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54 results for “image diversity”
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.
MedSegBench: A Comprehensive Benchmark for Medical Image Segmentation in Diverse Data Modalities
<p>We split the dataset into two parts due to the maximum uploaded file limit. You can find other data in Version 1.</p> <p>This dataset is an article study and is under evaluation.</p> <p><code>You can access the article on <a href="https://www.nature.com/articles/s41597-024-04159-2">Nature</a>.</code></p> <p><code>Trained model weights and detailed prediction results for each dataset (<a href="../records/13381081" target="_blank" rel="noopener">Zenodo</a>)</code></p>
PMcardio ECG Image Database (PM-ECG-ID): A Diverse ECG Database for Evaluating Digitization Solutions
<p>The dataset presents the collection of a diverse electrocardiogram (ECG) database for testing and evaluating ECG digitization solutions. The Powerful Medical ECG image database was curated using 100 ECG waveforms selected from the PTB-XL Digital Waveform Database and various images generated from the base waveforms with varying lead visibility and real-world paper deformations, including the use of different mobile phones, bends, crumbles, scans, and photos of computer screens with ECGs. The ECG waveforms were augmented using various techniques, including changes in contrast, brightness, perspective transformation, rotation, image blur, JPEG compression, and resolution change. This extensive approach yielded 6,000 unique entries, which provides a wide range of data variance and extreme cases to evaluate the limitations of ECG digitization solutions and improve their performance, and serves as a benchmark to evaluate ECG digitization solutions.<br><br>PM-ECG-ID database contains electrocardiogram (ECG) images and their corresponding ECG information. The data records are organized in a hierarchical folder structure, which includes metadata, waveform data, and visual data folders. The contents of each folder are described below:<br><br></p> <ul> <li><strong>metadata.csv:</strong> <br>This file serves as a key-to-key bridge between the image data and the corresponding ECG information. It contains the following columns: <ul> <li><strong>Image name: </strong>image name with extension,</li> <li><strong>ECG ID:</strong> this ID corresponds to the specific ECG identifier from the original PTB-XL dataset. Under this ID you can find a cutout array in the <em>leads.npz </em>and <em>rhythms.npz,</em></li> <li><strong>Image relative path: </strong>relative path to the image in question,</li> <li><strong>Image page: </strong>page number of the particular image (starting from 0),</li> <li><strong>ECG number of pages: </strong>number of pages in the whole ECG,</li> <li><strong>ECG number of columns per page: </strong>number of columns per page in the ECG,</li> <li><strong>ECG number of rows per page: </strong>number of rows in the ECG,</li> <li><strong>ECG number of rhythm leads: </strong>number of rhythms in the ECG,</li> <li><strong>ECG format: </strong>short version of the ECG format.</li> </ul> </li> <li><strong>data </strong>folder: <ul> <li><strong>leads.npz: </strong>NPZ file containing all underlying cutout lead signals; each signal is there under its ECG ID.</li> <li><strong>rhythms.npz:</strong> NPZ file containing all underlying rhythm signals; each signal is there under its ECG ID. If no rhythm lead is in the ECG, you will find an empty array in the NPZ.</li> </ul> </li> <li><strong>visual_data</strong> folder: <br>This folder contains subfolders for various image data, including augmented photos and visualization and different types of photos of ECG printouts. The subfolders are organized based on the specific augmentation or type of photograph. These folders contain images with various augmentation settings, such as different levels of blur, brightness, contrast, padding, perspective transformation, resolution scaling, and rotation. The database is organized in a way that allows for easy navigation and understanding of the different augmentations applied to the image data. Each of these subfolders contains images relevant to the specific augmentation or type of photograph. The <em>metadata.csv</em> file provides a direct link to each image and its associated ECG information.</li> </ul>
PRMI: A dataset of minirhizotron images for diverse plant root study
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Lebensspuren and benthic fauna diversity and density data obtained from KuramBio 2012 expedition still images (50 still images per 8 deep-sea stations)
<p>Lebensspuren and total benthic fauna diversity and density data generated by the random selection of 50 still images per station of the KuramBio 2012 expedition (Stations 3-6, 8-11; the depth of the stations ranged from 4,868 m to 5,768 m). Annotation and data reports were conducted with BIIGLE 2.0. Still images belong to videos where the Ocean Floor Observation System (OFOS) was deployed. For more information see the cruise report (RV Sonne cruise SO223; doi:10.1016/j.dsr2.2014.11.001) and Miguez-Salas et al., (2023).</p><p> </p>
Dataset for Phase-Diverse Imaging and Models for Real-World Aberration Correction
<p>The work is currently submitted for publication.</p>
Kümmerer et al.; Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass; open data
<p>The file contains data on cover crop biomass and canopy heights determined by ruler method, high-, and low-resolution imaging via unmanned aerial vehicle and structure-from-motion approach from an experimental field site in Triesdorf, Germany. The article to this data set is published in the journal MDPI Remote Sensing: Kümmerer, R.; Noack, P.O.; Bauer, B. Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass. Remote Sens. <strong>2023</strong>, 15, 1520. https://doi.org/10.3390/rs15061520</p>
Alzheimer's PET Imaging in Racially/Ethnically Diverse Adults
ClinicalTrials.gov study NCT03706261. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Tau Pet Imaging in the Aging Brain Cohort Dedicated to Diversity Study
ClinicalTrials.gov study NCT05393388. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Early Postprocedural Susceptibility-Weighted Imaging and Vessel-Wall Imaging For Prediction of Complications After Flow-Diversion Treatment
ClinicalTrials.gov study NCT05716360. IPD Sharing: NO. Countries: 1. Publications: 0.
Retinal Metabolic Imaging in a Diverse Population
ClinicalTrials.gov study NCT03442816. IPD Sharing: NO. Countries: 1. Publications: 0.
Quantitative phase imaging with temporal kinetics predicts hematopoietic stem cell diversity
GEO Series GSE286255. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Data for Efficient detection of fishes in underwater images from diverse reef ecosystems using deep learning (reviewer version)
<div> <div> <div> <p>FishForYolo.zip comprises images in .jpeg format along with normalized labels in YOLO format and non-normalized labels. This dataset is utilized for both training and testing purposes.</p> </div> </div> </div> <p>The shooting.csv file contains the mapping between videos and their respective shooting instances, which denote all videos captured at a particular location and time. It is used for data analysis.</p> <p>split.yaml delineates the partitioning of data into training, validation, and testing sets. This file is integral for the model to access annotations appropriately during its execution.</p> <p>labels_log.csv contains the annotations in CSV format, supplemented with additional details such as bounding box area or label region. It is instrumental for conducting thorough data analysis.</p> <p>SU_preds.zip contains the predictions of our best model on the test set (in the labels directory). The directory structure mirrors that of FishForYolo.zip. It also contains a file "all_preds.csv" which contains the predictions in CSV format.</p>
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images
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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.