Creating a Data Infrastructure for Tracking Knowledge Flow

Abstract

To understand how research and development leads to creation of knowledge and then to track the impact of that knowledge requires a comprehensive model of the research ecosystem that incorporates inputs, outputs, activities, and external factors, and the data to support longitudinal and network analysis. To date, most research has focused on those activities and outputs that are readily accessible, including publication output and follow-on citations, and patents and patent citations. While these outputs are robust and can be normalized by field of research, additional data are needed. Moreover, efforts to assemble systematic information on researchers, including their biographic information, institutions, support, and networks, are in a fledgling stage. We discuss requirements around data linkages, data standards, and data privacy in creating a distributed data infrastructure to support quantitative analysis of the research workforce.