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Medical imaging (X-ray, CT, MRI), electronic health records, clinical trials, ECG/EEG, pathology
12,367 datasets
figshare admin karger published a clinical case report on figshare in June 2026. The report details a 68-year-old male patient diagnosed with GFAP astrocytopathy, presenting with transient visual obscurations, bilateral optic disc edema, and systemic symptoms. The 1.2 MB PDF includes clinical examination, MRI findings, CSF analysis, and treatment outcomes.
figshare admin karger published a supplementary document for a medical case report on 2026-06-03. The 104.6 KB DOCX file details the case of a 78-year-old man with ulcerative colitis complicated by large-vessel giant cell arteritis. It describes the diagnostic imaging and treatment course, including the use of tofacitinib.
93 patients with severe aortic stenosis undergoing TAVI evaluation provided data for this study comparing strain measurements from cardiac CT and echocardiography. The dataset, authored by Vitaliy Androshchuk and last updated in May 2026, includes left ventricular, left atrial, and right ventricular global longitudinal strain values. It examines the agreement between imaging modalities and the association of strain with pulmonary hypertension.
Wenxin Liang published a clinical research dataset on figshare in 2026. It contains retrospective data from 173 patients with esophageal squamous cell carcinoma (ESCC) treated at Fujian Medical University Union Hospital between 2013 and 2018. The data supports analysis of lymph node regression grade as a prognostic factor for overall and disease-free survival.
A retrospective cohort study of gastroenterology inpatients at Nanchong Central Hospital between September 2020 and December 2025. The dataset, created by Yiqian Liang, compares the incidence of in-hospital acute cerebral infarction between patients with and without acute gastrointestinal bleeding and develops a six-factor predictive nomogram.
A 5.5 KB Excel file containing metric values for brain tumor segmentation from a validation set of 188 MRI scans. The dataset was created by Muthulakshmi Kirubakaran and last updated on 2026-05-18. It corresponds to a study proposing a GA-ResUNetGAN model for segmenting tumor core, whole tumor, and enhancing tumor subregions.
Outcomes for COVID-19 testing cohorts in New York City, organized by specimen collection date. The dataset includes columns for the number of deaths, tests, confirmed cases, hospitalizations, specimen date, and extract date. It is hosted by data.cityofnewyork.us and was last updated on May 21, 2026.
A study of 10 subjects with treatment-refractory Obsessive-Compulsive Disorder (OCD) who received Deep Brain Stimulation (DBS) surgery. The dataset includes pre- and post-surgery assessments for 8 participants and coded narrative themes from all 10, authored by Emily Hemendinger and last updated on 2026-05-29. It analyzes correlations between quality of life scores and OCD symptom severity.
Aishwarya Iyer published a dataset on figshare in 2026 containing clinical characteristics related to Porto-sinusoidal vascular disease (PSVD). The 5.5 KB XLS file likely contains data used for gene set enrichment and network analysis to identify dysregulated pathways in this rare liver disease. The dataset is licensed under CC-BY-4.0.
A 1.4 MB DOCX file authored by Hugo Geerts, last updated in April 2026, presents a quantitative systems pharmacology model for Alzheimer's Disease. The model accounted for 70% and 50% of the variance in observed plasma p-tau181 and Clinical Dementia Rating-Sum Of Boxes changes, respectively, across trials of seven amyloid antibodies. It provides antibody-specific normalized decreases in plasma p-tau181 and hypotheses on the impact of disease pathology and gender on functional outcomes.
A 2026 study by Ratiu and LaCroix compares short-term and working memory performance between 36 individuals with mild traumatic brain injury (mTBI) and 36 matched neurotypical controls. The dataset likely contains results from verbal and nonverbal memory tasks analyzed using ANOVA and hierarchical cluster analysis. It was published in the American Journal of Speech-Language Pathology and shared on figshare.
Results from an ablation study for a graph contrastive learning framework (EC-GCL) applied to resting-state fMRI data from 1,160 participants, including 597 patients with major depressive disorder and 563 healthy controls. The dataset, authored by Dan Long and last updated on 2026-05-05, contains results that achieved a diagnostic classification AUC of 71.2%. The study identified key brain regions linked to MDD pathophysiology, such as the dorsolateral superior frontal gyrus, thalamus, and insula.
Dan Long's dataset contains functional connectivity matrices derived from resting-state fMRI data for 1,160 participants, including 597 patients with major depressive disorder and 563 healthy controls. The data was used to evaluate a novel Graph Contrastive Learning framework (EC-GCL) against conventional machine learning and GNN models. The dataset was last updated on May 5, 2026.
1,160 participants, including 597 patients with major depressive disorder and 563 healthy controls, provided resting-state fMRI data for a novel graph contrastive learning framework. The framework, developed by Dan Long and last updated in May 2026, achieved a diagnostic classification AUC of 71.2%. It identified key brain regions like the dorsolateral superior frontal gyrus, thalamus, and insula linked to MDD pathophysiology.
Pengfei Zhang published a dataset on figshare in May 2026 representing primary headache syndromes from the International Classification of Headache Disorders (ICHD3) in matrix form. The 42.5 KB CSV file encodes headache phenotypes and diagnostic characteristics as a binary matrix. This mathematical representation enables automated diagnosis and analysis of the classification criteria.
A matrix representation of primary headache syndromes from the International Classification of Headache Disorders (ICHD3). The dataset encodes diagnostic criteria as a biadjacency matrix, enabling automated diagnosis and analysis. It was created by Pengfei Zhang and last updated on 2026-05-11.
A matrix representation of primary headache diagnoses from the International Classification of Headache Disorders (ICHD-3). The dataset encodes headache phenotypes and diagnostic characteristics as a biadjacency matrix for automated analysis. It was created by Pengfei Zhang and published on figshare under a CC-BY-4.0 license.
A matrix representation of primary headache diagnoses from the International Classification of Headache Disorders (ICHD3). The dataset encodes headache phenotypes and their diagnostic characteristics as a binary matrix to enable automated analysis. It was created by Pengfei Zhang and last updated in May 2026.
A 33.0 KB matrix encodes primary headache diagnoses from the International Classification of Headache Disorders (ICHD3) as logical statements. Pengfei Zhang published this dataset on figshare in 2026, demonstrating that headache phenotypes can be represented in a 63-dimensional vector space. The matrix format enables automated diagnosis and analysis of classification criteria.
63 basis vectors span the mathematical space of all primary headache phenotypes according to the International Classification of Headache Disorders. This 42.4 KB dataset encodes the ICHD3 classification as a biadjacency matrix, enabling automated diagnosis and analysis. The work by Pengfei Zhang, last updated in 2026, translates clinical criteria into a logical structure for computational exploration.