The past decade has seen unprecedented growth in active matter and autonomous biomaterials research. Yet, inconsistent metrics, definitions, and analysis algorithms across research groups, as well as the high-dimensionality of data streams, has hindered identification of performance intersections among such dynamic systems. To address this challenge, we have developed a hackathon platform, as part of our DMREF project and supported by an MGI Supplement. Through hackathons, we train future scientists and engineers in ‘big data’, interdisciplinary collaboration, and community coding to design and beta-test high-throughput (HTP) biomaterials analysis software and workflows. We enforce a flat hierarchy, from high school students to faculty to collectively contribute and collaborate. With clearly-defined goals and deliverables, participants achieve success through a series of tutorials, small group coding sessions, facilitated breakouts, and large group report-outs and discussions.

Our hackathons provide a powerful model for the soft matter community to educate and train students and collaborators in cutting edge data-driven analysis, which is critical for future innovation in complex materials research.

Notably, this paper, which outlines our designs, methods, and insights, is the inaugural offering for a new education/tutorial article format for Soft Matter.