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The Red Team

Explore ways to store, visualize, reproduce, and share biological data effectively. Topics include data validation, tools for inquiry, and improving computational work in the biomedical field. Support interdisciplinary training and robust software creation.

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The Red Team

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  1. The Red Team Gwen Jacobs Ed Lazowska

  2. What biologists want … • Can I evaluate an experimental design? • Can I store the results? • Can I visualize the results? • Can I reproduce the results? • Can I make inquiries? • Can I share and build upon data, tools, results?

  3. Can I store the results? • Data validation / quality control • Partial data • Errors in the data • Flamingly false data • Synonyms and homonyms • Context in which the data was gathered • Storing/retrieving combinatorial structures • Shared repositories

  4. Can I visualize the results? • Multi-dimensional data visualization is the challenge • Need time as a variable

  5. Can I reproduce the results? • Jill’s talk goes here

  6. Can I make inquiries? • Data mining • Non-parametric statistics • Content-based image retrieval • Standards: yes or no?

  7. Can I share data, tools, results? • Ontologies / semantics • Dealing with synonyms/homonyms • Standards: yes or no? • Yes: Can’t we all just get along? • No: Standards impede innovation; what we need is technologies that would allow ontologies to interoperate – schema mapping etc. • (cont’d …)

  8. How to make the best algorithms known • How to make tools that are usable by other than the developer, and that can interoperate • Data integration / federation • Searching the intergalactic knowledge base

  9. What can we do? • Fundamentally change the structure of the biomedical enterprise • Make computing explicit • Improve the peer review of computational work • Adequately fund the Roadmap • Fund algorithm and tool development where there is a clear biological driver • Create alternative funding models for hardening software • New panels, new panelists

  10. Define “challenge problems” • “Here are 3 large databases, here are 3 tough questions, whoever’s first wins” • Use your own tools • Tests tool capabilities • Have someone else use your tools • Tests tool usability

  11. Support training • Hourglass model • Broad at the undergraduate level • Narrow and deep at the graduate level • Broadening again post-graduate • Undergraduate • Less specialized • More concept-focused • CS students should have a serious minor (e.g., biology) • Bio students should have lots of computation (programming, data structures, algorithms, statistics, a smattering of databases and visualization)

  12. Support creation of robust software by a non-R01 process • Need for software development and algorithm development needs to be explicitly recognized in R01’s • Separate mechanism needed to fund the hardening of software tools that are of value to the community • Also may need to explicitly support algorithm and tool development (community infrastructure)

  13. Focus on tools usable by others • Figure out how to mandate reproducible research – openly publish • data • tools • papers

  14. Dangling observations • Need progress on simulation • Hierarchical / multi-level • Hybrid • Computer scientists and biologists have mismatched goals • CS people seek a general solution • Biologists want a specific application addressed

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