Advances in science come from the collective and linked efforts of thousands of researchers working within and across disciplines. This project creates rigorous models of the composition, dynamics, and network structure of the United States? scientific workforce across heterogeneous independent institutions with different strengths and emphases. The systematic influence on these institutional and individual characteristics on scientific advances across disciplines is investigated. The results of this project will generate new insights into the composition of the scientific workforce and scientific productivity across fields. In addition, this project trains new graduate and undergraduate students in cutting-edge computational and statistical research techniques, and will develop and disseminate new large-scale open data sets on the composition of the United States? scientific workforce and provide new software for collecting structured data automatically from open unstructured sources. This project uses state-of-the-art computational and statistical techniques from network science, machine learning, and social modeling to create a new technology platform for automatically and systematically collecting high-quality structured data on the composition, dynamics, and output of the scientific workforce. These data will be combined with social survey results of individual researchers and with rigorous network methods to model the relationship between workforce composition, productivity, and observable differences at the individual and institutional levels within and between scientific fields. Mathematical models of the short- and long-term evolution of workforce in order to evaluate the likely outcomes of certain types of interventions and policies are developed.
SBE Office of Multidisciplinary Activities (SMA)