Hacktive Matter: data-driven discovery through hackathon-based cross-disciplinary coding
Megan T. Valentine, Rae M. Robertson-Anderson

TL;DR
This paper presents a hackathon-based platform to train interdisciplinary researchers in big data and community coding, fostering collaboration and developing high-throughput analysis tools for active matter and biomaterials research.
Contribution
It introduces a novel hackathon model that promotes cross-disciplinary collaboration, training, and software development for data-driven discovery in active matter research.
Findings
Participants improve data analysis skills
Development of standardized analysis workflows
Enhanced interdisciplinary collaboration
Abstract
The past decade has seen unprecedented growth in active matter and autonomous biomaterials research, yielding diverse classes of materials that promise revolutionary applications such as self-healing infrastructure and self-sensing tissue implants. However, inconsistencies in metrics, definitions, and analysis algorithms across research groups, as well as the high-dimension data streams, has hindered identification of performance intersections. Progress in this arena demands multi-disciplinary team approaches to discovery with scaffolded training and cross-pollination of ideas, and requires new learning and collaboration methods. 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…
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Taxonomy
TopicsBiomedical and Engineering Education
