CyberTraining: Pilot: Broadening Advanced CI and AI/ML Adoption in the Biological Sciences through a Distributed Open-Source Training Platform

Modern biological research increasingly relies on large-scale computing, artificial intelligence, and machine learning algorithms to analyze complex data sets. However, many university biology programs lack formal coursework in advanced computational methods, placing the burden on each independent researcher to learn the computing skills necessary to conduct their research. This knowledge gap limits scientific progress and the effective utilization of national computing resources. This project addresses this fundamental problem by developing an accessible, open-source training platform specifically designed for life scientists, providing hands-on, interactive training directly on the computing systems that scientists use for their day-to-day research needs. This effort will help serve the national interest by advancing research and discovery in medicine and healthcare, environmental science, and wildlife conservation. The goal of this project is to develop and deploy an open-source training platform as a native interactive application within the Open OnDemand web portal framework. To ensure portability across various supercomputing systems, the team of researchers will utilize containerized execution environments tightly coupled with a split-screen instructional interface. The technical approach involves developing a frontend application that integrates directly with job scheduling software, allowing users to run computations and follow instructional materials simultaneously without switching interfaces. Research activities include developing the software architecture, creating customized software containers for each lesson, and designing a modular curriculum that ranges from basic system navigation to advanced applications like deep learning for biological imaging data and genomic sequence analysis. Additionally, the project will conduct comprehensive beta testing and host workshops to teach system facilitators how to deploy the platform at their own institutions. Ultimately, this project contributes a highly extensible, portable educational framework that lowers the barrier to entry for utilizing advanced computational resources, providing a seamless transition for users from training directly to applied research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.