What everybody knows but nobody says can hurt interdisciplinary research

What everybody knows but nobody says

can hurt interdisciplinary research

John V. CarlisUniversity 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 & DoInter-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