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Scientific databases, tacit knowledge, and the limits of federation

I was at the first day of SciBarCamp today, playing local host / fixer / keeping an eye on the furniture. Sean Mooney (a former professor at Indiana University, now relocated to the West Coast) gave a very interesting talk about current challenges in bioinformatics.

A fair amount of Sean's talk dealt with the technical challenges of creating federated databases, the differing demands of bench scientists and funders-- the former want tools for managing and analyzing data in today's problems, while the latter want to attack Big Questions-- and the issues involved in getting people to share their data. The issues aren't so much philosophical or competitive, but practical: people believe in sharing data, and once they're done with it are generally willing to share so long as it doesn't put a burden on them.

But as Sean was talking about how different labs used different procedures for similar experiments, and how those differences manifested themselves in the ways they produced and consumed data (at least, this is what I took away from his talk-- he might have meant something complete different), a thought came to me. Projects intended to let scientists assume that data can be converted into something like the reagents or instruments labs buy from suppliers-- a commodity that you don't have to think about, you just use. But what if data can't be black-boxed this way?

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