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Dataset results
979 results for “image dataset”
Image Dataset for 'AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water'
<p>This dataset is presented in the following publication. Please cite this publication if you use the dataset.</p> <p><em>Jiyoon Yi, Nicharee Wisuthiphaet, Pranav Raja, Nitin Nitin, J. Mason Earles. (2023). AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water. Water Research, 120258. doi: <a href="https://doi.org/10.1016/j.watres.2023.120258">10.1016/j.watres.2023.120258</a></em></p>
Unmanned Aerial Vehicle (UAV) image dataset.
<p>The dataset contains 2,919 images and separated into five classes of car, taxi, truck, bus and motorcycle.<br> </p>
Datasets for 3D shape reconstruction from 2D microscopy images
<p>Here we publish two single cell datasets for 3D shape reconstruction from 2D microscopy images with a detailed description.</p>
Simulated Long-Film image dataset
<p>This dataset contains simulated 10,368 Long-Film images and vertebrae centroid labels from projection of public CT images. The CT images are from SpineWeb [1] and The Cancer Imaging Archive [2]. Details of usage and generation process can be found in [3].</p> <p>[1] B. Glocker, J. Feulner, A. Criminisi, D.R. Haynor, E. Konukoglu, Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans., Med. Image Comput. Comput. Assist. Interv. 15 (2012) 590–8. https://doi.org/10.1007/978-3-642-33454-2_73.<br> [2] K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle, L. Tarbox, F. Prior, The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository., J. Digit. Imaging. 26 (2013) 1045–57. https://doi.org/10.1007/s10278-013-9622-7.<br> [3] Huang Y, Jones CK, Zhang X, Johnston A, Waktola S, Aygun N, Witham TF, Bydon A, Theodore N, Helm PA, Siewerdsen JH, Uneri A. "Multi-perspective region-based CNNs for vertebrae labeling in intraoperative long-length images." Computer Methods and Programs in Biomedicine, 2022 (under review)</p> <p> </p>
Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (3/3)
<p>Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (3/3)</p> <p>This dataset contains images of Camera 3.</p>
Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (2/3)
<p>Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (2/3)</p> <p>This dataset contains images of Camera 2.</p>
Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (1/3)
<p>Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (1/3)</p> <p>This dataset contains images of Camera 1 and data used for analysis.</p>
Maize whole plant image dataset
<p>This dataset contains materials to reproduce Figure 5 that shows plant representations at different development stages (one to eight weeks after sowing) for top (a) and (b) side images, together with time courses of the number of pixels corresponding to plants extracted from side and top views (c). The following materials are available:</p> <p>1. `Image dataset`: raw image dataset of side and top RGB images of a single plant that can be used in the segmentation pipeline (https://github.com/openalea/eartrack)</p> <p>2. `Segmented image dataset`: output images of the segmentation pipeline</p> <p>3. `Image analysis features`: A csv file contatining all image analysis features from the image dataset provided above</p> <p>4. 'FIG5 dataset': small dataset of segmented images for building Figure 5a,b</p> <p> </p>
Few juveniles or males were collected. Only four males from groups 7, 8, 9, and 11, all in clade D, were included in the dataset. The male in Fig. 13E–H conforms to the general morphological description of males in Lobocriconema with an undifferentiated labial region, the absence of a stylet, a degenerate pharyngeal region, a FIGURE 7. SEM images of specimens representing clades D (A–H) and B (I). NID numbers are associated with unique specimens, all are females except image C. A) Lobocriconema sp., face view with conspicuous labial disc surrounded by irregular labial structure, Nine-Mile Prairie, Nebraska, NID 4533. B) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Big Thicket National Preserve, Texas, NID 4560. C) Lobocriconema sp., juvenile, head with visible submedian lobes, body scales with fine terminal projections, Spring Creek Prairie, Nebraska, NID 4514. D) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Nine-Mile Prairie, Nebraska, NID 4527 E) Lobocriconema sp., cephalic profile with protruding stylet, Nine-Mile Prairie, Nebraska, NID 4529. F) Lobocriconema sp., head profile lacking submedian lobes, Tunica Hills, Louisiana, NID 4574. G) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4533. H) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4526. I) Lobocriconema sp., face view lacking submedian lobes, Great Smoky Mountains National Park, Purchase Knob, NID 4570. in Species discovery and diversity in Lobocriconema (Criconematidae: Nematoda) and related plant-parasitic nematodes from North American ecoregions
Few juveniles or males were collected. Only four males from groups 7, 8, 9, and 11, all in clade D, were included in the dataset. The male in Fig. 13E–H conforms to the general morphological description of males in Lobocriconema with an undifferentiated labial region, the absence of a stylet, a degenerate pharyngeal region, a FIGURE 7. SEM images of specimens representing clades D (A–H) and B (I). NID numbers are associated with unique specimens, all are females except image C. A) Lobocriconema sp., face view with conspicuous labial disc surrounded by irregular labial structure, Nine-Mile Prairie, Nebraska, NID 4533. B) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Big Thicket National Preserve, Texas, NID 4560. C) Lobocriconema sp., juvenile, head with visible submedian lobes, body scales with fine terminal projections, Spring Creek Prairie, Nebraska, NID 4514. D) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Nine-Mile Prairie, Nebraska, NID 4527 E) Lobocriconema sp., cephalic profile with protruding stylet, Nine-Mile Prairie, Nebraska, NID 4529. F) Lobocriconema sp., head profile lacking submedian lobes, Tunica Hills, Louisiana, NID 4574. G) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4533. H) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4526. I) Lobocriconema sp., face view lacking submedian lobes, Great Smoky Mountains National Park, Purchase Knob, NID 4570.
Dataset for the paper "Ensemble optimization retrieval algorithm of hydrometeor profiles for the Ice Cloud Imager submillimeter-wave radiometer'
<p>1. "Retrieval_Database" file contains the pre-calculated retrieval database.</p> <p>2. "Algorithm_Input" file contains the input of the ensemble optimization retrieval algorithm.</p> <p>3. "TrueProfiles" file contains the true profiles corresponding to the input brightness temperatures. </p> <p>4. "Algorithm_Output" file contains the output of the ensemble optimization retrieval algorithm.</p>
Atmospheric Turbulence Image Dataset
<p>You can use this dataset for atmospheric turbulence mitigation studies</p>
Dataset - No Reference Image Quality assessment Scores for Humanities Online Repositories
<p>The dataset contains data on No-Reference Image Quality Assessment (NR-IQA) scores for online repositories in the humanities.</p>
Datasets for airglow image data and ionogram data obtained from Zhuhai Station (113.58°E, 22.35°N) on April 3, 2022.
<p>This dataset contains airglow image data (airglow_image.mat) and ionogram data (ionogram_data.mat) obtained from Zhuhai Station (113.58°E, 22.35°N) on April 3, 2022. The airglow images ranged from 12:30-20:04 UT. The ionograms ranged from 15:00-20:00 UT.</p>
Dataset and AMF images
Open the record for dataset details and reuse information.
GradDA – A novel dataset for investigating domain shifts in image classification
<p>A domain shift occurs when the testing data is drawn from a distribution different from that of the training dataset. This shift presents a significant challenge and may compromise the performance of machine learning models, which leads to poor generalization. Over the past years, various models have been developed and evaluated on benchmark datasets such as VisDA, Office-Home and DomainNet. These datasets consist of discrete domains with different object classes. However, a notable limitation when addressing the domain shift is the absence of data samples where the exact same object exists in both domains. </p> <p>We propose a new dataset designed to address this challenge. In particular, we introduce a domain shift from a purely synthetic style (grey object on white background) to a more realistic appearance (object with texture against a realistic background) with differential modifications, which enables the representation of the same object in both synthetic and real domains, consequently facilitating the analysis of a transition between the two domains. The dataset comprises five distinct classes (Airplane, Bicycle, Bus, Car, Train), with multiple objects per class. Additionally, each object is depicted from 20 different perspectives, resulting in a total of 101 images per perspective that captures the transition from pure synthetic to a more real-world-like domain. This dataset offers a unique opportunity to investigate the impact of domain shift on model performance in classification tasks, as it focuses solely on domain changes without other interfering effects. It is the objective of our work to trigger new discussions about the domain shift problem, and how it can be tackled with alternative data driven model designs.</p>
Technology comparison (image-based spatial transcriptomics)- annotated datasets
<p>This repository contains all the AnnData datasets, regionally annotated, used in the comparison of image-based spatial transcriptomics technologies (Marco Salas et al. 2024)</p>
Image Datasets for "Virtual tissue microstructure reconstruction across species using generative deep learning"
<p>Training and velautaion image datasets used in the manuscript "Virtual tissue microstructure reconstruction across species using generative deep learning"</p>
PLANT SPECIES RECOGNITION USING LEAF IMAGES AND CONVOLUTIONAL NEURAL NETWORKS (CAAR dataset, version 1)
<p>The CAAR dataset contains leaf images from plants obtained from the Arboreal Collection at Augusto Ribas Agricultural College (CAAR/UEPG). This plant collection is situated in Augusto Ribas College, located at the Ponta Grossa State University, Ponta Grossa, Paraná, Brasil. The images were taken using a smartphone camera with a resolution of 1659 x 2658 pixels and 24 bits of color depth. For each plant, images samples were collected using a white paper sheet background, varying the leaf orientation. The<br>number of plant species used to build the dataset is equal to 35. Data augmentation, using rotation and zoom, were used to increase the data size from 730 to 1986 images in this dataset.</p>
Tongue image dataset for Tri-Dhat classification in traditional Thai medicine
<p>Traditional Thai medicine (TTM) is an increasingly popular treatment option. Tongue diagnosis is a highly efficient method for determining overall health, as practiced by TTM practitioners. However, the diagnosis naturally varies depending on the practitioner's expertise. In this work, we propose tongue image analysis with raw pixels using artificial intelligence (AI) to support TTM diagnoses. The target classification of Tri-Dhat consists of three classes: Vata, Pitta, and Kapha. We utilized our own organized, genuine datasets collected from our university TTM hospital. Class balancing and data augmentation were conducted, and we present analysis approaches and experimental designs. Transfer learning techniques for various pretrained deep learning models were developed. We used two-tailed paired t-tests and single-factor ANOVA for performance comparisons. Our work demonstrated that the DenseNet121 and Xception models provided the most significant results with cropped image datasets, including DSLR-taken and mobile-taken images. Notably, model ensemble evaluations yielded the highest average predictions, achieving a precision of 0.94, an F1 score of 0.96, an accuracy of 0.96, a sensitivity of 0.96, and a specificity of 0.97, supported by a p-value of 0.0003 from ANOVA. We suggest that our methods could be effectively deployed in real-world scenarios to aid TTM practitioners in their diagnoses.</p>
Validation and Test Datasets for "High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma"
<p>This deposition contains the validation and test dataset for our study "High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma".</p> <p>The training dataset for this study can be found at the following DOI: [<strong>10.5281/zenodo.12636426</strong>].<br><br></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.