Collaborative Research: RUI: HNDS-R: Stepping out of flatland: Complex networks, topological data analysis, and the progress of science

Scientific progress is measured by the number of new discoveries that scientists make. As science advances, the amount of prior knowledge that researchers must master to push the frontiers of science forward has increased dramatically. Consequently, scientific breakthroughs are harder to achieve. This project uses innovative mathematical tools to study how the changing scale and scope of knowledge and the social organization of science might be slowing the pace of scientific discovery. Being able to identify what encourages (and, conversely, stifles) scientific advances is important for making good decisions about future science policy, determining funding priorities, and figuring out how scientific work can best be organized. Because the increasing “burden of knowledge” required for scientific progress has made it more difficult for individual scientists to make new discoveries, the creation of larger and more diverse research teams of experts devoted to solving complex scientific problems has become common. This “divide and conquer” interdisciplinary approach has been very effective, but there is still much to learn about how knowledge growth slows discovery rates and what the ameliorating or exacerbating effects of changes in the social organization of science are. This project builds on the NSF-supported ExHACT project by applying mathematical tools drawn from topological data analysis to analyze large scale bibliometric data and data on grant payments from the UMETRICS project. These tools permit the examination of the topological structure and dynamics of concept networks that capture "what science knows," "what it wants to know," and "what scientists currently have the propensity to know." The project will help identify the factors that affect the rate and character of new discoveries, whether the discovery rate is affected by changes in research funding or the organization of scientific work, and how these effects vary across time and fields of inquiry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.