CDI-Type II: What drives the dynamic creation of science?

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This multidisciplinary project draws upon sociology, computer science, and linguistics to study how ideas are created and propagate through scientific communities, how these communities are formed and change over time, and how multidisciplinary networks spanning these communities shape scientific innovation. It creates sophisticated new computational models for extracting and representing ideas and measuring their impact and novelty, and for extracting and representing social relations and identifying forms of multidisciplinary collaboration. Its methods integrate the network analytic tools of social science with the language processing tools of computer science. It uses network analysis to improve the ability of computational tools to identify ideas in scientific texts, as well as the tools of computational linguistics to help explain the co-evolution of scientific collaborations and innovations. The models of ideas and their diffusion can be used to investigate hypotheses such as whether multidisciplinary research accelerates or decelerates scientific innovation, and how multidisciplinarity influences student and faculty careers. A relatively shallow but large-scale study of the MEDLINE corpus is combined with a richer organization-level study of Stanford University faculty (their publications, grants, affiliations, advising, and teaching) in order to explore and analyze the complex interrelationships of innovation and multidisciplinary collaboration.The research agenda of this project is to produce new and unique data, create new computational tools, and extend theory so that scholars change their conceptions of scientific innovation, multidisciplinarity, and research communities more generally. The integration of social network and natural language processing techniques is helping to develop a new vein of research in computational social science, simultaneously offering empirical rigor and scale to the sociology of science and extending natural language processing from its previous engineering focus toward true explanatory social science models.