what everybody knows but nobody says can hurt interdisciplinary research n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
What everybody knows but nobody says can hurt interdisciplinary research PowerPoint Presentation
Download Presentation
What everybody knows but nobody says can hurt interdisciplinary research

Loading in 2 Seconds...

  share
play fullscreen
1 / 20
arlais

What everybody knows but nobody says can hurt interdisciplinary research - PowerPoint PPT Presentation

136 Views
Download Presentation
What everybody knows but nobody says can hurt interdisciplinary research
An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  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 • 