Hacktive matter: data-driven discovery through hackathon-based cross-disciplinary coding†

Abstract

The past decade has seen unprecedented growth in active matter and autonomous biomaterials research, yielding diverse classes of materials capable of flowing, contracting, bundling, de-mixing, and coalescing. These innovations promise revolutionary applications such as self-healing infrastructure, dynamic prosthetics, and self-sensing tissue implants. However, inconsistencies in metrics, definitions, and analysis algorithms across research groups, as well as the high-dimensionality of experimental data streams, has hindered the identification of performance intersections among such dynamic systems. Progress in this arena demands multi-disciplinary team approaches to discovery, with scaffolded training and cross-pollination of ideas, and requires new methods for learning and collaboration. To address this challenge, we have developed a hackathon platform to train future scientists and engineers in ‘big data’, interdisciplinary collaboration, and community coding; and to design and beta-test high-throughput (HTP) biomaterials analysis software and workflows. We enforce a flat hierarchy, pairing participants ranging from high school students to faculty with varied experiences and skills to collectively contribute to data acquisition and processing, ideation, coding, testing and dissemination. 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. These modules facilitate efficient iterative algorithm development and optimization; strengthen community and collaboration skills; and establish teams, benchmarks, and community standards for continued productive work. 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.