Members in the Media
From: The New York Times

Can Big Science Be Too Big?

Modern science is largely a team sport, and over the past few decades the makeup of those teams has shifted, from small groups of collaborators to ever larger consortiums, with rosters far longer than that of the New England Patriots. Answering big questions often requires scientists and institutions to pool resources and data, whether the research involves detecting gravitational waves in deep space, or sorting out the genetics of brain development.

But that shift has prompted scientists to examine the relative merits of small groups versus large ones. Is supersizing research projects the most efficient way to advance knowledge? What is gained and what, if anything, is lost?

Psychologists have found that people working in larger groups tend to generate fewer ideas than when they work in smaller groups, or when working alone, and become less receptive to ideas from outside. Why that would be isn’t entirely clear, but it runs counter to intuition, said Suparna Rajaram, a professor of psychology at Stony Brook University.

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What is unfortunate is the tendency in academia (and I include funders in the mix) is the never-tiring effort to take something complicated and make it simple. Identifying important and interesting problems and assembling the approaches needed to attempt to gather knowledge is what matters. We have seen this in the past with the mindless rush to champion interdisciplinarity as a catch-all solution. Right now it is “team science.” Melding disciplines may indeed be what some problems require – and how it is achieved will look very different in a variety of contexts. Be it small teams, big teams, lab teams, multi-institutional teams – assembling teams only makes sense in the context of what is being attempted. Lets stop the buzz-wording and center how we pursue important, meaningful work by the important meaningful research we want to undertake.

You can have your cake and eat it too. When we assembled the cooperative for the first major Computerized Adaptive Testing system since CAT ASVAB, our NIH team sought to have a group of groups. Consensus was built early on regarding general approach and psychometrics, then every team went back to their labs and built NIH PROMIS []. As better mousetraps were discovered, they were shared among the teams.

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