Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
38,240
datasets available to search
ShareScore release 0.7.1
Dataset results
38,240 results for “Imaging”
DHP images collected from Alto Tajo and Cuellar in Spain.
<p>Digital Hemispherical Photography images taken in 33 30 x 30 m plots across two sites in Spain. Images were taken on a 10 m grid, making 16 locations per plot (see Flynn et al., 2022 for details). At each location, DHP images were captured with three exposure settings (automatic and ± one stop exposure compensation), levelling a Canon EOS 6D full frame DSLR sensor with a Sigma EX DG F3.5 fisheye lens, mounted on a Vanguard Alta Pro 263AT tripod. For each RGB image, the blue band was extracted, as this best represents sky/ vegetation contrast. For each plot, an exposure setting was chosen based on visual assessment and pixel brightness histograms of four images indicative of the whole plot. Automatic thresholding was carried out using the Ridler and Calvard method (1978), creating a binary image of sky and vegetation.</p>
Doodleverse/Segmentation Zoo Res-UNet model for NOAA ERI/4-class segmentation of RGB 512x512 images
<p>This Residual-UNet model is trained on 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery (ERI) collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. The dataset is available here: https://doi.org/10.5281/zenodo.7268082</p> <p>Models have been created using Segmentation Gym:</p> <p>Code - https://github.com/Doodleverse/segmentation_gym</p> <p>Paper - https://doi.org/10.1029/2022EA002332</p> <p> </p> <p>The model takes input images that are 512 x 512 x 3 pixels, and the output is 512 x 512 x 4, corresponding to 4 classes:</p> <ol> <li>water</li> <li>bare sediment</li> <li>vegetation</li> <li>development (roads, buildings, power lines, parking lots, etc.)</li> </ol> <p> </p> <p>Included here are 6 files with the same root name:</p> <ol> <li> '.json' config file: this is the file that was used by Segmentation Gym to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction.</li> <li>'.h5' weights file: this is the file that was created by the Segmentation Gym function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym function `seg_images_in_folder.py`.</li> <li> '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</li> <li> '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</li> <li>'.zip' of the model in the Tensorflow ‘saved model’ format. It is created by the Segmentation Gym function `utils/gen_saved_model.py`</li> <li>'_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</li> </ol> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p>
ISS Mouse brain embryo - MIPPED images , all rounds all channels
<p>Repository containing the stitched, mipped and aligned images of all the cycles and channels used in the Mouse embryo ISS characterization from La Manno et al 2020 The repository contains:</p> <ul> <li>Stitched aligned and mipped images of all round and cycles for different samples (2A,2D, 6B,10B)</li> <li>A codebook with the code of every expected gene detecoded is included</li> <li>A preliminary decoding of the 4 samples included in the folder "decoded_spots"</li> <li>Information about channel order in a .txt</li> </ul>
Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments
<p><strong>Context</strong></p><p>This dataset is part of the paper "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments" presented at Oceans 2022, Hampton Roads,<strong> </strong>DOI: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977024">10.1109/OCEANS47191.2022.9977024</a></p><p>This dataset consists of paired camera and multi-beam sonar images of technical divers performing different underwater tasks in two locations: an indoor test basin and a lake. The general goal is to assist emergency operators that monitor the safety of divers operating in bad visibility conditions.</p><p>This data was used to train image-to-image translation models in order to generate realistic optical-like images given only sonar images as input or a combination of a sonar image and a dark or turbid optical image.</p><p> </p><p><strong>Content</strong></p><p>This repository contains three .zip folders each containing data collected in a different lab or field trial.</p><ul><li>'basin-dataset-1.zip' and 'basin-dataset-2.zip' contain data that were collected in an indoor testing facility at DFKI - Robotics Innovation Center, Bremen, Germany.</li><li>'lake-dataset-1.zip' and 'lake-dataset-2.zip' contains data collected at lake Kreidesee, Hemmoor, Germany.</li></ul><p>Each .zip file contains two subfolders labelled as 'camera' and 'sonar', each containing the images in png format. Data files under these subfolders with matching names composes a pair of time-synchronized images. For example, 'camera/0001.png' corresponds to 'sonar/0001.png'. The acquisition timestamp represented in seconds since epoch for every data file is recorded in 'sample.csv' include in each .zip file.</p><p>For more details and meta-information on the collected data please refer to "data_description.json" included in this repository.</p><p>Additional tools for handling and preparing the data can be found under <a href="https://github.com/DeeperSense/oceans_2022">https://github.com/DeeperSense/oceans_2022</a></p><p> </p><p><strong>Acknowledgements</strong></p><p>The data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.</p><p>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their initiative within the framework of the NFDI4Ing consortium (German Research Foundation (DFG) - project number 442146713).</p>
Hyperspectral Imaging dataset for use in Heritage Science
<p>The following data sets were collected to support the potential uses of opensource data in the context of digital humanities and heritage sciences. </p> <p>This proposed experiment is conducted by the UCL Institute for Sustainable Heritage in collaboration with the Centre for Digital Humanities. Imaging methods including Photography, Multispectral Imaging, Hyperspectral Imaging and Xray Fluorescence Mapping have been collected along with the complete readout metadata of the instrumentation. </p> <p>We hope that you find the data helpful, and we welcome you to use the data in any way you wish, for all and any analysis development purposes. For us to build upon this research, we ask that in return you would be willing to share in some regard your experiences in using open-source data, using our data, successes and issues. </p> <p>If you would be willing to engage with us in this endeavor, please feel free to contact us so that we may be able to follow up with you. </p> <p>Other Data sets available <a href="https://zenodo.org/record/7319696#.Y3NuOXbP2Uk">Here</a></p> <p> </p> <p>E: <a href="mailto:molly.fort.21@ucl.ac.uk">molly.fort.21@ucl.ac.uk</a> </p> <p>Object Paradata; </p> <ul> <li><strong>Postcard – c. Early 1900's </strong></li> <li><strong>Language – Eng. </strong></li> <li><strong>Materials – colour print on card, metallic leafing. </strong></li> <li><strong>Front transcription - </strong></li> <li><strong> ‘Greetings’ </strong></li> <li><strong> ‘May your Birthday bring you Peace & perfect Happiness, Golden hopes & Love of Friends, And every Happiness this world can send.’ </strong></li> <li><strong>Object Dimensions – 138mm X 88mm </strong></li> </ul> <p>The postcard is an item of ephemera donated to the UCLDH Digitisation Suite by Prof Melissa Terras, for teaching and training purposes in 2015.</p> <p>This folder contains:</p> <p>Hyperspectral Image data collected using a <a href="https://www.clydehsi.com/hyperspectral-cameras">ClydeHSI VNIR-HR+ Hyperspectral Imaging System</a>.</p> <p>Images captured : 477x484 pixel, 304 spectral band images, 4*4 pixel binning</p> <ul> <li>*.hdr - Header file read out from the ClydeHSI systems instructions for reading the subsequent .raw spectral database. </li> <li>*.raw - Hyperspectral image data cube information. Combination with hdr file creates a ENVI file format, this can be read into a variety of image analysis software packages. </li> <li>postcardhsi.ini - Metadata collected and read out from ClydeHSI system.</li> <li>Dark/White.corr - Correction files taken from camera for processing and minimalising system noise and illumination variences.</li> <li>*_refl.* - Pre - Corrected hyperspectral image data, using provided ClydeHSI software.</li> <li>Truecolour RGB reference image </li> </ul> <p>Each raw and header file set makes-up a single data set in ENVI file format. </p> <p>ENVI reading support exists in Python, R, Matlab, and other common image analysis packages.</p>
Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis - Imaging Flow Citometry Data
<p>Imaging flow citometry (IFC) datasets analysed in "Quantification of Giant Unilamellar Vesicle Fusion Products by High-Throughput Image Analysis" (under revision).</p> <p>The folders contain acquisitions of giant unilamellar vesicles (GUVs) for lipid exchange and content exchange controls, with file naming convention DATE_SAMPLE_REPLICATE.rif, content exchange is indicated by CE samples in the 20230228_CE.zip folder, lipid exchange by LE samples in the 20221222_LE.zip folder. 24 samples per set are included, triplicates of isolated P1 (DOPE Af488 0.6% in LE; Dex-Af488 40 uM for CE), P2 (DOPE Cy50.6% in LE; Dex-Af647 10 uM for CE), NC (P1 + P2 1:1), PC (DOPE Af488 0.3% + DOPE Cy5 0.3 in LE; Dex-Af488 20 uM + Dex-Af647 5 uM for CE), and M samples numbered 1 to 4, prepared by mixing P1, P2 and PC in different ratios (M1= 1:1:1; M2= 1:1:0.5; M3= 1:1:0.1; M4= 1:1:0.05).</p> <p>Only .rif files are provided, they have to be elaborated via compensation and application of an analysis template using the Amnis IDEAS software. Compensation matrices for lipid exchange (20230217_LEcom.ctm) and content exchange (20230217_CEcomp.ctm) are included, as well as the analysis template (Lipid_exchange_analysis_6.2.ast). Gating in the latter may have to be adjusted to analyse LE and CE experiments.</p> <p>10000 objects in the GUV population or 50000 objects in total were acquired in each file. The files were elaborated in batch mode, outputting the statistic reports (Statistics report CE.txt for CE; Statistics report LE.txt for LE) that were elaborated using an R scirpt (included, IFC_analysis.R) </p>
Image sets used in the development of a connected auto-encoders based approach to separate mixed X-radiographs from double-sided paintings
<p>The following sets of images were used during the development of an algorithm (described in the publication detailed below) designed to separate the mixed X-radiographs from double-sided paintings into two hypothetical X-ray images corresponding to each side of the painting, when visible images of the two sides of the painting are available.</p> <p>The images sets are taken from a painting that is only painted on one side and were used to assess the regularization parameters associated with the separation approach. The details are taken from the visible image and the X-radiograph of Anthony van Dyck’s painting <em>Lady Elizabeth Thimbelby and Dorothy, Viscountess Andover</em> dated to about 1635 and now in the collection of the National Gallery in London (NG6437). See <a href="https://www.nationalgallery.org.uk/paintings/anthony-van-dyck-lady-elizabeth-thimbelby-and-her-sister">https://www.nationalgallery.org.uk/paintings/anthony-van-dyck-lady-elizabeth-thimbelby-and-her-sister</a> for further details of the painting.</p> <p>The code can be downloaded from: <a href="https://github.com/ART-ICT/Xray_Separation_2RGB">https://github.com/ART-ICT/Xray_Separation_2RGB</a> and the algorithm is described in W. Pu, B. Sober, N. Daly, C. Zhou, Z. Sabetsarvestani, C. Higgitt, I. Daubechies and M. Rodrigues, ‘Image Separation with Side Information: A Connected Auto-Encoders Based Approach’, <em>Transactions on Image Processing, </em>2023 </p> <p><strong>All images © The National Gallery, London</strong></p> <p> </p> <p><strong><em>Datasets available: </em></strong></p> <p><strong>NG6437_vis_800pixel_230502.tif</strong>: 800 pixel thumbnail visible image of the entire painting showing the location of the two details used for the algorithm development. This image is derived from a visible image of the whole painting acquired 25 November 2019 (Original file: N-6437-00-000041.tif; 6272 x 5940 pixels).</p> <p><strong>NG6437_xray_800pixel_230502.tif</strong>: 800 pixel thumbnail image of the X-radiograph of the entire painting showing the location of the two details used for the algorithm development. This image is derived from the composite X-radiography of the whole painting created by mosaicking digital scans of the individual sheets of film and then registering the resulting image to the high resolution visible image described above (Original file: N-6437-00-000049.tif; 36847 x 32516 pixels).</p> <p><strong>NG6437_vis_crop_01_230502.tif</strong>: 1543 x 2078 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_01_230502.tif</strong>: 1543 x 2078 pixel detail of the X-radiograph corresponding to NG6437_vis_crop_01_230502.tif. The X-ray images were acquired using sheets of film (27 November 2019) and 16-bit digital scans were then produced (original files: N-6437-00-000047-009 and -014 (each 9539 x 7199 pixels), processed 28 January 2020). This crop is an 8-bit composite image of 2 X-ray plates that had been manually registered to the high resolution visible image described above using Adobe Photoshop.</p> <p><strong>NG6437_vis_crop_02_230502.tif</strong>: 1562 x 2023 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_02_230502.tif</strong>: 1543 x 2078 pixel detail of the X-radiograph corresponding to NG6437_vis_crop_02_230502.tif. The X-ray images were acquired using sheets of film (27 November 2019) and 16-bit digital scans were then produced (original files: N-6437-00-000047-002 and -007 (each 9539 x 7199 pixels), processed 28 January 2020). This crop is an 8-bit composite image of 2 X-ray plates that had been manually registered to the high resolution visible image described above using Adobe Photoshop.</p> <p><strong>NG6437_vis_crop_03_230502.tif</strong>: 2088 x 2088 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_03_230502.tif</strong>: 2088 x 2088 pixel detail taken from the composite X-radiograph described above corresponding to NG6437_vis_crop_03_230502.tif. </p> <p><strong>NG6437_vis_crop_04_230502.tif</strong>: 2088 x 2088 pixel detail taken from the high resolution visible image described above.</p> <p><strong>NG6437_xray_crop_04_230502.tif</strong>: 2088 x 2088 pixel detail taken from the composite X-radiograph described above corresponding to NG6437_vis_crop_04_230502.tif. </p> <p> </p>
Synthetic Particle Image Dataset (SPID)
<p>SPID is a comprehensive dataset composed of synthetic particle image velocimetry (PIV) image pairs and their corresponding exact optical flow computations. It serves as a valuable resource for researchers and practitioners in the field. The dataset is organized into three subsets: training, validation, and test, distributed in a ratio of 70%, 15%, and 15%, respectively.</p><p>Each subset within SPID consists of an input denoted as "x", which comprises synthetic image pairs. These image pairs provide the necessary context for the optical flow computations. Additionally, an output termed "y" is provided, which represents the exact optical flow calculated for each image pair. Notably, the images within the dataset are single-channel, and the optical flow is decomposed into its u and v components.</p><p>The shape of the input subsets in SPID is given by (number of samples, number of frames, image width, image height, number of channels), representing the dimensions of the input data. On the other hand, the shape of the output subsets is given by (number of samples, velocity components, image width, image height), denoting the shape of the optical flow data.</p><p>It is important to mention that SPID dataset is a preprocessed version of the Raw Synthetic Particle Image Dataset (RSPID), ensuring improved usability and reliability. Moreover, the dataset is packaged as a NumPy compressed NPZ file, which conveniently stores the inputs and outputs as separate NumPy NPZ files with the labels train, validation and test as acess keys. This format simplifies data extraction and integration into machine learning frameworks and libraries, facilitating seamless usage of the dataset.</p><p>SPID incorporates various factors that impact PIV analysis to provide a comprehensive and realistic simulation. The dataset includes image pairs with an image width of 665 pixels and an image height of 630 pixels, ensuring a high level of detail and accuracy with an 8-bit depth. It incorporates different particle radii (1, 2, 3, and 4 pixels) and particle densities (15, 17, 20, 23, 25, and 32 particles) to capture diverse particle configurations.</p><p>To simulate real-world scenarios, SPID introduces displacement variations through the delta x factor, ranging from 0.05% to 0.25%. Noise levels (1, 5, 10, and 15) are also incorporated to mimic practical PIV measurements with varying degrees of noise. Furthermore, out-of-plane motion effects are considered with standard deviations of 0.01, 0.025, and 0.05 to assess their impact on optical flow accuracy.</p><p>The dataset covers a wide range of flow patterns encountered in fluid dynamics. It includes Rankine uniform, Rankine vortex, parabolic, stagnation, shear, and decaying vortex flows, allowing for comprehensive testing and evaluation of PIV algorithms across different scenarios.</p><p>By leveraging the SPID dataset, researchers can develop and validate PIV algorithms and techniques under various challenging conditions. Its realistic and diverse simulation of particle image velocimetry scenarios makes it an invaluable tool for advancing the field and improving the accuracy and reliability of optical flow computations.</p><p> </p>
Imaging the footprint of nanoscale electrochemical reactions for assessing synergistic hydrogen evolution
<p>Dataset complementary to supporting information, such as optical movies, COMSOL model, and Python codes to analyze the experimental and simulated data according to the manuscript submitted for publication.<br> The movies correspond to cyclic voltammetry operando monitoring by optical microscopy of the reduction of water + KCl in the presence of NiCl2 at an ITO electrode or NiCl2 or MgCl2 at ITO electrode coated with Pt nanoparticles.</p> <p>The python function was used to extract the halo size around each nanoparticle from optical images, the python routines were used to postprocess the COMSOL simulation and evalaute the simulated halo size.</p>
Super-Resolved FRET Imaging by Confocal Fluorescence-Lifetime Single-Molecule Localization Microscopy
<p>FRET-based methods are a special tool for detecting interactions between (bio)molecules and their immediate environment. The spatial distribution of molecular interactions and functional states can be seen using FLIM (Fluorescence Lifetime IMaging) and FRET imaging. The spatial information, accuracy, and dynamic range of the observed signals are, however, constrained by the fact that conventional FLIM and FRET imaging only provides average information over an ensemble of molecules within a diffraction-limited volume. On the other hand, conventional Single Molecule Localization Microscopy (SMLM) relies on highly sensitive multi-pixel detectors (e.g. sCMOS or EM-CCD) whose time resolution is not suitable for fluorescence lifetime measurements.</p> <p>Here, we demonstrate a method for obtaining super-resolved FRET imaging using confocal fluorescence-lifetime single-molecule localization microscopy. The proof of concept was carried out using a DNA origami sample for performing DNA-PAINT measurements in combination with fluorogenic probes for reducing background signal. With this method, We show that FRET events separated by sub-diffraction distances can be distinguished based on lifetime modifications.</p>
Multispectral Images of the Kiev Folia
<p>Multispectral Images of the Kiev Folia (F. 301, Nr. 328 P), acquired at the Kiev National Library in 2018.</p> <p>Detailled descriptions of image acquisition, processing and usage are found in<em> Images_Kiev-Folia_zenodo_1.0.pdf </em></p>
CKN Edge AI Dataset for Image inference at the Edge (CEAD)
<p>This synthetic workload models camera device requests for resource constrained inference requests at the Edge for Campaign Knowledge Network evaluation. </p> <p>The workload is a deterministic and pre-ordered set of time windows containing close to 5 million individual data points belonging to 1500 time windows, each time window with a number of requests between 100-1000. Composed of independent inference requests (events), the workload is structured to reflect sudden changes in need as reflected by the user-perceived quality of experience (e.g., accuracy and latency). </p>
Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the English Channel (ICES area 27.7.d) stock
<p><strong>Contents: </strong></p> <p>This dataset can be completed with the : <strong>Macroscopic, histological and stereological image dataset of the Striped red mullet (<em>Mullus surmuletus</em>) ovaries from the Bay of Biscay (ICES area 27.7.g,j & 27.8.a-c) stock</strong>, which can also be found on the Zenodo repository.</p> <p>This dataset contains the macroscopic and histological images of the ovaries of 214 Striped red mullet (female, <em>Mullus surmuletus</em>, Linnaeus 1758) collected from the English Channel stock (ICES area 27.7.d) in February 2021 (n=20), March 2021 (n=13), April 2021 (n=12), May 2021 (n=15), August 2021 (n=15), September 2021 (n=15), October 2021 (n=41), November 2021 (n=10), December 2021 (n=14), January 2022 (n=30), February 2022 (n=15) and August 2022 (n=14).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 621 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 211 female Striped red mullets 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 : <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> </ul> <ul> <li><strong>Histology_slides.zip :</strong> archive in zip format containing the ovarian histological slides digitized using an Olympus V120 slide scanner, x20 lens. The pictures (.vsi from the OlympusVSI format) are of the 484 histological slides acquired during this study.</li> <li>Data was split for smaller size downloads : <ul> <li><strong>Histology_slides_1of5 :</strong> histological sections for individuals numbered 001 to 045</li> <li><strong>Histology_slides_2of5 :</strong> histological sections for individuals numbered 046 to 138</li> <li><strong>Histology_slides_3of5 :</strong> histological sections for individuals numbered 154 to 180</li> <li><strong>Histology_slides_4of5 :</strong> histological sections for individuals numbered 196 to 270</li> <li><strong>Histology_slides_5of5 :</strong> histological sections for individuals numbered 271 to 334</li> </ul> </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>Readings.zip :</strong> archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li><strong>Calibration</strong> : Reading results of 4 different agents, with the first and last readings, as well as the Qupath scripts used.</li> <li><strong>Homogeneity</strong> : Reading results for 96 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 96 slides belong to 16 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li><strong>Total </strong>: Reading results for 214 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> </ul> <ul> <li><strong>Macro_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_MULL.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_MULL.xlsx</strong> : Excel file (.xlsx) containing measurements of macroscopic parameters for all 214 fish sampled during this study. The information contained in this table is as follows: <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> </li> </ul> <ul> <li><strong>Stereo_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereo_MULL.csv</strong> file, as well as their meaning.</li> <li><strong>Stereo_MULL.csv</strong> : a text data file (.csv) of the stereology count results of 294 slides read during this study. Among these slides, 96 were read to test the homogeneity distribution of different cell types found throughout each ovary (16 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see <strong>Calibration</strong> folder for reading results of the 4 agents). Finally, 214 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin <em>et al.</em> 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with <strong>Fish_id</strong></li> </ul> </li> </ul>
Macroscopic, histological and stereological image dataset of the Striped red mullet (Mullus surmuletus) ovaries from the Bay of Biscay (ICES area 27.7.g,j & 27.8.a-c) stock
<p><strong>Contents: </strong></p> <p>This dataset can be completed with the : <strong>Macroscopic, histological and stereological image dataset of the Striped red mullet (<em>Mullus surmuletus</em>) ovaries from the English Channel (ICES area 27.7.d) stock</strong>, which can also be found on the Zenodo repository.</p> <p>This dataset contains the macroscopic and histological images of the ovaries of 103 Striped red mullet (female, <em>Mullus surmuletus</em>, Linnaeus 1758) collected from the Bay of Biscay stock (ICES areas 27.7.j,g & 27.8.a-c) in November 2020 (n=9), May 2021 (n=11), June 2021(n=7), July 2021 (n=15), September (n=15), October 2021 (n=3), November 2021 (n=27) and February 2022 (n=15).</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li><strong>Macroscopic_pictures.zip: </strong>archive in zip format of 290 pictures (.JPG; 2Mo-8Mo; JPG; 350pp) from 103 female Striped red mullets 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 : <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> </ul> <ul> <li><strong>Histology_slides.zip:</strong> archive in zip format containing the ovarian histological slides digitized using an Olympus V120 slide scanner, x20 lens. The pictures (.vsi from the OlympusVSI format) are of the 264 histological slides acquired during this study. Data was split for smaller size downloads : <ul> <li><strong>Histology_slides_1of3 :</strong> histological sections for individuals numbered 062 to 094</li> <li><strong>Histology_slides_2of3 :</strong> histological sections for individuals numbered 100 to 250</li> <li><strong>Histology_slides_3of3 :</strong> histological sections for individuals numbered 290 to 304</li> </ul> </li> </ul> <p> </p> <p><strong>Data:</strong></p> <ul> <li><strong>Readings.zip:</strong> archive in zip format containing the stereology reading results of the ovarian histological slides. In this folder, three directories are available. <ul> <li><strong>Calibration </strong>: Reading results of 4 different agents, with the first and last readings, as well as the Qupath scripts used<strong>.</strong></li> <li><strong>Homogeneity</strong> : Reading results for 84 histological slides used to check the cellular homogeneity inter- and intra-gonad. These 84 slides belong to 14 fish, with three histological samples taken in the anterior (1), median (2) and posterior (3) sections of the left (G) and right (D) ovaries. A QuPath folder is also present, containing the scripts used.</li> <li><strong>Total</strong> : Reading results for 103 ovarian histological slides of the median position of either the left or right ovary. One median slide was read per sampled fish. A QuPath folder is also present, containing the scripts used.</li> </ul> </li> </ul> <ul> <li><strong>Macro_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Macro_MULL.xlsx</strong> file, as well as their meaning.</li> <li><strong>Macro_MULL.xlsx</strong> : Excel file (.xlsx) containing measurements of macroscopic parameters for all 103 fish sampled during this study. The information contained in this table is as follows: <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> </li> </ul> <ul> <li><strong>Stereo_MULL_read_me.txt</strong> : a text file (.txt) listing the acronyms used in the <strong>Stereo_MULL.csv</strong> file, as well as their meaning.</li> <li><strong>Stereo_MULL.csv</strong> : a text data file (.csv) of the stereology count results of 173 slides read during this study. Among these slides, 84 were read to test the homogeneity distribution of different cell types found throughout each ovary (14 fish with 6 histological sections : a median, an anterior and a posterior histological section, for both ovaries), slides were read by multiple agents for calibration purposes (see <strong>Calibration</strong> folder for reading results of the 4 agents). Finally, 103 median histological ovarian slides were read. The information contained in this table is as follows: <ul> <li>cell_type: structure identified for one sample point (for the abbreviations, see Heude-Berthelin <em>et al.</em> 2023)</li> <li>idpt: identification number of the sampling point</li> <li>id: unique complex identification number of the sampling point generated by combining the x and y coordinates</li> <li>x: x coordinate of the sampling point</li> <li>y: y coordinate of the sampling point</li> <li>reading: Indicates if the reading data was used to test cellular homogeneity (Homogeneity) or to the sexual maturity phase</li> <li>slideid: identification number of the digitized histological slide that was used for the stereological count. Shares the same 12 first characters with <strong>Fish_id</strong></li> </ul> </li> </ul>
Images of 3D digitisation of articles of traditional attire
<p>These files are images digitisations of traditional, handcrafted dresses, shoes, handbags, and fabrics. These items are manufactured during the 21st century following traditional manufacturing methods and utilising designs and motifs from Greek antiquities. The 3D models were photogrammetrically captured. These images correspond to the 3D models in <a href="http://doi.org/10.5281/zenodo.8098709">https://doi.org/10.5281/zenodo.8098709</a></p>
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall
<p><strong>Dataset overview</strong></p> <p>This dataset provides data and images of snowflakes in free fall collected with a <a href="https://amt.copernicus.org/articles/5/2625/2012/">Multi-Angle Snowflake Camera (MASC)</a> The dataset includes, for each recorded snowflakes:</p> <ol> <li>A triplet of gray-scale images corresponding to the three cameras of the MASC</li> <li>A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement.</li> </ol> <p>The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example:</p> <ul> <li>Textural and geometrical descriptors as in <a href="https://amt.copernicus.org/articles/10/1335/2017/"><em>Praz et al 2017</em></a></li> <li>Hydrometeor classification, riming degree estimation, melting identification, as in <a href="https://amt.copernicus.org/articles/10/1335/2017/"><em>Praz et al 2017</em></a></li> <li>Blowing snow identification, as in <a href="https://tc.copernicus.org/articles/14/367/2020/"><em>Schaer et al 2020 </em></a></li> <li>Mass, volume, gyration estimation<em>, as in <a href="https://amt.copernicus.org/preprints/amt-2021-176/">Leinonen et al 2021</a></em></li> </ul> <p><strong>Data format and structure</strong></p> <p>The dataset is divided into four <em>.parquet</em> file (for scalar descriptors) and a <em>Zarr</em> database (for the images). A detailed description of the data content and of the data records is available <a href="https://pymascdb.readthedocs.io/en/latest/data.html#data">here</a>.</p> <p><strong>Supporting code</strong></p> <p>A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on <a href="https://github.com/ltelab/pymascdb">GitHub</a>. See also the code documentation on <a href="https://pymascdb.readthedocs.io/en/latest/index.html">ReadTheDocs</a>.</p> <p><strong>Download notes</strong></p> <ul> <li>All files available here for download should be stored in the same folder, if the python-based API is used</li> <li><em>MASCdb.zarr.zip</em> must be unzipped after download</li> </ul> <p><strong>Field campaigns</strong></p> <p>A list of campaigns included in the dataset, with a minimal description is given in the following table</p> <table> <tbody> <tr> <td><strong>Campaign_name</strong></td> <td><strong>Information</strong></td> <td> <p><strong>Shielded / Not shielded</strong></p> <p><em>DFIR = Double Fence Intercomparison Reference</em></p> </td> </tr> <tr> <td> <p><em>APRES3-2016 & APRES3-2017</em></p> </td> <td>Installed in Antarctica in the context of the APRES3 project. See for example <a href="https://essd.copernicus.org/articles/10/1605/2018/essd-10-1605-2018.html">Genthon et al, 2018</a> or <a href="https://tc.copernicus.org/articles/11/1797/2017/">Grazioli et al 2017</a></td> <td>Not shielded</td> </tr> <tr> <td><em>Davos-2015</em></td> <td>Installed in the Swiss Alps within the context of <a href="https://public.wmo.int/en/resources/meteoworld/spice-%E2%80%93-improving-snowfall-measurements">SPICE</a> (Solid Precipitation InterComparison Experiment)</td> <td>Shielded (DFIR)</td> </tr> <tr> <td><em>Davos-2019</em></td> <td>Installed in the Swiss Alps within the context of <a href="https://www.envidat.ch/group/about/raclets-field-campaign">RACLETS</a> (<em>Role of Aerosols and CLouds Enhanced by Topography on Snow</em>)</td> <td>Not shielded</td> </tr> <tr> <td><em>ICEGENESIS-2021</em></td> <td>Installed in the Swiss Jura in a MeteoSwiss ground measurement site, within the context of ICE-GENESIS. See for example <a href="https://doi.org/10.1175/BAMS-D-21-0184.1">Billault-Roux et al, 2023</a></td> <td>Not shielded</td> </tr> <tr> <td><em>ICEPOP-2018</em></td> <td>Installed in Korea, in the context of ICEPOP. See for example <a href="https://doi.org/10.5194/essd-13-417-2021">Gehring et al 2021</a>.</td> <td>Shielded (DFIR)</td> </tr> <tr> <td><em>Jura-2019 & Jura-2023</em></td> <td>Installed in the Swiss Jura within a MeteoSwiss measurement site</td> <td>Not shielded</td> </tr> <tr> <td><em>Norway-2016</em></td> <td>Installed in Norway during the High-Latitude Measurement of Snowfall (HiLaMS). See for example <a href="https://doi.org/10.1175/BAMS-D-21-0007.1">Cooper et al, 2022</a>.</td> <td>Not shielded</td> </tr> <tr> <td><em>PLATO-2019</em></td> <td>Installed in the "Davis" Antarctic base during the <a href="https://www.osti.gov/biblio/1524773">PLATO</a> field campaign</td> <td>Not shielded</td> </tr> <tr> <td><em>POPE-2020</em></td> <td>Installed in the "Princess Elizabeth Antarctica" base during the POPE campaign. See for example <a href="https://essd.copernicus.org/articles/15/1115/2023/essd-15-1115-2023.html">Ferrone et al, 2023</a>.</td> <td>Not shielded</td> </tr> <tr> <td><em>Remoray-2022</em></td> <td>Installed in the French Jura.</td> <td>Not shielded</td> </tr> <tr> <td><em>Valais-2016</em></td> <td>Installed in the Swiss Alps in a ski resort.</td> <td>Not shielded</td> </tr> <tr> <td>ISLAS-2022</td> <td>Installed in Norway during the <a href="https://www.uib.no/en/rg/meten/150202/islas2022-field-campaign">ISLAS campaign</a></td> <td>Not shielded</td> </tr> <tr> <td>Norway-2023</td> <td>Installed in Norway during the MC2-ICEPACKS campaign</td> <td>Not shielded</td> </tr> </tbody> </table> <p> </p> <p><strong>Version</strong></p> <p>1.1 - Two new campaigns ("ISLAS-2022", "Norway-2023") added.</p> <p>1.0 - Two new campaigns ("Jura-2023", "Norway-2016") added. Added references and list of campaigns.</p> <p>0.3 - a new campaign is added to the dataset ("Remoray-2022")</p> <p>0.2 - rename of variables. Variable precision (digits) standardized</p> <p>0.1 - first upload</p>
DICOM converted whole slide hematoxylin and eosin images of rhabdomyosarcoma from Children's Oncology Group trials
<p>Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. This dataset contains manifests referring to the hematoxylin and eosin (H&E) stained images in Digital Imaging and Communications in Medicine (DICOM) format available from National Cancer Institute Imaging Data Commons (IDC) [1] (also see IDC Portal at <a href="https://imaging.datacommons.cancer.gov">https://imaging.datacommons.cancer.gov</a>) as of data release v16. The original images in vendor-specific format were collected on IRB-approved clinical trials or tissue banking studies from Children’s Oncology Group (COG) patients enrolled on ARST0331, ARST0431, D9602, D9803, and D9902 trials, as described in [2]. Those images, augmented with the metadata describing their content, were provided to the IDC team for the purposes of archival, and were converted into DICOM Whole Slide Microscopy (SM) representation [3], [4] using custom open source scripts and tools available and described here [5]. The resulting converted images were released in IDC in the RMS-Mutation-Prediction collection with the data release v16.</p> <p>To conveniently explore the data available for this dataset, please use this dashboard: <a href="https://lookerstudio.google.com/reporting/7f267400-8774-42e1-b5d1-ca11863c52a9">https://lookerstudio.google.com/reporting/7f267400-8774-42e1-b5d1-ca11863c52a9</a>.</p> <p>Notebooks demonstrating how to use this data are available here: <a href="https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction">https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction</a>.</p> <p>Clinical data accompanying the images is available via SQL interface in IDC BigQuery tables, see details on accessing IDC clinical data in the respective tutorial (<a href="https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/clinical_data_intro.ipynb">https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/clinical_data_intro.ipynb</a>).</p> <p>The images referred to by the accompanying manifests can be explored and visualized using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/">https://portal.imaging.datacommons.cancer.gov/explore/</a>. Direct link to open the collection is <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=rms_mutation_prediction">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=rms_mutation_prediction</a>.</p> <p>The GCP and AWS manifests provided with this dataset record can be used to download the corresponding files from the IDC Google Cloud Storage (GCS) or Amazon S3 (AWS) buckets free of charge following the instructions available in IDC documentation here: <a href="https://learn.canceridc.dev/data/downloading-data">https://learn.canceridc.dev/data/downloading-data</a>. Specifically, you will need to install the s5cmd command line tool on your computer (see instructions at <a href="https://github.com/peak/s5cmd#installation">https://github.com/peak/s5cmd#installation</a>), and follow the manifest-specific download instructions accompanying the file list below.</p> <p>If you use the files referenced in the attached manifests, we ask you to please cite this dataset, as well as the publication describing the original dataset [2] and the publication acknowledging IDC [1].</p> <p>Specific files included in the record are:</p> <ol> <li> <p><strong><code>rms_mutation_prediction_gcs.s5cmd</code></strong>: GCS-based manifest (to download the files described in the manifest, execute this command: <code>s5cmd --no-sign-request --endpoint-url https://storage.googleapis.com run rms_mutation_prediction_gcs.s5cmd</code>)</p> </li> <li> <p><strong><code>rms_mutation_prediction_aws.s5cmd</code></strong>: AWS-based manifest (to download the files described in the manifest, execute this command: <code>s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run rms_mutation_prediction_aws.s5cmd</code>)</p> </li> <li> <p><strong><code>rms_mutation_prediction_dcf.csv</code></strong>: Gen3-based manifest (see details in <a href="https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>).</p> </li> </ol> <p><strong>References</strong></p> <p>[1] A. Fedorov et al., "NCI Imaging Data Commons," Cancer Res., vol. 81, no. 16, pp. 4188–4193, Aug. 2021, doi: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-21-0950">10.1158/0008-5472.CAN-21-0950</a>. </p> <p>[2] D. Milewski et al., "Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of H&E images: A report from the Children's Oncology Group," Clin. Cancer Res., vol. 29, no. 2, pp. 364–378, Jan. 2023, doi: <a href="https://dx.doi.org/10.1158/1078-0432.CCR-22-1663">10.1158/1078-0432.CCR-22-1663</a>.</p> <p>[3] National Electrical Manufacturers Association (NEMA), "DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD." Accessed: Aug. 11, 2023. [Online]. Available: <a href="https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8">https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8</a></p> <p>[4] M. D. Herrmann et al., "Implementing the DICOM standard for digital pathology," J. Pathol. Inform., vol. 9, no. 1, p. 37, Jan. 2018, doi: <a href="https://dx.doi.org/10.4103/jpi.jpi_42_18">10.4103/jpi.jpi_42_18</a>. </p> <p>[5] D. Clunie, A. Fedorov, and M. D. Herrmann, ImagingDataCommons/idc-wsi-conversion: Initial release. Zenodo, 2023. doi: <a href="https://dx.doi.org/10.5281/zenodo.8240154">10.5281/zenodo.8240154</a>. </p>
Underwater surveys of mullet schools (Mugil liza) with Adaptive Resolution Imaging Sonar
<p>This dataset is part of a research project that employs deep learning, with a density-based regression approach, to count fish in low-resolution sonar images (Tarling et al. preprint arXiv DOI: http://arxiv.org/abs/2104.14964).</p> <p>In this repository, we provide data from sonar-based underwater videos of schools of migratory mullets (<em>Mugil liza</em>) recorded at the Tesoura beach (28.495775 S, 48.759996 W), a 100-meter long beach at the inlet canal connecting the Laguna lagoon system to the Atlantic Ocean, in southern Brazil. Since the water transparency at the lagoon canal is very low (from 0.3 to 1.5m visibility; collected <em>in situ</em> with a Secchi disk), mullet schools were recorded by deploying an Adaptive Resolution Imaging Sonar, ARIS 3000 (Sound Metrics Corp, WA, USA), which uses 128 beams to project a wedge-shaped volume of acoustic energy and convert their returning echoes into a digital overhead view of the mullet schools.</p> <p>This dataset contains 500 fully annotated images that were manually marked for the location and abundance of mullet fish, and 126 raw sonar video files, representing over 100k images. The files are organized as follows:</p> <p>1) "2018-MM-DD_HHMMSS" files are mp4 videos (you may need to add the file extension ".mp4"): There are 126 ARIS files converted into MP4 videos totalling over 789MB of underwater footage captured at 3 frames/seconds. Note that file names indicate the date and time the video was recorded.</p> <p>2) ".npy" files (in Labelled_data.zip): From the video files, 500 images were selected for labelling. Images (x) were cropped to represent a 4x8.5m<sup>2</sup> area and resized to 320 x 576 pixels. Mullet fish were marked with a point annotation. Corresponding ground truth density maps (y) were generated by convolving a Gaussian kernel over the image mask, size =4 and standard deviation = 1. The labelled dataset was randomly split into a holdout partition of 350 training images, 70 validation, and 80 test. </p> <p>3) ".csv" files: log of frames selected for the labelled subset of data</p> <p>4) ".h5" file: pre-trained weights for our multi-task with uncertainty regularisation network</p> <p>To advance the development of these machine learning tools, we also make our code openly available (https://github.com/ptarling/DeepLearningFishCounting).</p>
Pre-training with simulated ultrasound images for breast mass segmentation and classification - dataset
<p>Dataset assosiated with the MICCAI Workshop on Data Engineering in Medical Imaging paper: "Pre-training with Simulated Ultrasound Images for Breast Mass Segmentation and Classification"</p>
NDVI images derived from the 2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included ten flight lines flown for the examination of salt marsh and upland vegetation and water for spectral properties at 1 m spatial resolution. For all vegetation images, the Normalized difference vegetation index (NDVI) was calculated. NDVI uses the ratio of reflectance in the red and NIR wavelengths (NDVI = (NIR799 - RED675)/ (NIR799 + RED675)) to derive an index of plant vigor (Rouse et al., 1974). The subscript values are the wavelength band centers used to calculate NDVI. Values indicate the amount of green vegetation present in the pixel—higher NDVI values indicate more green vegetation. Vallid results fall between -1 and +1.
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.