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Medical imaging (X-ray, CT, MRI), electronic health records, clinical trials, ECG/EEG, pathology
13,186 datasets
State-level health burden data for diabetes includes prevalence, incidence, and rates of associated conditions like hypertension and stroke. Statistics derive from self-reported surveys, hospitalization records, and Medicare beneficiary files. The dataset is compiled by the U.S. Department of Health & Human Services and was last updated in 2026.
A dataset concerning heart disease, published on Kaggle. The specific variables, collection method, and time period are not detailed in the available metadata.
Featuring miRNA expression data from lung tissue samples of polytraumatized pigs, comparing two surgical treatment methods (Early Total Care and Damage Control Orthopedics) and a drug-based treatment group. The study involved 20 pigs across four experimental groups, analyzing miRNAs associated with lung function, inflammation, and fibrosis. Data was collected over a 72-hour monitoring period in an ICU setting.
A dataset titled 'Retinal Clearing Paper' authored by Jonathan Soucy of Baranov-Soucy. It was published on the Dataverse platform and last updated on April 22, 2026. The dataset's content likely pertains to retinal imaging or clearing techniques used in ophthalmic research.
A collection of image patches extracted from whole-slide images of lung tissue. The dataset is hosted on Kaggle, but its specific source institution, collection date, and total volume are not detailed in the provided metadata. The content likely contains processed tiles from larger medical scans for machine learning applications.
Filtered Lung WSI Patches v1 is a collection of image tiles extracted from whole-slide images of lung tissue. It was published on Kaggle, but the author, organization, and specific details about its creation are unknown. The dataset's size, format, and exact content require verification after download.
A collection of image patches likely extracted from whole-slide images (WSI) of lung tissue. The dataset is hosted on Kaggle, but its specific source, size, and creation details are not provided. The 'v3' in the title suggests it is a third version of a filtered patch collection.
Filtered Lung WSI Patches v7 is a collection of image patches derived from whole-slide images of lung tissue. The dataset is hosted on Kaggle, but its specific size, creation date, and author are not detailed in the provided metadata. The title suggests it is the seventh version of a filtered set of patches, likely intended for computational pathology tasks.
A collection of image patches likely derived from Whole Slide Images (WSI) of lung tissue. The dataset is hosted on Kaggle and appears to be the fourth version of a filtered set. Specific details on the number of patches, source institutions, and collection dates are not provided in the available metadata.
A collection of image patches likely extracted from whole-slide images of lung tissue. The dataset is hosted on Kaggle, but its exact size, source, and creation date are unspecified. The 'v5' in the title suggests it is a fifth version or iteration of a filtering process.
Filtered Lung WSI Patches v6 is a dataset of image patches likely extracted from whole-slide images (WSI) of lung tissue. The dataset is hosted on Kaggle, but its author, organization, and creation date are unknown. The specific number of patches, their annotations, and the source of the original slides are not detailed in the available metadata.
Filtered Lung WSI Patches v9 is a collection of image patches derived from Whole Slide Images of lung tissue. The dataset is hosted on Kaggle, but specific details about its size, creation date, and author are not provided in the available metadata. Its content likely relates to computational pathology and medical image analysis.
Kaggle hosts this collection of Whole Slide Image patches focused on lung tissue. The dataset title suggests it contains processed image segments, likely for computational pathology tasks. Specific details on the number of patches, source institution, and creation date are not provided in the available metadata.
Coastal waters of Hawaii contain data on phaeopigments and other chemical properties, collected from 1989 to 1994. Measurements were taken using salinometer, fluorometer, and other instruments. The Hawaii State Department of Health submitted this data as part of the Mamala Bay Study project.
ANODE-OncoGen High-Fidelity Synthetic Records provide a high-density .jsonl collection integrating clinical history with deep genomic markers for Non-Small Cell Lung Cancer. The dataset was created by AnodeAI and was last updated in March 2026.
Patient-level data for breast cancer risk classification. The dataset includes lifestyle and hormonal factors for each patient. Its source, size, and specific collection details are not provided.
Comprising 160 successful computer-use agent trajectories comprising 1,378 total steps, generated by markov-ai using the Gemini 3 Flash Preview model on OSWorld tasks. Updated in February 2026, the collection focuses on fully completed tasks (score 1.0) across software domains including Chrome and GIMP.
A study of 142 hospitalized adults of both genders compared actual body measurements with estimates from formulas. Anthropometric measurements included body weight, height, knee height, arm length, span, demi-span, recumbent height, and several circumferences and skinfold thickness. The data was used to analyze the accuracy of different estimation formulas, finding most differed significantly from actual measurements except for height in men estimated via knee height.
A methodological paper by Mats Julius Stensrud of Harvard University proposes separable effects for causal inference in time-to-event settings with competing events. The approach is illustrated using data from a randomized clinical trial on estrogen therapy for individuals with prostate cancer. The dataset likely contains patient-level event times and treatment assignments.
This dataset contains 10,000 records across 20 countries from 2020 to 2024, with 34 features related to workplace mental health indicators, stress factors, and employee well-being metrics. It is designed for ML applications predicting burnout risk and identifying workplace stressors.