Work

COVID Data for Shared Learning (CDSL): a multimodal publicly accessible dataset of electronic health records and chest imaging from hospitalized COVID-19 patients

Year: 2026

Type: data-paper

Abstract: Comprehensive, high-resolution, and accurate datasets are critical for advancing COVID-19 research. However, most early pandemic datasets were limited to demographic data and sparse clinical information, often lacking diagnostic imaging tests. We present COVID Data for Shared Learning (CDSL), a multimodal publicly accessible database of patients hospitalized with COVID-19 in Spain. This dataset includes de-identified medical data from 4,479 hospitalization episodes, represented by distinct patient_id values, covering electronic health records with demographics, admissions, diagnoses, clinical and treatment information, and radiological images, specifically 4,608 X-ray images from 1,616 episodes and 1,440,156 computed tomography slices from 784 episodes. Comprehensive anonymization techniques were implemented by removing protected health information to ensure patient privacy, and extensive technical validation was conducted to confirm data completeness, internal consistency, and image conversion quality. Open-source code for data preprocessing, metadata extraction, and database deployment is publicly available to support reproducibility and community-driven improvement. Early in the pandemic, CDSL was shared to support timely COVID-19 research and became one of the earliest publicly accessible multimodal COVID-19 datasets available to the international research community. CDSL aims to promote global collaboration by sharing clinical data to advance healthcare research and drive innovation in personalized medicine and public health.

Source: Scientific Data

DOI: 10.1038/s41597-026-08184-1