Science relies on funding. The models that have controlled scientific research funding for the last half century have brought us where we are today. But do they also contain the seeds of their own destruction? Despite the obvious benefits of rigorous peer review in funding cycles, excessively competitive review processes can encourage counter-productive behaviour by forcing scientists to compete rather than collaborate, to restrict rather than share access to data, and to make it difficult for unconventional approaches or new researchers to gain a foothold in established disciplines.
The last decade has seen the rise of new models of collaboration. Force11 and various other Open initiatives have shown that groups with little money but strong community buy-in can accomplish a lot in a short time. Are there lessons to be learned from these grassroots initiatives, which can then inform the larger world of funding? Can we "improve" the current funding system to ensure incentives better align with best practice – and discourage poor or counterproductive practice? What is the best way of supporting networked, collaborative, and community-driven research? How can we best ensure reproducibility of results and interchange of data? What role can new funding models play in these data-centric infrastructures?
Session Chairs: Anita DeWaard and Dan O'Donnell
Liz Allen, Wellcome Trust
Phil Bourne, U.S. National Institutes of Health (NIH)
Amy Friedlander, U.S. National Science Foundation (NSF)
Josh Greenberg, Alfred P. Sloan Foundation
Chris Mentzel, Gordon and Betty Moore Foundation
Katherine Skinner, Educopia