what everybody knows but nobody says can hurt interdisciplinary research john v. carlis university...

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 Plan for success

exponential growth in data beyond human scale

Yeah! good work to do invent together

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

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?

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?

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

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

Surprise (2/5): Casual Vocabulary

chimp chimp + baby chimp + offspring chimp + offspring

+ close personal friend

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

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”

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

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 attitudelack vision

Togetherness

Communication

Anchored/Enabled/Rewarded

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

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

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

Good Work Expect to struggle to communicate

invent vocabulary define verbs

Seek visionary colleagues