Accelerating Science: Risks, Incentives, and Rewards

The overarching premise of this meeting is that the scientific process has not learned valuable lessons from scientific practices and methods. While scientific research has embraced experiment, empirical observation, non-parametric statistics, computational modeling, distributed collaboration, and theory, the institutions of science have proved to be rather recalcitrant and conservative. 

Departments, disciplines, peer-review; journals, grants/patronage, and review-panels, would all strike Newton, Darwin, and Curie as near adjacent to their own experiences. Whereas the web and internet, black holes, evolutionary genetics, and classical and quantum computing would seem fantastically futuristic. 

This meeting explores the implications of turning the models and products of science back on the process of science. This has been underexplored by virtue of limited data and few models. We believe that by drawing from new forms of data and theory we can enable alternative institutional and career models, thereby accelerating scientific progress.