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Hacktive matter: data-driven discovery through hackathon-based cross-disciplinary coding
Megan T Valentine1, Rae M Robertson-Anderson2
1Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, CA 93106, USA. valentine@engineering.ucsb.edu.
Soft Matter
|June 25, 2025
Summary
A novel hackathon platform trains scientists in big data analysis and collaborative coding for autonomous biomaterials research. This approach fosters interdisciplinary teamwork and develops high-throughput analysis software for complex materials innovation.
Area of Science:
- Soft Matter Physics and Materials Science
- Biomaterials Engineering
- Computational Biology and Data Science
Background:
- Rapid advancements in active matter and autonomous biomaterials enable dynamic functionalities like self-healing and self-sensing.
- Research is hampered by inconsistent metrics, definitions, and analysis algorithms, alongside complex, high-dimensional data.
- Progress requires interdisciplinary collaboration, novel training, and effective learning methodologies.
Purpose of the Study:
- To establish a hackathon platform for training scientists and engineers in big data, interdisciplinary collaboration, and community coding.
- To design and beta-test high-throughput (HTP) biomaterials analysis software and workflows.
- To foster a collaborative environment for developing and optimizing data-driven analysis methods in soft matter research.
Main Methods:
- Implementation of a hackathon model with a flat hierarchy, pairing participants of diverse experience levels.
- Structured learning modules including tutorials, small group coding sessions, and facilitated discussions.
- Collective contribution to data acquisition, processing, ideation, coding, testing, and dissemination of HTP software.
Main Results:
- Successful training of participants in 'big data' analysis, interdisciplinary teamwork, and community coding practices.
- Development and beta-testing of HTP biomaterials analysis software and workflows.
- Establishment of teams, benchmarks, and community standards for continued research and development.
Conclusions:
- The hackathon platform effectively trains future scientists in critical data-driven analysis for complex materials.
- This model promotes efficient algorithm development, enhances collaboration, and sets standards for the soft matter community.
- The approach is vital for accelerating innovation in autonomous biomaterials and active matter research.
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