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979 results for “image dataset”
Single-Cell Imaging Dataset: Hela FUCCI Cell Fluorescence Analysis
<p>Hela FUCCI Cell Dataset: Fluorescence Intensity and Segmentation</p> <p>The "Hela FUCCI Cell Dataset" is a comprehensive collection of fluorescence microscopy data capturing the fluorescence intensity of Hela FUCCI cells. The dataset encompasses a diverse range of cellular images acquired through fluorescence imaging techniques, offering valuable insights into the cellular behavior and fluorescent signal patterns.</p> <p>Contents:</p> <p>Fluorescence Intensity Data: The dataset includes fluorescence images of Hela FUCCI cells captured in both red and green channels. These images represent the intensity levels of cellular fluorescence signals.</p> <p>Purpose:<br> The dataset serves as a resource for researchers and scientists interested in cellular fluorescence analysis. It supports investigations into cellular dynamics, cell cycle studies, and fluorescence signal patterns. Researchers can utilize this dataset to develop and evaluate image processing, analysis, and machine learning techniques for cell detection and fluorescence quantification.</p> <p>Data Collection:<br> The data were collected using fluorescence microscopy techniques, capturing the distinct fluorescence signals emitted by Hela FUCCI cells. </p> <p>Usage:<br> Researchers can use this dataset to:</p> <p>Investigate fluorescence patterns and intensities of Hela FUCCI cells.<br> Develop and validate machine learning algorithms for cell segmentation and detection.<br> Explore cellular behaviors and dynamics under various experimental conditions.</p> <p>Citation:<br> If you use this dataset in your research, please cite the original source to acknowledge its contribution.</p> <p>Access and Availability:<br> The dataset is openly available through Zendo, accessible via the following link: https://zenodo.org/. Researchers are encouraged to explore, analyze, and contribute to the dataset's applications and advancements in cellular fluorescence analysis.</p>
CitrusUAT: A Dataset of Orange Citrus sinensis Leaves for Abnormality Detection Using Image Analysis Techniques
<p>This dataset provides a collection of color images taken from the orange leaves of <em>Citrus sinensis</em> (L.) Osbeck species with diseases, nutritional deficiencies, and pest symptoms, proper to develop abnormality detection algorithms based on digital image analysis techniques. The dataset comprises 953 color images divided into 12 classes of orange leaves: Healthy, Huanglongbing (HLB), Greasy spot, Iron deficiency, Magnesium deficiency, Manganese deficiency, Nitrogen deficiency, Zinc deficiency, Texas citrus mite, Red scale, Red scale sequelae, and Citrus leafminer. Each color image was segmented by a thresholding method to obtain a binary mask of the leaf region. Samples were analyzed by the quantitative real-time polymerase chain reaction (qPCR) diagnostic test to detect the <em>Ca.</em> L. asiaticus bacterium that causes HLB disease.</p>
Microwave Imaging dataset
<p>The content of this dataset has arrays named:</p> <ul> <li>Es: scattering data</li> <li>inputs: orthogonality sampling method (OSM) reconstructions</li> <li>xhi: contrast function</li> <li>outputs: intermediate gradient of contrast function</li> </ul>
SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs
<p>SRDTrans dataset: simulated calcium imaging data sampled at 30 Hz under different SNRs.</p>
Annotated dataset of microscope images of pollen grains from 40 beekeeping taxa
<p>The study of beekeeping flora and the analysis and identification of pollen collected by bees are important tools for beekeepers and researchers seeking to understand bee feeding habits and assess the ecological interactions between bees and plants. To identify the botanical origin of the pollen pellets collected by the bees, palynology method is mainly followed.</p><p>Pollen grains show great diversity, in terms of size, shape, symmetry and surface, as well as in terms of the number and type of their openings (apertures). They often present openings on their outer surface, serving as excellent diagnostic characters, as they show stability in their form and number. The most common types of openings are the pores, the sinuses, and the combination of the above, the anal canals. Pollen grains with pores are characterized as porate, those with sinuses as colpate, if they contain both as colporate. Depending on the number of openings, corresponding prefixes such as mono-, di-, tri- etc. precede the above terms. Also, the position of the openings, whether they are at the poles or in the equatorial zone, as well as their shape, are taken also into account.</p><p>Pollen size can be used as a diagnostic feature, but it shows great variability even within the same pollen grain. This is because it can be affected by various factors such as chemical treatment and the materials used in the creation of the preparations, genetic variability, etc. Specifically, a frequent phenomenon is the shrinking of pollen grains (harmomegathy or Wodehouse effect) resulting from the change in the bursting pressure of the cytoplasm during the hydration or dehydration of the pollen. Therefore, the degree of hydration is responsible for the actual shape and size of the pollen grains.</p><p>Considering all the above, a database was created including microscope images and characteristics (such as the type of pollen grain, the size, the type and number of openings, etc) of 40 taxa of major beekeeping importance.</p><p>Pollen in the form of pellets was crushed and a small amount was taken with special stainless steel forceps and then mixed with a drop of 20% glucose solution on a slide. Fuchsin solution was added and the preparation was spread over a 22 x 22 mm surface. The preparations were dried by gentle heating to 40°C, on a heating plate and a cover slip is placed to which a small amount of Entellan was added. All pollen grains were photographed on an optical microscope (Olympus SZX12), with lens 40× (Olympus DF PLAPO 1X DF), with a digital analysis camera (Olympus SC30), while a morphometry software (Image Pro Plus Software, V1.1.19) was used for their determination. For the microscopic identification of the pollen types, the collection of reference slides from the Laboratory of Apiculture of the Aristotle University of Thessaloniki, which is accredited to ISO 17025:2017, was used.</p><p>The dataset contains 3379 training captured microscope images of pollen grains from 40 major beekeeping taxa (class list can be found below) and 85 testing captured images. Polygon annotations (files train.json and val.json included) were created using LabelMe software and saved in COCO Annotation format.</p><p>Further information about the related project (SmartBeeKeep) can be found in the following article and presentation (please site if you use these data):</p><ul><li>Vasilios Liolios, Dimitrios Kanelis, Maria-Anna Rodopoulou, Chrysoula Tananaki (2023). A Comparative Study of Methods Recording Beekeeping Flora. Forests, 14(8), 1677; <a href="https://doi.org/10.3390/f14081677">https://doi.org/10.3390/f14081677</a> </li><li>Nikos Grammalidis, Andreas Stergioulas, Aggelos Avramidis, Konstantinos Karystinakis, Athanasios Partozis, Athanasios Topaloudis, Georgia Kalantzi, Chrisoula Tananaki, Dimitrios Kanelis, Vasilis Liolios, and Madesis Panagiotis "A smart beekeeping platform based on remote sensing and artificial intelligence", Proc. SPIE 12786, Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 127860C (21 September 2023); <a href="https://doi.org/10.1117/12.2681866%20">https://doi.org/10.1117/12.2681866</a> Event: Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 2023, Ayia Napa, Cyprus <a href="https://smartbeekeep.eu/files/rscyp23_sbk_paper.pdf">Author preprint available</a></li></ul><p><strong>Annotation - Latin name</strong></p><p>Anthemis - Anthemis sp.</p><p>Asphodelus - Asphodelus fistulosus</p><p>Brassica napus - Brassica napus</p><p>Castanea - Castanea sativa </p><p>Cephalaria - Cephalaria transsylvanica</p><p>Chenopodium - Chenopodium album</p><p>Cichorium intybus - Cichorium intybus</p><p>Cistus - Cistus creticus</p><p>Cistus salvifolius - Cistus salvifolius</p><p>Convolvulus - Convolvulus arvensis</p><p>Daucus - Daucus carota</p><p>Echium - Echium plantagineum</p><p>Erica - Erica manipuliflora</p><p>Hederahelix - Hedera helix</p><p>Helianthus - Helianthus annuus</p><p>Heliotropium - Heliotropium europaeum</p><p>Hypericum - Hypericum perforatum</p><p>Lavandula - Lavandula angustifolia</p><p>Ligustrum - Ligustrum japonicum</p><p>Matricaria - Matricaria chamomilla</p><p>Olea - Olea europaea</p><p>Paliurus - Paliurus spina-christi</p><p>Papaver - Papaver rhoeas</p><p>Pinus - Pinus sp.</p><p>Polugonum - Polygonum aviculare</p><p>Portulaca - Portulaca oleracea</p><p>Pyrus spinosa - Pyrus spinosa</p><p>Quercussp - Quercus coccifera</p><p>Rosmarinus officinalis - Rosmarinus officinalis</p><p>Rubus - Rubus ulmifolius</p><p>Silybum marianum - Silybum marianum</p><p>Sinapis - Sinapis arvensis</p><p>sonchus - Sonchus asper</p><p>Taraxacum officinale - Taraxacum officinale</p><p>Tamarix - Tamarix sp.</p><p>Tilia intermedia - Tilia sp.</p><p>Tribulus - Tribulus terrestis</p><p>Trifolium - Trifolium campestre</p><p>Verbascum - Verbascum nigrum</p><p>Vicia vilosa - Vicia villosa</p>
Dataset for "Multi-photon time-of-flight MLEM application for the positronium imaging in J-PET"
<p>Dataset used to reconstruct an image of 4-sources. Simulated using J-PET Geant4 and analyzed with the J-PET Framework.</p><p>In the form of <br>X position [cm], Y position [cm], Z position [cm], Time [ps]<br>for every hit in an event and as folows<br>deexcitation hit, first annihilation hit, second annihilation hit</p>
High-quality Image (NIR and RGB) Dataset Synchronized With Contact Vital Sings Recordings and Clinical Data of Stratified Healthy Population. Algorithms and AI Models to Obtain a Set of Vital Signs Im
ClinicalTrials.gov study NCT05947721. IPD Sharing: NO. Countries: 1. Publications: 2.
Hematoxylin-and-eosin-stained bladder urothelial cell carcinoma versus inflammation digital histopathology image dataset
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PRMI: A dataset of minirhizotron images for diverse plant root study
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Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
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Image dataset of disk diffusion assay scanned with the SIRscan system
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Dataset: lhp1 FLC-Venus time course imaging – Hybrid protein assembly-histone modification mechanism for PRC2-based epigenetic switching and memory
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CODEX multiplexed imaging cell datasets used for using STELLAR to transfer cell type annotations to other tissues and donors
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Geographical Distribution of Mammal Images (Flickr-Mammal dataset)
<p>This dataset is created based on the geographical distribution of common mammal images on Flickr.</p> <p>In this dataset, we provide the metadata of all images in this dataset, including their geotags, license status, URL to the images, and their corresponding countries and geographical regions (based on <a href="https://unstats.un.org/unsd/methodology/m49/">UN M49 Standard</a>).</p> <p>We also provide easy-to-use scripts for the user to download the images from their URLs.</p> <p>If you use this dataset in your research, we would appreciate a reference to the following paper:</p> <p>Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B Gibbons. "<a href="https://proceedings.icml.cc/static/paper_files/icml/2020/3152-Paper.pdf">The Non-IID Data Quagmire of Decentralized Machine Learning</a>." <em>Proceedings of the 37th International Conference on Machine Learning (ICML 2020)</em>.</p> <p>Bibtex entry</p> <pre><code>@incollection{icml2020_3152, author = {Hsieh, Kevin and Phanishayee, Amar and Mutlu, Onur and Gibbons, Phillip}, booktitle = {International Conference on Machine Learning ({ICML})}, pages = {5819--5830}, title = {The Non-{IID} Data Quagmire of Decentralized Machine Learning}, year = {2020} }</code></pre> <p> </p>
Dataset and software for "Time-resolved imaging of three-dimensional nanoscale magnetization dynamics"
<p>Dataset and software for "Time-resolved imaging of three-dimensional nanoscale magnetization dynamics", Donnelly, C., Finizio, S., Gliga, S. <em>et al.</em> Time-resolved imaging of three-dimensional nanoscale magnetization dynamics. <em>Nat. Nanotechnol.</em> (2020). https://doi.org/10.1038/s41565-020-0649-x</p>
Sensing and Imaging Dataset for Ground-based Adaptive Optics (SIDGAO)
<p>our synthetic Sensing and Imaging Dataset for Ground-based Adaptive Optics.</p> <p>This Project is Partially Supported by the National Natural Science Foundation of China(No.62005221) and the State Key Laboratory of Applied Optics(No.SKLAO2021001A04).</p>
Training datasets and final models from paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p> See paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation' for an explanation of how the models and datasets were created.</p> <p>The images are extracted from the following larger datasets using the RootPainter software:</p> <p>Nodules: http://doi.org/10.5281/zenodo.3753603</p> <p>Biopores: http://doi.org/10.5281/zenodo.3753969</p> <p>Roots: http://doi.org/10.5281/zenodo.3527713</p> <p> </p>
A dataset of long-term consistency values of resting-state fMRI connectivity maps in a single individual derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol
<p>This dataset contains preprocessed resting state fMRI data (.nii.gz) with accompanying confound files (.tsv) from the Single Individual volunteer for Multiple Observations across Networks (SIMON; http://fcon_1000.projects.nitrc.org/indi/retro/SIMON.html) dataset that has been minimally preprocessed using the NeuroImaging Analysis Kit (NIAK; http://niak.simexp-lab.org/build/html/PREPROCESSING.html). Preprocessing steps included: (1) Slice timing correction; (2) Estimation of rigid-body motion in fMRI runs, both within- and between sessions; (3) Linear or non-linear coregistration of the structural scan in stereotaxic space; (4) Individual coregistration between structural and functional scans; (5) Resampling of functional scans in stereotaxic space. Confound files (.tsv) have been included for purposes of scrubbing and regression of confounds using NIAK or other analysis software, allowing for further processing without conflicts.</p>
Dataset to replicate experiments in "Image Feature Learning with Genetic Programming" paper
<p>The zip file contains Dataset to replicate experiments in "Image Feature Learning with Genetic Programming" paper published at the PPSN 2020 conference.</p> <p>This package also contains a version of Lenet5 to classify the MNIST digits.</p> <p>MNIST dataset has been corrupted with salt noise. We made white a different proportion of the pixel at random (5% 10% 30% 40%).</p> <p> </p> <p> </p>
Wavelet Domain Compensation of Frequency Dispersion of UWB Electromagnetic Waves for Time-Reversal Imaging (dataset)
<p>These files are the simulation data used to create the figures illustrated in the relevant journal paper. Each filename indicates which figure number it relates to. These files are text files. The first row in each file describes the content of each of its columns.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.