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979 results for “Image dataset”
Minimal dataset to test multiplexed DNA imaging (Hi-M) software pipelines
<p>This is a dataset of nuclei (DAPI), and 3 multiplexed DNA imaging cycles to test and validate processing software packages, such as pyHiM (https://github.com/marcnol/pyHiM). This dataset was acquired in a nc14 Drosophila embryo.</p> <p>File contents:</p> <p>scan_001_RT27_001_ROI_converted_decon_ch00.tif barcode 27, fiducial <br> scan_001_RT27_001_ROI_converted_decon_ch01.tif barcode 27<br> scan_001_RT29_001_ROI_converted_decon_ch00.tif barcode 29, fiducial <br> scan_001_RT29_001_ROI_converted_decon_ch01.tif barcode 29 <br> scan_001_RT37_001_ROI_converted_decon_ch00.tif barcode 37, fiducial <br> scan_001_RT37_001_ROI_converted_decon_ch01.tif barcode 37 <br> scan_006_DAPI_001_ROI_converted_decon_ch00.tif DAPI <br> scan_006_DAPI_001_ROI_converted_decon_ch01.tif DAPI, fiducial <br> scan_006_DAPI_001_ROI_converted_decon_ch02.tif RNA</p> <p> </p> <p>To test this dataset please refer to <a href="https://github.com/marcnol/pyHiM">pyHiM documentation page</a>.</p>
Multiplexed DNA-FISH imaging dataset, drosophila embryos, nuclear cycles 11-14
<p>Multiplexed DNA-FISH imaging dataset from Drosophila embryos at nuclear cycles 11-14.</p> <p>Examples on how to load and use this dataset can be found at this <a href="https://github.com/NollmannLab/Goetz_etal">GitHub repository</a>.</p> <p><strong>Data processing details</strong></p> <p>Barcodes were segmented using a neural network (<a href="https://github.com/stardist/stardist"><em>stardist</em></a>) specifically trained for the detection of 3D diffraction limited spots produced by our microscope. To extract the position of the barcode with sub-pixel accuracy, a subsequent 3D Gaussian fit of the regions segmented by <em>stardist</em> was performed with Big-FISH (<a href="https://github.com/fish-quant/big-fish">https://github.com/fish-quant/big-fish</a>). Barcode localizations with intensities lower than 1.5 times that of the background were filtered out.</p> <p>Nuclei were segmented from projected DAPI images using <em><a href="https://github.com/stardist/stardist">stardist</a> </em>with a neural network trained for detection of nuclei from <em>Drosophila</em> embryos under our imaging conditions. Barcodes were then attributed to single nuclei by using the XY coordinates of the barcodes and the DAPI masks of the nuclei. Finally, pairwise distance matrices were calculated for each single nucleus.</p> <p><strong>Processed data in Figures</strong></p> <p>This new version of the dataset contains the raw data for each of the figures in the manuscript:</p> <p><strong>Associated publication</strong></p> <p><strong>Multiple parameters shape the 3D chromatin structure of single nuclei at the doc locus in </strong><em>Drosophila</em>.</p> <p>Markus Götz, Olivier Messina, Sergio Espinola, Jean-Bernard Fiche, Marcelo Nollmann</p> <p>Nature Communications (2022).</p>
Datasets for "Single-molecule and super-resolved imaging deciphers membrane behaviour of onco-immunogenic CCR5"
<p><strong>Flow cytometry</strong></p> <p>Modality / instrument: <em>Flow cytometer</em> <em>(CytoFLEX LX, Beckman Coulter)</em></p> <p>File format:<em> FCS + XIT (CytExpert, Beckman Coulter).</em></p> <p>Samples and acquisitions:</p> <p>Fluorescent fusions in live Chinese Hamster ovary (CHO) cells.</p> <table> <tbody> <tr> <td> <p><em>File</em></p> </td> <td> <p><em>Cell line</em></p> </td> <td> <p><em>Runs</em></p> </td> <td> <p><em>Cells counted</em></p> </td> </tr> <tr> <td> <p>CONTROL.fcs</p> </td> <td> <p>CHO wild-type</p> </td> <td> <p>1</p> </td> <td> <p>7000</p> </td> </tr> <tr> <td> <p>GFP-CCR5.fcs</p> </td> <td> <p>CHO-GFP-CCR5</p> </td> <td> <p>1</p> </td> <td> <p>7000</p> </td> </tr> <tr> <td> <p>Exp_20220916_1_GFP.xit</p> </td> <td> <p>N/A - metadata</p> </td> </tr> </tbody> </table> <p>Approx. size 6 MB</p> <p> </p> <p><strong>PaTCH microscopy images</strong></p> <p>Imaging modality / instrument: <em>Brightfield</em> + <em>PaTCH fluorescence microscopy</em></p> <p>Image format:<em> OME TIFF (16 bit) + MicroManager metadata files</em></p> <p>Microscope settings:</p> <p><em>488 nm triggered excitation; split red/green detection, cropped to green (GFP) channel only; 10 ms/frame laser exposure; 13.5 ms/frame-to-frame; 53 nm/px. Photometrics Prime95b CMOS.</em></p> <p>Samples and acquisitions:</p> <p>Fluorescent fusions of GFP-CCR5 receptor in live CHO cells imaged with and without 100 nM CCL5 ligand. Each subfolder corresponds to a field of view and contains one brightfield and one PaTCH acquisition of the same cell.</p> <table> <tbody> <tr> <td> <p>Folder</p> </td> <td> <p>Condition</p> </td> <td> <p>Fields of view</p> </td> </tr> <tr> <td> <p>AC6 CONTROL sc</p> </td> <td> <p>CCL5-</p> </td> <td> <p>11</p> </td> </tr> <tr> <td> <p>AC6 CCL5 sc</p> </td> <td> <p>CCL5+ (100 nM)</p> </td> <td> <p>10</p> </td> </tr> </tbody> </table> <p>Approx. size before compression: 14 GB</p> <p> </p> <p><strong>Structured illumination microscopy - volumetric stacks</strong></p> <p>Imaging modality / instrument: <em>SIM fluorescence microscopy (custom setup at NPL based on Olympus IX71)</em></p> <p>Image format:<em> OME TIFF (16 bit) with intrinsic metadata (voxel size)</em></p> <p>Microscope settings: <em>638 nm excitation; 60x/1.3 NA; Flash 4.0, Hamamatsu Photonics. For additional details see the reference below (Hunter et al, bioRxiv).</em></p> <p>Samples and acquisitions:</p> <p>Dylight 650-MC-5 labeled CCR5 receptor in fixed CHO-CCR5 cells, imaged with and without 100 nM CCL5 ligand. Each acquisition is of a unique field of view and contains one SIM reconstruction as an XYZ volumetric stack. ‘Basal membrane’ acquisitions consist of 5 slices at 200 nm z-intervals across the range of the basal membrane. ‘Whole cell' acquisitions are made up of 7 slices with 500 nm z-interval ranging from just below the basal membrane to just above the apical membrane. </p> <table> <tbody> <tr> <td>Folder</td> <td>Subfolder/condition</td> <td>Fields of view</td> </tr> <tr> <td>Basal membrane</td> <td>CCL5-</td> <td>5</td> </tr> <tr> <td>Basal membrane</td> <td>CCL5+ (100 nM)</td> <td>6</td> </tr> <tr> <td>Whole cells</td> <td>CCL5-</td> <td>5</td> </tr> <tr> <td>Whole cells</td> <td>CCL5+ (100 nM)</td> <td>8</td> </tr> </tbody> </table> <p>Approx. size before compression: 300 MB</p>
Dataset of Scanning Tunneling Microscopy (STM) images of model surfaces for elementary steps in catalytic reactions
<p>STM images presented in the dataset were recorded by the STRAS research group using a Omicron Variable Temperature STM (VT-STM) microscope, in the TASC laboratory of the CNR-IOM in Trieste.</p> <p>This work has been done within the NFFA-DI project funded by the European Union – NextGenerationEU - Missione 4, “Istruzione e Ricerca” – Componente 2, “Dalla ricerca all'impresa” – Linea di investimento 3.1,“Fondo per la realizzazione di un sistema integrato di infrastrutture di ricerca e innovazione” – Azione 3.1.1, “Creazione di nuove IR o potenziamento di quelle esistenti che concorrono agli obiettivi di Eccellenza Scientifica di Horizon Europe e costituzione di reti”.</p>
MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING.
<p>This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as:</p> <ul> <li>Greece</li> <li>Italy</li> <li>France</li> <li>Spain</li> <li>Belgium</li> <li>UK</li> <li>Sweden</li> <li>Finland and</li> <li>Serbia</li> </ul> <p>From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: <a href="../api/records/11220437/draft/files/Accepted_Abstract_03_15_2024.pdf/content" target="_blank" rel="noopener noreferrer">Accepted_Abstract_03_15_2024.pdf</a>) or the published paper, that you can find here: <a href="https://doi.org/10.1109/IGARSS53475.2024.10642051" target="_blank" rel="noopener">10.1109/IGARSS53475.2024.10642051</a>.</p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI) [raw datasets]
<p>Raw datasets accompanying the analysis in "Quantitative comparison of camera technologies for cost-effective Super-resolution Optical Fluctuation Imaging (SOFI)"</p> <p>The datasets contain raw fluorescence microscopy images aimed to be processed in a SOFI analysis. They are acquired with different camera technologies, allowing for direct comparison of an industry-grade CMOS detector with both a scientific-grade sCMOS and emCCD detector.</p>
Synthetic dataset accompanying Neural Image Compression for Gigapixel Histopathology Image Analysis
<p>This dataset was used to develop and evaluate the main method proposed in the paper "Neural Image Compression for Gigapixel Histopathology Image Analysis" published in IEEE Transactions on Pattern Analysis and Machine Intelligence with DOI 10.1109/TPAMI.2019.2936841. Please refer to the paper for a detailed description of the dataset.</p> <p>The dataset consists of a set of 50000 images and 50000 associated ground truth masks, distributed into training and test partitions. The name of each file follows the pattern "{id}_{tilted_label}_{nontilted_label}_{tilted_size}_{nontilted_size}_{kind}.png" where:<br> * id: unique identifier within each partition.<br> * tilted_label: image-level label corresponding to the tilted rectangle.<br> * nontilted_label: image-level label corresponding to the non-tilted rectangle.<br> * tilted_size: longest size of the tilted rectangle.<br> * nontilted_size: longest size of the non-tilted rectangle.<br> * kind: either "tile" or "mask" image type.</p> <p>The images are distributed into several data partitions used during cross-validation and fully described in "mnist_folds_set.json". Please rename "mnist_folds_set.json.removethis" into "mnist_folds_set.json".</p> <p>The code to recreate this dataset can be found in https://github.com/davidtellez/neural-image-compression.</p>
Dataset of Scanning Tunneling Microscopy (STM) images of graphene on nickel
<p>STM images presented in the dataset were recorded by the STRAS research group using a Omicron Variable Temperature STM (VT-STM) microscope, in the TASC laboratory of the CNR-IOM in Trieste.</p> <p> </p>
MORSE image reconstruction example dataset
<p>This repository contains raw k-space datasets from 3T and 7T multi-echo spoiled gradient echo MRI scans, which along with the source code uploaded on <a href="https://github.com/fil-physics/gadgetron-matlab">GitHub</a>, can be used to demonstrate FIL Physics MORSE image reconstruction as in the following manuscript:</p> <p>"MORSE CODE: Multiple Orthogonal Reference Sensitivity Encoding Combined Over Dominant Eigencoils" by O. Josephs and B. Dymerska et al.</p> <p>If you use these data or MORSE image reconstruction, please make sure to cite the paper.</p>
IPTIS (individual pomelo tree image sample) datasets
<p>These datasets include 480 clip images from two study sites (i.e., Site A and B) totally. The origional UAV-based images were captured with DJI drones on four different dates, i.e., 3 Debruary 2021, 12 March 2021, 12 April 2021, and 16 January 2022. They were proceeded into four separate datasets according to the date and one in total. They were used for the study on Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5 by Y Xiong, X Zeng, W Lai, J Liao, Y Chen, M Zhu, and K Huang, which was published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 7554 - 7576, 2024(22 March 2024). https://doi.org/10.1109/JSTARS.2024.3379522.<br>They were named IPTIS (individual pomelo tree image sample) datasets for short.</p>
Direct imaging of carbohydrate stereochemistry structural dataset
<p>Dataset includes. </p> <ol> <li>Training data for the NequIP model (all_4NPxG_mod_E.extxyz) including structures, energies and force components.</li> <li>Zip file (4NPxG_training_run.zip) containing training parameters (config.yaml) and metrics (.csv files) and the deployed NequIP model (deployed_model.pth) used for minima hopping in the corresponding study.</li> <li>Bayesian Optimization Structure Search results for conformers (alpha/beta-4-Nitrophenyl-D-Galacturonide_opt.extxyz) and isolated adsorbates (alpha/beta_isolated_adsorbates_opt.extxyz) on Au(111). DFT relaxed structures.</li> <li>CREST NADG conformers (crest_conformers_alpha.extxyz). </li> <li>Initial monolayer structure relaxations (4NPaG/4NPbG_monolayer_relaxation_every_fifth.extxyz). Every fifth geometry from the relaxation.</li> <li>Results from NequIP minima hopping for monolayer structures as trajectory files (alpha/beta_minima_hopping_nequip.traj). Contains also protonated NADG structures (alpha_protonated_minima_hopping_nequip.traj).</li> <li>Final NADG and NBDG monolayer structures with Hartree potentials and STM images simulated with FHI-aims (Final_NADG/NBDG_hartree_potential.cube, Final_NADG/NBDG_stm_01.cube) with the z-maps (Final_NADG/NBDG_stm_z_map.cube) for creating STM image contrast.</li> </ol>
Plant image identification application demonstrates high accuracy in Northern Europe dataset
<p><strong>Images and data for the study "Plant image identification application demonstrates high accuracy in Northern Europe"</strong></p> <p><strong>Details: Jaak Pärtel, Meelis Pärtel, Jana Wäldchen, Plant image identification application demonstrates high accuracy in Northern Europe, <em>AoB PLANTS</em>, Volume 13, Issue 4, August 2021, plab050, <a href="https://doi.org/10.1093/aobpla/plab050">https://doi.org/10.1093/aobpla/plab050</a></strong></p> <p>The data table displays Flora Incognita's identification results together with species and observations characteristics. All (3199) used images are included.</p> <p>The study was conducted in two parts: database and field study.</p> <p>Database study images have been taken from eBiodiversity database (https://elurikkus.ee/en) under Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). Please cite the original source for the images as well when using the dataset.</p> <p>Field study images were taken by Jaak Pärtel in 2020 in field conditions from different habitats across Estonia.</p>
Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset
<p><strong>Overview:</strong></p> <p>This repository contains the <strong>raw images</strong> for the dataset of <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>.</p> <p>This dataset contains <strong>5696 drone images</strong> (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.</p> <p>All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.</p> <p><strong>Usage:</strong></p> <p>Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal:</p> <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> <p><strong>File Numbering/Naming Scheme:</strong></p> <p>The pairs of RGB + Thermal images follow the simple numbering scheme of: <strong>RGB = Thermal + 1</strong>.<br> For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image.</p> <p>To perform the merging, we recommend using the <strong>merge_image_layers.py</strong> script provided by the associated <strong><a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a></strong> (see the scripts/alignment/ directory).</p> <p>For convenience, we have provided a CSV listing all annotated images in the <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>. The CSV format is as follows:</p> <pre><code>Flight,RGB,Thermal Flug_100,DJI_0048.jpg,DJI_0047_R.JPG Flug_100,DJI_0050.jpg,DJI_0049_R.JPG ...</code></pre> <p> </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>
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>
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>
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