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979 results for “Image Dataset”
Global ML-ready dataset for mining areas in satellite images
<p>This dataset is a global resource for machine learning applications in mining area detection and semantic segmentation on satellite imagery. It contains Sentinel-2 satellite images and corresponding mining area masks + bounding boxes for 1,210 sites worldwide. Ground-truth masks are derived from <a href="https://doi.org/10.1594/PANGAEA.942325" target="_blank" rel="noopener">Maus et al. (2022)</a> and <a href="https://doi.org/10.5281/zenodo.6806817" target="_blank" rel="noopener">Tang et al. (2023)</a>, and validated through manual verification to ensure accurate alignment with Sentinel-2 imagery from specific timestamps. </p> <p>The dataset includes three mask variants:</p> <ul> <li>Masks exclusively from Maus et al. (n=1,090)</li> <li>Masks exclusively from Tang et al. (n=817)</li> <li>A preferred mask selected from either Maus or Tang based on alignment quality determined during manual review (n=1,210).</li> </ul> <p>Each tile corresponds to a 2048x2048 pixel Sentinel-2 image, with metadata on mine type (surface, placer, underground, brine & evaporation) and scale (artisanal, industrial). For convenience, the preferred mask dataset is already split into training (75%), validation (15%), and test (10%) sets. </p> <p>Furthermore, dataset quality was validated by re-validating test set tiles manually and correcting any mismatches between mining polygons and visually observed true mining area in the images, resulting in the following estimated quality metrics: </p> <table> <tbody> <tr> <td> </td> <td>Combined</td> <td>Maus</td> <td>Tang</td> </tr> <tr> <td>Accuracy</td> <td>99.78</td> <td>99.74</td> <td>99.83</td> </tr> <tr> <td>Precision</td> <td>99.22</td> <td>99.20</td> <td>99.24</td> </tr> <tr> <td>Recall</td> <td>95.71</td> <td>96.34</td> <td>95.10</td> </tr> </tbody> </table> <p>Note that the dataset does not contain the Sentinel-2 images themselves but contains a reference to specific Sentinel-2 images. Thus, for any ML applications, the images must be persisted first. For example, Sentinel-2 imagery is available from Microsoft's Planetary Computer and filterable via STAC API: <a href="https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a" target="_blank" rel="noopener">https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a</a>. Additionally, the temporal specificity of the data allows integration with other imagery sources from the indicated timestamp, such as Landsat or other high-resolution imagery.</p> <p>Source code used to generate this dataset and to use it for ML model training is available at <a href="https://github.com/SimonJasansky/mine-segmentation" target="_blank" rel="noopener">https://github.com/SimonJasansky/mine-segmentation</a>. It includes useful Python scripts, e.g. to <a href="https://github.com/SimonJasansky/mine-segmentation/blob/main/src/data/05_persist_pixels_masks.py">download Sentinel-2 images via STAC API</a>, or to <a href="https://github.com/SimonJasansky/mine-segmentation/blob/main/src/data/06_make_chips.py">divide tile images (2048x2048px) into smaller chips (e.g. 512x512px)</a>. </p> <p>A database schema, a schematic depiction of the dataset generation process, and a map of the global distribution of tiles are provided in the accompanying images. </p>
Product Images of Life Cycle Assessment Dataset For Peritoneal Dialysis in Warsaw, Poland
<p>The database contains a collection of images showcasing the individual components of peritoneal dialysis (PD) products, along with their corresponding weights. These images serve as a visual record for life cycle assessment (LCA) purposes, focusing on the material composition and environmental impact of each product.</p> <ol> <li> <p><strong>Patient Education Materials</strong>: Photographs of educational materials provided to patients, with accompanying data on the weight of the paper and packaging.</p> </li> <li> <p><strong>Catheters and Surgical Kits</strong>: Images display the disassembled components of PD catheters and surgical kits, including tubing, connectors, and packaging. Each image is annotated with the precise weight of the individual components.</p> </li> <li> <p><strong>Dialysis Solution Bags</strong>: The database includes images of both CAPD and APD solution bags, separated into their constituent parts (e.g., plastic bag, solution, and protective wrapping), with weights noted for each component.</p> </li> <li> <p><strong>Connection Devices and Consumables</strong>: Detailed images of connection devices, clamps, and other consumable items, with individual component weights clearly labeled.</p> </li> <li> <p><strong>Packaging and Transport Materials</strong>: Photographs of transport packaging, such as cardboard boxes and plastic wraps, alongside recorded weights for each element.</p> </li> <li> <p><strong>Maintenance Items</strong>: Visuals of terminal catheter sets, cleaning agents, and related products, each accompanied by their respective weight data.</p> </li> <li> <p><strong>Disposal Components</strong>: Images of used solution bags, syringes, and other single-use items, separated into recyclable and non-recyclable components, with weights specified for each.</p> </li> </ol> <p>This image-based database provides a clear and comprehensive reference for the material breakdown and weight distribution of PD product components, essential for conducting a thorough LCA and identifying areas for environmental improvement.</p>
Dataset and software for processing of hyperspectral images of different CDW materials
<h2>Overview</h2> <p>The provided scripts are designed to process hyperspectral images of construction and demolition waste (CDW) materials, extract relevant features, and train a machine-learning model for material classification. The scripts perform the following tasks:</p> <ol> <li><strong>Feature Extraction</strong>: Extract spectral features from hyperspectral data.</li> <li><strong>Background Removal and Subset Extraction</strong>: Remove backgrounds from images and extract subsets for analysis.</li> <li><strong>Data Visualization</strong>: Generate plots to visualize the extracted features and reflectance curves.</li> <li><strong>Machine Learning Model Training</strong>: Using the extracted features, train and evaluate a multilayer perceptron (MLP) classifier.</li> </ol> <h2>Prerequisites</h2> <p>Before running the scripts, ensure that you have the following:</p> <ul> <li><strong>Python 3.x</strong> installed on your system.</li> <li>Required Python packages: <ul> <li><code>numpy</code></li> <li><code>matplotlib</code></li> <li><code>scipy</code></li> <li><code>pandas</code></li> <li><code>scikit-learn</code></li> <li><code>seaborn</code></li> <li><code>rembg</code> (for background removal)</li> <li><code>Pillow</code> (PIL)</li> </ul> </li> <li><strong>Hyperspectral data files</strong> in <code>.mat</code> format containing calibrated hyperspectral cubes and wavelength information.</li> <li>A directory structure to organize input and output files as described in each script.</li> </ul> <h2>Scripts Description</h2> <h3>1. <code>hyperspectral_features_v2.py</code></h3> <h4><strong>Purpose</strong></h4> <p>This script processes individual hyperspectral image files to extract spectral features from a central subset of the image. It generates RGB images from the hyperspectral data, plots the mean reflectance spectra, and outputs a LaTeX-formatted table containing the extracted features.</p> <h4><strong>Functionality</strong></h4> <ul> <li><strong>Loading Data</strong>: Reads <code>.mat</code> files containing hyperspectral data from a specified input directory.</li> <li><strong>Feature Calculation</strong>: <ul> <li>Calculates mean reflectance within a central window of the image.</li> <li>Extracts spectral features such as peak wavelength and area under the reflectance curve.</li> <li>Records reflectance values at selected wavelengths, including standard RGB channels and additional wavelengths.</li> </ul> </li> <li><strong>RGB Image Generation</strong>: Creates RGB images using specific wavelengths corresponding to the red, green, and blue channels.</li> <li><strong>Spectra Plotting</strong>: Plots the mean reflectance spectra for each sample.</li> <li><strong>LaTeX Table Generation</strong>: Produces a LaTeX-formatted table of the extracted features for inclusion in a report or paper.</li> </ul> <h4><strong>Usage Instructions</strong></h4> <ol> <li> <p><strong>Prepare Input Data</strong>:</p> <ul> <li>Place your <code>.mat</code> files containing the hyperspectral data in the appropriate input directory (e.g., <code>input/mortar</code>).</li> </ul> </li> <li> <p><strong>Run the Script</strong>:</p> <ul> <li>Modify the <code>materials</code> list at the end of the script to include the materials you want to process (e.g., <code>materials = ['mortar']</code>).</li> <li>Execute the script: <div> <div>bash</div> <div> <div> <div> </div> </div> </div> </div> </li> </ul> </li> </ol>
Dataset for the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars."
<p>This dataset supports the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars." The dataset is contained in a single CSV file with 201 data rows (one row per NASA Curiosity rover ChemCam instrument target used in the study). The columns in this dataset include the martian solar day (sol) on which each target was imaged by ChemCam; the standoff distance from ChemCam to each target (in meters); binary columns (values are either 1 or 0, indicating presence or absence, respectively) for each of the 17 visual attributes we documented for each target image; the corresponding greyscale ChemCam RMI mosaic file location (on the Planetary Data System); and columns indicating which group each target was sorted into under each classification algorithm discussed in the text (P_{SG}: simple graph method; P_{AP}: automatic partitioning method; P_{\lambda=1.6}: community detection method with \lambda=1.6). To obtain the binary strings used for the classification algorithms, the 17 visual attribute columns can be concatenated. </p> <p>Also included is a collection of HTML files that enables easy viewing of the RMI mosaics in each cluster, using the Planetary Data System links. To use it, download the <code>.zip</code> file, unzip it, and open the <code>index.html</code> file in the browser of your choice (likely will work to simply double-click <code>index.html</code>)</p>
Microscopy Imaging Dataset: Trypanosoma brucei Bloodstream Form Classification Using Deep Learning
<p>This dataset provides a comprehensive collection of microscopic images and associated labels, specifically designed to facilitate the automated classification of <em>Trypanosoma brucei</em> bloodstream forms—slender and stumpy. Accurate differentiation of these life cycle stages is vital for understanding the parasite's biology, transmission dynamics, and adaptation mechanisms in its mammalian host.</p> <p><strong>Contents:</strong></p> <ul> <li><strong>Image Data</strong>: Microscopic images of <em>T. brucei</em> bloodstream forms captured under standard imaging conditions, encompassing a broad array of image quality, cellular arrangements, and morphological characteristics.</li> <li><strong>Label Data</strong>: Annotation files for each image, specifying cellular forms as slender or stumpy, essential for supervised machine learning applications.</li> <li><strong>Supplementary Files</strong>: Additional Excel files providing information on training, testing, and validation splits, alongside test results for model evaluation.</li> </ul> <p><strong>Purpose:</strong></p> <p>This dataset serves as a valuable resource for researchers in parasitology, machine learning, and computational biology. It supports investigations into the biology and life cycle of <em>T. brucei</em>, while also providing a robust testbed for developing, validating, and benchmarking image processing and classification algorithms tailored to parasite morphology.</p> <p><strong>Data Collection and Methodology:</strong></p> <p>The dataset was compiled using advanced deep learning techniques, integrating the Cellpose segmentation algorithm with a custom-trained Xception model optimized for classifying <em>T. brucei</em> forms. The model achieved 97% classification accuracy, demonstrating effective application in handling complex cell images and distinguishing between slender and stumpy forms in challenging imaging conditions.</p> <p><strong>Usage:</strong></p> <p>Researchers are encouraged to use this dataset to:</p> <ul> <li>Analyze and classify the life cycle stages of <em>T. brucei</em> bloodstream forms in microscopic images.</li> <li>Develop and test deep learning models for single-cell image segmentation and classification.</li> <li>Explore cellular morphology patterns and refine machine learning approaches for other single-cell imaging applications.</li> </ul> <p><strong>Citation:</strong></p> <p>Please cite the original dataset if you utilize this resource in your research to acknowledge its contribution to the field.</p> <p><strong>Access and Availability:</strong></p> <p>This dataset is openly available through Zenodo, enabling researchers to download, explore, and apply it in various fields, from parasitology to advanced computational biology.</p>
Dataset: Planetary-scale waves seen in thermal infrared images of Venusian cloud top
<p>The data archive contains data used in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al.</p> <p>The contents of the directories are as follows.</p> <p>"data_used_in_figures" : The table data used in the figures are given in Excel and CSV. The table format is described in the data files. An image in NetCDF format is also included.</p> <p>"time_series_of_brightness_temperature_gradient" : The files contain the time series of the longitudinal gradient of Venusian cloud's brightness temperature measured by LIR onboard JAXA's Venus orbiter Akatsuki. The data were derived and analyzed in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The filename represents the latitude for each time series (For example, "10N" means 10 degrees north, and "EQ" means the equator). In all files, the first column gives the approximate elapsed time in days from 18 May 2017: the exact dates are given in the paper (Table S1 in the Supporting Information). The second column gives the longitudinal gradient of the brightness temperature in unit of K/degree.</p>
An Image Dataset for Training Deep Learning Segmentation Models to Identify Karst Sinkholes
<p>The image dataset was prepared for training deep learning image segmentation models to identify karst sinkholes. Information about the work can be found at (https://github.com/mvrl/sink-seg/). The dataset consists of a DEM image, an aerial image, and a binary sinkhole label image in an area in central Kentucky, USA. It also includes four images derived from the DEM image. The image dataset is sourced from publicly available data from Kentucky's Elevation Data & Aerial Photography Program (https://kyfromabove.ky.gov/) and Kentucky LiDAR-derived sinkholes (https://kgs.uky.edu/geomap).</p> <p> </p>
Dataset: HRSTEM Images of Defective and Non-Defective Quasi-Periodic Materials
<p>This is the image dataset and model used to produce the results reported in the following publication: <br> Dennler, N., Foncubierta-Rodriguez, A., Neupert, T., Sousa, M. (2021). <em>Learning-based defect recognition for quasi-periodic HRSTEM images</em>. Micron, 146(July 2020), 103069. https://doi.org/10.1016/j.micron.2021.103069<br> <br> For questions, please correspond with N. Dennler (n.dennler2<strong> </strong>at<strong> </strong>herts.ac.uk) or with M. Sousa (sou at zurich.ibm.com).</p> <p><strong>hrstem_defects_dataset.zip:</strong> These are the images and labels used to develop and test the algorithm proposed in the above-mentioned publication. They correspond to high resolution scanning transmission electron microscopy images obtained for various III-V films, namely InP, GaAs, InGaAs and InAlGaAs using a JEOL ARM200F microscope. The raw images have been converted in .tif format with the GMS 3 program from Digital Micrograph. The labels have been created by a microscopy expert. Black: main crystal symmetry (non-defective). Gray: secondary crystal symmetry (symmetry defect). White: blurred (amorphous region or beam defect)<br> <br> <strong>vgg16.zip: </strong>The trained neural network model as well as a detailed description of the training/testing dataset that was used to achieve the results reported in the above-mentioned publication.</p>
Imaging Mass Cytometry Dataset of exhausted and non-exhausted breast cancer microenvironments
<p>A cohort of human breast tumor samples were annotated as having an "exhausted" or "non-exhausted" immune environment based on CyTOF characterization of T cell phenotypes (see Wagner et al. 2019). 12 samples (6 exhausted, 6 non-exhausted) were then selected for further analysis by Imaging Mass Cytometry (IMC) with the goal to compare the two immune environment types and to comprehensively characterize exhaustion-associated spatial features of the tumor microenvironment. For IMC, two consecutive FFPE sections of each sample were stained with two different antibody panels (Protein Panel and RNAscope Panel), and 4-10 regions of interest (ROIs, 1mm x 1mm) were measured on each section. ROIs on consecutive sections were registered manually to be as spatially close as possible.</p>
Dataset of the paper "Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)"
<p>This dataset contains 3792 images of 26 plant bug (Insecta: Heteroptera: Miridae: Mirini) species used to test the performance of a CNN in species recognition. All jpg files are 1920 pixels on the long size and additionally available as an archive file to facilitate download of the entire dataset. </p> <p>Bar code labels (unique specimen identifiers or USIs) were attached to all examined specimens used for this study. Further information such as additional photographs of habitus and genitalic structures, georeferenced coordinates of each locality, specimens dissected, notes, collecting method can be obtained from the Heteroptera Species Pages (http://research.amnh.org/pbi/heteropteraspeciespage/) which assembles available data from a specimen database and are also provided as an Excel spreadsheet (file _Adelphocoris_CNN_label_data.xlsx).</p>
Dataset accompanying manuscript "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope"
<p>Raw images and microscope calibration metadata for reconstruction of datasets presented in Fig. 1 and Fig. 3 of "Correlative imaging of spatio-angular dynamics of biological systems with multimodal instant polarization microscope". Notebooks demonstrating steps in the label-free and fluorescence anisotropy reconstruction pipelines can be found at https://github.com/mehta-lab/miPolScope.</p>
PTI datasets: 3D imaging
<p>This dataset includes the following data reported in the PTI paper (<a href="https://www.biorxiv.org/content/10.1101/2020.12.15.422951v2">link</a>). These datasets can be read and processed using the provided notebooks (<a href="https://github.com/mehta-lab/waveorder/tree/master/examples/uPTI_experiment">link</a>) with the waveorder package (<a href="https://github.com/mehta-lab/waveorder">link</a>). The zarr arrays (live one level below Col_x in the zarr files) in these datasets can also be visualized with the python image viewer (<a href="https://napari.org/">napari</a>). You will need the ome-zarr plugin in napari and drag the zarr array to the napari viewer. </p> <p>1. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Anisotropic_target_small.zip?versionId=4e890f01-6442-41eb-bffa-0387cfae510b">Anisotropic_target_small.zip</a> includes two zarr files that save the raw intensity images and processed physical properties of the small anisotropic target (double line-scan, 300-fs pulse duration):</p> <p>- Anisotropic_target_small_raw.zarr: array size in the format of (PolChannel, IllumChannel, Z, Y, X) = (4, 9, 96, 300, 300)</p> <p>- Anisotropic_target_small_processed.zarr: </p> <ul> <li>(Pos0 - Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 96, 300, 300)</li> <li>(Pos1 - Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 96, 300, 300)</li> </ul> <p> </p> <p>2. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Anisotropic_target_raw.zip?versionId=d783651b-de1b-45fd-8e71-df17073ced40">Anisotropic_target_raw.zip</a> includes the raw intensity images of another anisotropic target (single line-scan, 500-fs pulse duration):</p> <p>- data: 9 x 96 (pattern x z-slices) raw intensity images (TIFF) of the target with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- bg: - data: 9 (pattern) raw intensity images (TIFF) of the background with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- cali_images.pckl: pickle file that contains calibration curves of the polarization channels for this dataset</p> <p> </p> <p>3. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Anisotropic_target_processed.zip?versionId=4638a49f-82c2-4156-9c6c-e38742db0408">Anisotropic_target_processed.zip</a> includes two zarr files that save the processed scattering potential tensor components and the processed physical properties of the anisotropic target (single line-scan, 500-fs pulse duration):</p> <p>- uPTI_stitched.zarr: (Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 96, 1024, 1224)</p> <p>- uPTI_physical.zarr: (Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 96, 700, 700) (cropping the star target region)</p> <p> </p> <p>4. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Mouse_brain_aco_raw.zip?versionId=2c2a62bf-ab1a-4473-a064-4a58964aca56">Mouse_brain_aco_raw.zip</a> includes the raw intensity images of the mouse brain section at aco region:</p> <p>- data: 9 x 96 (pattern x z-slices) raw intensity images (TIFF) of the mouse brain section with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- bg: - data: 9 (pattern) raw intensity images (TIFF) of the background with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- cali_images.pckl: pickle file that contains calibration curves of the polarization channels for this dataset</p> <p> </p> <p>5. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Mouse_brain_aco_processed.zip?versionId=22423258-3fe0-4240-b88e-e40f97321447">Mouse_brain_aco_processed.zip</a> includes two zarr files that save the processed scattering potential tensor components and the processed physical properties of the mouse brain section at aco region:</p> <p>- uPTI_stitched.zarr: (Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 96, 1024, 1224)</p> <p>- uPTI_physical.zarr: (Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 96, 1024, 1224)</p> <p> </p> <p>6. Cardiomyocytes_(condition)_raw.zip includes two zarr files that save the raw PTI intensity images and the deconvolved fluorescence images of the cardiomyocytes with the specified (condition):</p> <p>- Cardiomyocytes_(condition)_raw.zarr:</p> <ul> <li>(Pos0) raw intensity images with the array size in the format of (PolChannel, IllumChannel, Z, Y, X) = (4, 9, 32, 1024, 1224)</li> <li>(Pos1) background intensity images with the array size in the format of (PolChannel, IllumChannel, Z, Y, X) = (4, 9, 1, 1024, 1224)</li> </ul> <p>- Cardiomyocytes_(condition)_fluor_decon.zarr: deconvolved fluorescence images with the array size in the format of (T, C, Z, Y, X) = (1, 3, 32, 1024, 1224)</p> <p> </p> <p>7. Cardiomyocytes_(condition)_processed.zip includes two zarr files that save the processed scattering potential tensor components and the processed physical properties of the cardiomyocytes with the specified (condition):</p> <p>- uPTI_stitched.zarr: (Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 32, 1024, 1224)</p> <p>- uPTI_physical.zarr: (Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 32, 1024, 1224)</p> <p> </p> <p>8. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/cardiac_tissue_H_and_E_processed.zip?versionId=45a5c99c-b2fb-4a23-94f2-1ccc96e2d5a7">cardiac_tissue_H_and_E_processed.zip</a> and <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Human_uterus_section_H_and_E_raw.zip?versionId=83ded89e-b62a-4f06-be0f-f18d28eaf0aa">Human_uterus_section_H_and_E_raw.zip</a> include the raw PTI intensity and H&E images of the cardiac tissue and human uterus section:</p> <p>- data: 10 x 40 (pattern x z-slices) raw intensity images (TIFF) of the target with size of (2048, 2448) -> 4 channels of (1024, 1224), the last channel is for images acquired with LCD turned off (the light leakage needed to be subtracted from the data)</p> <p>- bg: - data: 10 (pattern) raw intensity images (TIFF) of the background with size of (2048, 2448) -> 4 channels of (1024, 1224)</p> <p>- cali_images.pckl: pickle file that contains calibration curves of the polarization channels for this dataset</p> <p>- fluor: 3 x 40 (RGB x z-slices) raw H&E intensity images (TIFF) of the sample with size of (2048, 2448)</p> <p>- fluor_bg: 3 (RGB) raw H&E intensity images (TIFF) of the background with size of (2048, 2448)</p> <p> </p> <p>9. <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/cardiac_tissue_H_and_E_processed.zip?versionId=45a5c99c-b2fb-4a23-94f2-1ccc96e2d5a7">cardiac_tissue_H_and_E_processed.zip</a> and <a href="https://zenodo.org/api/files/c3b949a0-caf2-46ba-bb7c-b49192148ba9/Human_uterus_section_H_and_E_processed.zip?versionId=5080de3f-8527-40e6-bc80-6a6ec4f9bcf8">Human_uterus_section_H_and_E_processed.zip</a> include three zarr files that save the processed scattering potential tensor components, the processed physical properties, and the white-balanced H&E intensities of the cardiac tissue and human uterus section:</p> <p>- uPTI_stitched.zarr: (Stitched_f_tensor) array size in the format of (T, C, Z, Y, X) = (1, 9, 40, 1024, 1224)</p> <p>- uPTI_physical.zarr: (Stitched_physical) array size in the format of (T, C, Z, Y, X) = (1, 5, 40, 1024, 1224)</p> <p>- H_and_E.zarr: (H_and_E) array size in the format of (T, C, Z, Y, X) = (1, 3, 40, 1024, 1224)</p>
deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques
<p>In this paper, we present datasets that can be utilised for synthetic near infrared (NIR) image and bounding box level fruit detection system. It is undeniable fact that high-caliber machine learning software frameworks such as Tensorflow or Pytorch and large scale dataset such as ImageNet and COCO, and accelerated GPU hardware support have pushed the limit of machine learning for more than decades.</p> <p>Among these breakthroughs quality dataset is one of important key building blocks that can lead to success in model generalisation and deployment for data-driven deep neural networks. Particularly, synthetic data generation such as generative adversarial networks often requires relatively larger scale data than other supervised approaches. In addition, posing constrains such as geometrical facial constrains in fake face generation or consistent and radiometrically calibrated reflectances from satellite imagery commonly yield better results. We share NIR+RGB dataset that are re-processed from other two public datasets (nirscene and SEN12MS) and our own novel sweetpepper dataset to be able to timely adopt to other following studies.</p> <p>We oversampled from original nirscene dataset at 10, 100, 200, and 400 ratios and total of 127k pair of images. For SEN12MS satellite multispectral dataset, we selected one largest subset; Summer (45k) and All seasons (180k). Our sweetpeppr dataset consists of 1,615 pairs of NIR+RGB images. We demonstrate these NIR+RGB datasets are sufficient to be used for synthetic NIR generation quantitatively and qualitatively. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweetpepper dataset respectively.</p> <p>We also release <em>11</em> fruits' bounding box annotations that can be exported as various formats using cloud service. 4 newly added fruits [blueberry, cherry, kiwi, and wheat] compounds 11 novel bounding box dastaset together with our previous work in deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances is 162k and all bounding box dataset is ready for use from cloud service. For evaluation of these dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.5:0.95] results of [min:0.49, max:0.812]. We hope these dataset is useful and serves as one of baseline for the following up studies.</p>
Datasets for Background and Shading Correction of Optical Microscopy Images by BaSiC
<p>This repository holds all the example data for publication: "<strong>A BaSiC tool for background and shading correction of optical microscopy images, Nature Communications (2017)</strong>" DOI: <a href="https://doi.org/10.1038/ncomms14836">https://doi.org/10.1038/ncomms14836</a>. A downsampled version is available at Zenodo repository with DOI: <a href="https://zenodo.org/record/6974039#.YvD8G3ZBxD8">10.5281/zenodo.6974039</a>.</p>
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
<p>This is a Fossil Image Dataset, which contains >415000 images. A total of 50 clades were labeled, with a final 90% accuracy. We used the web crawler to download fossil images from the Internet. We declare that all the collected images are used for academic purposes only. If anyone wants to use this dataset, please agree on the Terms of access for the Fossil Image Dataset (FID). We uploaded two datasets: FID (contains 0.415 million images) and reduced-FID (60 thousand images, 1200 for each clade). Requirements of necessary preinstalled Python libraries, algorithms for analysis, and the model weights are available at <a href="https://github.com/XiaokangLiuCUG/Fossil_Image_Dataset">https://github.com/XiaokangLiuCUG/Fossil_Image_Dataset</a>.</p>
Labeled Images for Ulcerative Colitis (LIMUC) Dataset
<h1><strong>Dataset Details</strong></h1> <p>The LIMUC dataset compromises 11276 images from 564 patients and 1043 colonoscopy procedures, who underwent colonoscopy for ulcerative colitis between December 2011 and July 2019 at the Department of Gastroenterology in Marmara University School of Medicine. Two experienced gastroenterologists blindly reviewed and classified all images according to the Mayo endoscopic score (MES). Images that were differently labeled by two reviewers were also labeled by a third experienced reviewer independently without seeing their previous labels. The final MES for differently labeled images was determined using majority voting.</p> <p><br><strong>Mayo 0:</strong> 6105 (54.14%)<br><strong>Mayo 1:</strong> 3052 (27.70%)<br><strong>Mayo 2:</strong> 1254 (11.12%)<br><strong>Mayo 3:</strong> 865 (7.67%)</p> <p><strong>patient_based_classified_images:</strong> Images of each patient are separated according to Mayo classes. If a train-val-test splitting is to be made according to the ratios desired by the user, this folder should be used.</p> <p><strong>train_and_validation_sets:</strong> Train and validation sets used in the <a href="https://academic.oup.com/ibdjournal/advance-article-abstract/doi/10.1093/ibd/izac226/6830946">research paper</a>. Using the scripts in dataset's GitHub repository, same 10-fold can be generated for replicating the results.</p> <p><strong>test_set:</strong> Test set used for performance measurement in the research paper. For a fair performance comparisons, this should be used to report performances.</p> <h1><strong>Suggested Metrics</strong></h1> <p>Since there are imbalances and ordinality among classes (Mayo-0, Mayo-1, Mayo-2, Mayo-3), quadratic weighted kappa (QWK) can be used as the main performance metric. The QWK is one of the commonly used statistics for the assessment of agreement on an ordinal scale and it is one of the best singular performance metrics for this problem regarding class imbalances. Mean absolute error (MAE), macro F1 score, or macro accuracy can be used as alternative performance metrics.</p> <h1><strong>LIMUC Code Repository</strong></h1> <p>Many scripts for preprocessing, splitting, training, and validating the dataset are provided in <a href="https://github.com/GorkemP/labeled-images-for-ulcerative-colitis">this</a> GitHub repository.</p> <h1><strong>Terms and Conditions</strong></h1> <p>The LIMUC dataset is licensed under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. This license permits unrestricted use, distribution, and reproduction in any medium, provided that proper attribution is given to the original creators. This ensures that the dataset can be used for both <strong>research and commercial applications</strong> while maintaining transparency and acknowledgment of the contributors.</p> <p>For more details about the license, please refer to: <a>Creative Commons Attribution 4.0 International License</a>.</p> <p>Regarding the questions, please contact <em><strong>polatgorkem@gmail.com.</strong></em></p>
Dataset for the paper "Imaging evolution of Cascadia slow‑slip event using high‑rate GPS"
<p>Slip and slip rate files for the preferred model of Itoh, Aoki, and Fukuda (2022, Scientific Reports, doi:10.1038/s41598-022-10957-8).</p> <p>Please see readme.txt for further details. The corresponding author information is also available there.</p> <p>Caution: the dataset has the size of 4.3GB after uncompression/extraction.</p>
(10)-Strobl2022A-DS0008 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0008 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0004 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0004 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0001 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0001 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</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.