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Hacktive matter: data-driven discovery through hackathon-based cross-disciplinary coding
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DOI：10.1039/D5SM00401B.png)
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Abstract
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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.
Keywords:
active matter
biomaterials
interdisciplinary collaboration
data-driven analysis
hackathon platform
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2.8
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