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What everybody knows but nobody says can hurt interdisciplinary research

What everybody knows but nobody says can hurt interdisciplinary research. John V. Carlis University of Minnesota. “What we have here is a lack of communication”. Messages. What everybody knows, nobody says Missed (not mis-) communication: painful surprises

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What everybody knows but nobody says can hurt interdisciplinary research

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  1. Whateverybody knowsbut nobody says can hurt interdisciplinary research John V. Carlis University of Minnesota

  2. “What we have here is a lack of communication”

  3. Messages • What everybody knows, nobody says • Missed (not mis-) communication: painful surprises • Plan for success exponential growth in data beyond human scale • Yeah! good work to do invent together

  4. Inter-Disciplinary Research • IT-ist [CS/Eng. … /Math/Stat] • Tool Builder [content neutral] • Biologist [soils/neuro/microbio/dent/biochem/ecol/vet] • Content Seeker/Maker [tool user] • Surprises & Do Inter-Alien Research

  5. Can an IT-ist become a Biologist or vice versa? • Well, life’s too short • specialization of labor • bioinformatics grad minor • CBCB in our future? Ante-Disciplinary?

  6. Business Surprise • User: NO! • IT: but I built what you told me to build • User: I gave you a typical example, but of course there are exceptions • IT: you didn’t tell me • User: you didn’t ask, and, besides, everybody knows that worse in science – but why?

  7. Science for IT is harder • Business – human decides complexity • Science -- reality >> models • Exponential growth in data • Competing models • Lots of vocabulary • Specific vs Abstract • Vocabulary sloshes  Surprises

  8. Surprise (1/5): Context • Ph is not Ph • need to remember instrument used • Annotation • Beyond genome is harder • What plus When & Where [microarray; mass spec] •  harder to share/re-use data

  9. Surprise (2/5): Casual Vocabulary • chimp • chimp + baby • chimp + offspring • chimp + offspring + close personal friend

  10. Surprise (3/5): Success Brings Pain • Prosite’s curated protein patterns + descriptions: ~2 mb of free (con)text • human browses  toooo little • success  tooooooo many • Genbank • Obsolete fields • “misc” • Parsing free text is hard & error prone

  11. Surprise (4/5): Vocabulary missing/overloaded/off • Text readable only by those who already know • Nouns – pretty good • Verbs -- Janeway’s “Immunology”: mediate, … • “Pathway” • BAD diagrams 248 e.g., “metadata”

  12. Surprise (5/5): Idiosyncratic brain viewing • Different machines,conditions & warping parameters • Fuss ‘til it looks right • a day’s work! requires scarce expertise • Doesn’t scale to comparisons among images  processing plan is data too

  13. Can IT-ist ignore performance? • IT-ist expects specifications • Short run efficiency for given specs • get it working • but cycles/space cheap/available • Change? • Plan for unplanned changes not trained/rewarded  attitude lack vision

  14. Togetherness • Communication • Anchored/Enabled/Rewarded

  15. Vocabulary Mantra:what do we mean by one of this type? • Data Model • What to remember, not how • Fine distinctions [singular/plural] • disease vs affliction • host vs pathogen • Multi, not single function, • so not partition cluster

  16. Hit Limits  DBMS Extensions • “manual” brain image manipulations new content-neutral operators • “this” is a special case of what more general task • constant  vector • multi-hull not “the” query; parachute in then explore territory

  17. Interdisciplinary Impedance Mismatch • Mundane vs interesting • Messy problem (seeking insights) vs optimal solution (irrelevant but hopeful) • Good clusters/fast algorithm/DB • not directly a Bio goal • Some professional danger but big potential reward

  18. Good Work • Expect to struggle to communicate • invent vocabulary • define verbs • Seek visionary colleagues • 

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