of underwater image patches to facilitate evaluation of coarse segmentation seagrass methods.</p> <p>Original dataset description: "This dataset comprises of annotated footage of Girella tricuspidata in two estuary systems in South East Queensland, Australia. This data is suitable for a range of classification and object detection research in unconstrained underwater environments."</p> <p>Original dataset citation: Ditria, Ellen M; Connolly, Rod M; Jinks, Eric L; Lopez-Marcano, Sebastian (2021)<strong>:</strong> Annotated video footage for automated identification and counting of fish in unconstrained marine environments. <em>PANGAEA</em>, <a href="https://doi.org/10.1594/PANGAEA.926930">https://doi.org/10.1594/PANGAEA.926930</a>.</p> <p>The original dataset is available at: <br> https://github.com/globalwetlands/luderick-seagrass<br> https://download.pangaea.de/dataset/926930/files/Fish_automated_identification_and_counting.zip<br> https://globalwetlands.blob.core.windows.net/globalwetlands-public/datasets/luderick-seagrass/luderick-seagrass.zip</p>
Consensual videos of potentially re-identifiable individuals recorded at the Autonomous Driving Test Area Baden-Württemberg (raw images with location and IMU data).
<p>For the purpose of research on data intermediaries and data anonymisation, it is necessary to test these processes with realistic video data containing personal data. For this purpose, the <a href="http://treumoda.de">Treumoda</a> project, funded by the German Federal Ministry of Education and Research (BMBF), has created a dataset of different traffic scenes containing identifiable persons.</p> <p>This video data was collected at the <a href="https://taf-bw.de/">Autonomous Driving Test Area Baden-Württemberg</a>. On the one hand, it should be possible to recognise people in traffic, including their line of sight. On the other hand, it should be usable for the demonstration and evaluation of anonymisation techniques.</p> <p><strong>The legal basis for the publication of this data set the consent given by the participants as documented in the file Consent.pdf (all purposes) in accordance with Art. 6 1 (a) and Art. 9 2 (a) GDPR. Any further processing is subject to the GDPR.</strong></p> <p>We make this dataset available for non-commercial purposes such as teaching, research and scientific communication. Please note that this licence is limited by the provisions of the GDPR. Anyone downloading this data will become an independent controller of the data. This data has been collected with the consent of the identifiable individuals depicted.</p> <p>Any consensual use must take into account the purposes mentioned in the uploaded consent forms and in the privacy terms and conditions provided to the participants (see Consent.pdf). All participants consented to all three purposes, and no consent was withdrawn at the time of publication. KIT is unable to provide you with contact details for any of the participants, as we have removed all links to personal data other than that contained in the published images.</p>
Test Dataset of AI Altered Images for Photogrammetric Reconstruction
<p>As a part of a test of the capability of using AI altered images in photogrammetry, I generated these image sets using the Prisma AI app. All of these datasets will result in a model in Agisoft Metashape. Prisma AI acts as a filter, changing an input image to the style of another artwork. </p> <p>Original images were collected using a Samsung S22 camera on the 1x zoom rear lens. The subject is a rock decoration in the Utrecht University Botanical Garden.</p> <p>Results can be seen on Sketchfab: https://sketchfab.com/gspeed0689/collections/prisma-photogrammetry-2e334a36c6e942798370d594de5e6f0c </p> <p>Included datasets:</p> <ul> <li>true-color.zip - The original images from the Samsung S22</li> <li>aviator-lowres - Processed images at normal (low resolution) quality with the filter labelled Aviator</li> <li>bubblegum-lowres - Processed images at normal (low resolution) quality with the filter labelled Bubblegum</li> <li>gothic-lowres - Processed images at normal (low resolution) quality with the filter labelled Gothic</li> <li>gothic-hd.zip - Processed images at HD (paid subscription required) quality with the filter labelled Gothic</li> <li>true-color.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from true-color.zip</li> <li>aviator-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from aviator-lowres.zip</li> <li>bubblegum-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from bubblegum-lowres.zip</li> <li>gothic-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from gothic-lowres.zip</li> <li>gothic-hd.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from gothic-hd.zip</li> </ul> <p>Processing parameters in Agisoft Metashape v 1.7:</p> <ul> <li>Alignment defaults</li> <li>Dense cloud creation at "High" quality</li> <li>Mesh defaults</li> </ul>
Consensual videos of potentially re-identifiable individuals recorded at the Autonomous Driving Test Area Baden-Württemberg (raw images recorded daytime).
<p>For the purpose of research on data intermediaries and data anonymisation, it is necessary to test these processes with realistic video data containing personal data. For this purpose, the <a href="http://treumoda.de/">TreuMoDa</a> project, funded by the German Federal Ministry of Education and Research (BMBF), has created a dataset of different traffic scenes containing identifiable persons.</p><p>This video data was collected at the <a href="https://taf-bw.de/">Autonomous Driving Test Area Baden-Württemberg</a>. On the one hand, it should be possible to recognise people in traffic, including their line of sight. On the other hand, it should be usable for the demonstration and evaluation of anonymisation techniques.</p><p><strong>The legal basis for the publication of this data set the consent given by the participants as documented in the file Consent.pdf (all purposes) in accordance with Art. 6 1 (a) and Art. 9 2 (a) GDPR. Any further processing is subject to the GDPR.</strong></p><p>We make this dataset available for non-commercial purposes such as teaching, research and scientific communication. Please note that this licence is limited by the provisions of the GDPR. Anyone downloading this data will become an independent controller of the data. This data has been collected with the consent of the identifiable individuals depicted.</p><p>Any consensual use must take into account the purposes mentioned in the uploaded consent forms and in the privacy terms and conditions provided to the participants (see Consent.pdf). All participants consented to all three purposes, and no consent was withdrawn at the time of publication. KIT is unable to provide you with contact details for any of the participants, as we have removed all links to personal data other than that contained in the published images.</p>
Data from: SLICE-MSI: A machine learning interface for system suitability testing of mass spectrometry imaging platforms
Open the record for dataset details and reuse information.
Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition
<p>This data contain multisensor image dataset constructed for benchmarking purposes. It contains multiple images of the constructed scenes -- on which objects made of different materials are placed to test image recognition scenarios. The scene is recorded from various angles by imagining sensors, i.e. HSI camera, HD camera on mobile chassis and MS Kinect to provide complete information.<br> </p> <p><strong>Equipment</strong><br> The imaging was performed with use of three devices for three different approaches to data. Those three devices' imaging characteristics are widely different when it comes to angle and resolution which required them to be separately positioned to acquire the matching images. Therefore while HD camera was being transferred on the moving platform (chassis), both Kinect and SOC710 were placed on a stationary position which was moved between the frames by hand.</p> <p><em>Hyperspectral data</em></p> <p><br> Hyperspectral data acquisition was performed with Surface Optics SOC710 camera. This camera records spectra at VNIR range $377-1046$ nm; the output image has dimensions $696 \times 520$ with 128 bands and $12$ bit dynamic range.</p> <p>The camera is equipped with sensor line translation unit and can be used from static stand as a conventional camera (i.e. it does not require mechanical translation of the observed sample or rotary stand, as in traditional ‘push broom’ hyperspectral cameras). The lighting was provided with four ambient lamps and adjusted for each scenario separately, so that most of the dynamic range of the camera was used and image saturation is avoided. Captured hyperspectral images were subject to a standard calibration procedure, including: the removal of a dark frame, spectral and radiometric calibration as well as reflectance normalization using the calibration panel. </p> <p><em>3D point clouds</em></p> <p><br> The Kinect sensor incorporates several advanced sensing hardware. The depth sensor consists of the IR projector combined with the IR camera, which is a monochrome complementary metaloxide semiconductor (CMOS) sensor. The IR projector is an IR laser that passes through a diffraction grating and turns into a set of IR dots. The relative geometry between the IR projector and the IR camera as well as the projected IR dot pattern are known. If we can match a dot observed in an image with a dot in the projector pattern, we can reconstruct it in 3D using triangulation. Because the dot pattern is relatively random, the matching between the IR image and the projector pattern can be done in a straightforward way by comparing small neighborhoods using, for example, normalized cross correlation. The depth value is encoded with gray values; the darker a pixel, the closer the point is to the camera in space. The black pixels indicate that no depth values are available for those pixels. This might happen if the points are too far (and the depth values cannot be computed accurately), are too close (there is a blind region due to limited fields of view for the projector and the camera), are in the cast shadow of the projector (there are no IR dots), or reflect poor IR lights </p> <p><em>HD Images</em></p> <p><br> The HD images were acquired using 5 Megapixel HD camera mounted on a mobile chassis made by Dawn Robotics, that allowed the camera to be moved freely on the scene. Both camera and mobile chassis was controlled by a Raspberry PI unit which was also responsible to position the camera in accord to the data being collected by other sources. <br> </p> <p><strong>Data</strong></p> <p>The dataset consists of three scenes consisting of various objects -- minerals, fruit, wood plastic and metal -- placed on a stand. The objects, depending on the view are partially covered and seen from different perspective. Each scene is captured from 8 different angles.</p> <p>Scene 1 (denoted <em>SceneEagle</em>) uses mostly inorganic materials, such as wood, metal, plastic and glass all placed on the vertical stand.<br> Scene 2 (<em>SceneFruit</em>) uses fruits normal and artificial, that are similar on HD photography and 3D cloud of point, but differs in hyperspectral image.<br> Scene 3 (<em>SceneFruit2</em>) uses the fruits but also includes printed full colour images of same fruits that are 2-dimensional.</p> <p> </p> <p>The data are formatted as follows:<br> - The HIS images are available in both \text{*.hdr} and \text{*.cube} formats. The separate files with calibrating panel is provided for each frame.<br> - Kinect clouds are provided in \text{*.obj} format, typical for Kinect output files.<br> - Matched Hyperspectral clouds are also provided as \text{*.obj} files<br> - HD photo files are provided in \text{*.jpg} files.<br> </p> <p><br> <strong>Acknowledgements</strong></p> <p>This work has been supported by the National Science Centre, based on decision no. DEC2012/07/N/ST6/03656.</p> <p> </p>
Speckle images from biaxial test on PMMA
<p>These images were captured during a biaxial test on a PolyMethylAcrylate cruciform sample. The actuators were driven based on the strain measured during the test using GPU integrated DIC.</p> <p>The speckle pattern was made by spraying black paint using an airbrush on a white coat of paint.</p> <p>The camera is a Ximea XiB (CB500MG-CM), the lens a canon EF 100mm, pixel size is 4.51 µm/pix</p> <p>The name of the images is "img_{count}_{time}.tiff" where {count} is the number of processed images {time} the instant the image was captured after the beginning of the test in seconds. The average time between two saved image 5.21s but is not guaranteed to be constant (std = 0.0364s). img_ref_{time}.tiff is the reference image used by the DIC algorithm.</p> <p>Not all indexes are available because only 1/100 images were saved for post-processing to limit the volume of data.</p> <p>This set of image can be used in many ways, including as a benchmark for comparing DIC algorithms. Since they are real-world images, the "real" displacement is not available.</p> <p>correl.zip contains a csv file with the results of the real-time integrated DIC used during the test. Because a few images were dropped due to limitations, make sure to use the timestamp to match the images and the csv.</p>
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>
PySeq2500 Test Images
<p>Raw TIFF images of 4i experiment from a PySeq</p> <p>[sections]</p> <p>mouse G93A m388 p100<br>m1a = A: 15, 19, 11.5, 16.5<br>m3b = B: 14, 55, 11, 32.5</p> <p>[cycles]<br>1stab 1 = GFAP_594<br>1stab 2 = ELAVL2_700<br>1stab 3 = Laminin1b_532<br>1stab 4 = Mbp_Cy5<br>1stab 5 = primary_mix<br>2ndab 1 = ch594<br>2ndab 2 = rAF700<br>2ndab 3 = mAF532<br>2ndab 4 = huCy5<br>2ndab 5 = secondary_mix</p>
JCURA 2024 PyLC Image Testing Set and Landcover Masks
<p>This is the image and landcover mask testing set created for the 2024 JCURA project "Mountains of Confusion: Evaluating Image Enhancement to Improve AI Landscape Classification" by Larissa Bron. </p> <p>The Python Landscape Classification tool (PyLC) [https://github.com/scrose/pylc] was trained using 95 images from two researcher's work, [Fortin (2018)](https://dspace.library.uvic.ca/items/0a911eb0-53bf-4a82-a75a-8b6949c28edd) and [Jean et al. (2015)](https://ieeexplore.ieee.org/document/7045940), and tested with 19 images that were a combination of 11 images from Fortin and Jean withheld from training and 8 images from the Landscapes in Motion project [(Higgs et al., (2020))](https://friresearch.ca/publications/advances-visual-applications-visualizing-quantifying-landscape-change-sw-alberta-using). This core set of training and testing images was used to train the colour models of PyLC with repeat images, then these images were grayscaled and added to training the grayscale models of PyLC with historic images. </p> <p>For this JCURA project, the testing image set was updated to: 1) Incorporate images from more geographic areas, and 2) Remove images with reference land cover masks with large errors. </p> <p>The files included are 24 test images (.jpg, .tiff, .tif) and 24 manually annotated land cover masks (.png). File names indicate: Researcher_ImageIdentifier_Time where time is whether the image is a historic capture or a repeat. </p>
Data to "Cardiopulmonary exercise testing complements both spirometry and nuclear imaging for assessing sarcoidosis disease stage and for monitoring disease activity"
<p>This record contains analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Torregiani, C., Reale, M., Confalonieri, M., Dore, F., Crisafulli, C., Baratella, E., ... & Maiello, G. (2024). Cardiopulmonary exercise testing complements both spirometry and nuclear imaging for assessing sarcoidosis stage and for monitoring disease activity. <em>Sarcoidosis, Vasculitis, and Diffuse Lung Diseases</em>, 41(1), e2024017-e2024017. https://doi.org/10.36141/svdld.v41i1.15125</p>
In situ plankton footage for testing image processing routines
<p>This dataset contains 12 video files collected from the deep-focus plankton imager (DPI) version of the in situ ictyoplankton imaging system (ISIIS). These files are intended to be used for testing, validating, and comparing computational pipelines. These videos were collected in the Northern Gulf of Alaska as part of an NSF-sponsored field campaign (NGA LTER).</p>
TRPV1 calcium imaging assay data from testing of capsaicinoids
<p>irTRPV1 calcium imaging assay data collected in HTS format included legend to files, raw data, feature files with description, aligned data and final GraphPad file. Correspoinding paper's DOI will be added once generated.</p>
Annotated pixels from brightfield images from yeast cell in microfluidic traps - Training and Test sets - DetecDiv (id02)
<p>This dataset has been generated by manual segmentation from timelapse images of yeast cells dividing using the DetecDiv software (see below).</p> <p>It contains ~1950 raw images (folder "images") associated with their mask (folder "labels") with 3 classes of pixels according to their value on the mask image, "1. background", "2. mother", "3. other".</p> <p>A splitSets.mat indicates which images have been used for training or for testing.</p> <p>It is related to the trained network <a href="https://doi.org/10.5281/zenodo.5553851">doi.org/10.5281/zenodo.5553851</a> from the software DetecDiv: github.com/gcharvin/DetecDiv</p> <p><a href="https://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a></p> <p>-------------------------------------------------------</p> <p><strong>Data type</strong>: 2D microscopy images (brightfield) (.tif) + mask image (.tif)</p> <p><strong>Microscopy data type</strong>: Brightfield images</p> <p><strong>Imaging</strong>: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS</p> <p><strong>Cell type</strong>: Budding yeast wild type cell (BY4742)</p> <p><strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack) + .mat</p> <p><strong>Image size</strong>: Brightfield: 60x60x1 (Pixel size: x,y: 325 nm) // Mask: 60x60x1 (Pixel size: x,y: 325 nm), 3 pixel values (one per class).</p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Test Image Set for Pyramid Generation
<p>A set of 7 images for a test of pyramid generation of the OMERO.server</p> <p>Varying sizes and file formats</p>
Testing a new optical strain gage for full-field strain measurement: Raw images
<pre>This dataset contains images obtained during two different tests performed to assess the response of a new optical strain gage developped for full-field strain measurement.</pre> <pre><strong>File contents:<br></strong> - SMA: Folder containing images obtained with a SMA specimen<br> - SMA_Paint_Ref: contains 100 images in the reference state. These were taken with the optimal parameters for the painted and engraved half (lower half in the images).<br> - SMA_Paint_Def: contains 100 images in the deformed state. <br> - SMA_Gage_Ref: contains 100 images in the reference state. These were taken with the optimal parameters for the gage (upper half in the images).<br> - SMA_Gage_Def: contains 100 images in the deformed state. <br> - Wood: Folder containing images obtained with a wood specimen<br> - Wood_Paint_Ref: contains 100 images in the reference state.<br> - Wood_Paint_Def: contains 100 images in the deformed state. <br> - Wood_Gage_Ref: contains 100 images in the reference state.<br> - Wood_Gage_Def: contains 100 images in the deformed state. <br><br>These images can be processed with the Python code available in the OpenLSA library: https://gitlab.ip.uca.fr/expmech/openlsa .</pre> <p>An example is provided in this library.</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.