Mainen et al in Nature:
At least half a dozen major initiatives to study the mammalian brain have sprung up across the world in the past five years. This wave of national and international projects has arisen in part from the realization that deciphering the principles of brain function will require collaboration on a grand scale. Yet it is unclear whether any of these mega-projects, which include scientists from many subdisciplines, will be effective. Researchers with complementary skill sets often team up on grant proposals. But once funds are awarded, the labs involved often return to work on their parts of the project in relative isolation. We propose an alternative strategy: grass-roots collaborations involving researchers who may be distributed around the globe, but who are already working on the same problems. Such self-motivated groups could start small and expand gradually over time. But they would essentially be built from the ground up, with those involved encouraged to follow their own shared interests rather than responding to the strictures of funding sources or external directives.
…We propose that researchers join forces in 'meso-scale' collaborations of around 20 principal investigators and between 50 and 100 researchers to conduct experiments that are beyond the reach of single labs. Even at this scale, there will be many hurdles to clear. Specifically, an effective collaboration would need to do the following.
Focus on a single brain function. The downfall of many neuroscience collaborations — and especially of mega-projects — is setting goals that are too broad. The common goal has to be ambitious, yet reachable within, say, ten years, and well defined. A whole-brain theory of one brain function — a single behaviour — could meet those requirements. If a collaboration were largely limited to labs interested in the same behaviour — such as courtship in fruit flies, or foraging in mice — clear, shared objectives could be defined at the start. The labs would apply a range of recording and manipulation techniques to the same common behavioural task, allowing the functional data to be seamlessly combined.