A social network-empowered research analytics framework for project selection

Abstract

Traditional approaches for research project selection by government funding agencies mainly focus on the matching of research relevance by keywords or disciplines. Other research relevant information such as social connections (e.g., collaboration and co-authorship) and productivity (e.g., quality, quantity, and citations of published journal articles) of researchers is largely ignored. To overcome these limitations, this paper proposes a social network-empowered research analytics framework (RAF) for research project selections. Scholarmate.com, a professional research social network with easy access to research relevant information, serves as a platform to build researcher profiles from three dimensions, i.e., relevance, productivity and connectivity. Building upon profiles of both proposals and researchers, we develop a unique matching algorithm to assist decision makers (e.g. panel chairs or division managers) in optimizing the assignment of reviewers to research project proposals. The proposed framework is implemented and tested by the largest government funding agency in China to aid the grant proposal evaluation process. The new system generated significant economic benefits including great cost savings and quality improvement in the proposal evaluation process.