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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.<\/jats:p>","DOI":"10.3389\/fdgth.2025.1570009","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T10:46:37Z","timestamp":1753267597000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Gen3 for clinical trial time series analytics and data discovery: a data commons framework for NIH clinical trials"],"prefix":"10.3389","volume":"7","author":[{"given":"Meredith C. 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