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Enhancing Gen3 for Clinical Trial Time Series Analytics and Data Discovery: A Data Commons Framework for NIH Clinical Trials

Year Published: 2025
Authors: Meredith C. B. Adams, MD, MS Colin Griffin, BE Hunter Adams, BS Stephen Bryant, AA Robert W. Hurley, MD, PhD Umit Topaloglu, PhD

This work presents a framework for enhancing Gen3, an open-source data commons platform, with temporal visualization capabilities for clinical trial research. We describe the technical implementation of cloud-native architecture and integrated visualization tools that enable standardized analytics for longitudinal clinical trial data while adhering to FAIR principles. The enhancement includes Kubernetes-based container orchestration, Kibana-based temporal analytics, and automated ETL pipelines for data harmonization. Technical validation demonstrates reliable handling of varied time-based data structures, while maintaining temporal precision and measurement context. The framework’s implementation in NIH HEAL Initiative networks studying chronic pain and substance use disorders showcases its utility for real-time monitoring of longitudinal outcomes across multiple trials. This adaptation provides a model for research networks seeking to enhance their data commons capabilities while ensuring findable, accessible, interoperable, and reusable clinical trial data.