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

Megan T Valentine1, Rae M Robertson-Anderson2

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Summary
This summary is machine-generated.

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.

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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.