OpenTrialLens

OpenTrialLens is an early-stage open-source project launched publicly through GitHub Pages, GitHub, LinkedIn, Hashnode, and HackerNoon submission. Current reach is still developing, with initial distribution through my professional network and public technical writing. The project is designed for repeatable public access: anyone can use the live dashboard without installation, review the GitHub repository, and test it with ClinicalTrials.gov data or CSV uploads. I plan to track GitHub views, stars/forks, dashboard visits, article reads, and user feedback as adoption grows.

OpenTrialLens is for data engineers, data analysts, clinical analytics teams, health-tech builders, students, and researchers who work with public clinical trial data and need a fast way to inspect data quality before analysis. It is especially useful for people evaluating ClinicalTrials.gov datasets, building healthcare dashboards, preparing analytics-ready files, or learning practical data quality patterns. The project currently has no commercial customers; it is an open-source public-interest tool intended for technical users and life sciences data professionals.

Other, OpenTrialLens uses a custom open-source stack: JavaScript, HTML, CSS, GitHub Pages, CSV parsing, browser-based data visualization, and the public ClinicalTrials.gov API. The companion OpenTrialDQ toolkit uses Python, PySpark, pytest, and metadata-driven data quality rules for validating clinical trial datasets.

Evidence of Traction OpenTrialLens is newly launched and currently in early public validation. Public evidence includes: Live dashboard: https://akhilachanubala-alt.github.io/OpenTrialDQ/opentriallens/ GitHub repository: https://github.com/akhilachanubala-alt/OpenTrialDQ Technical article: https://lifesciencesdataengineering.hashnode.dev/introducing-opentriallens-a-dashboard-for-clinical-trial-data-quality-and-visual-insights Related implementation article: https://lifesciencesdataengineering.hashnode.dev/designing-reusable-data-quality-checks-for-public-clinical-trial-data-with-pyspark I have also shared the project on LinkedIn and submitted it to HackerNoon, where the editorial team redirected it to the Proof of Usefulness contest as a better fit for a live technical project. Current traction is early, but the project is publicly accessible, documented, and ready for users to test without installation.