Emelia Probasco and Christian Schoeber
Introduction
Decision-makers today are pressed to stay ahead of the tsunami of new science and technology research. Many hope that big data and artificial intelligence (AI) will help identify research evolutions and revolutions in real time, or even before they happen. As we will discuss below, data alone cannot predict scientific revolutions. Examining data to stay current with, or slightly ahead of, new technologies, however, is still valuable.
This paper proposes a human-machine teaming approach to systematically identify research developments for an organization. First, our approach starts by identifying papers that the organization has authored. Second, we use those papers to find research clusters in the Center for Security and Emerging Technology (CSET) Map of Science, which displays global academic literature clustered according to citation patterns. Third, we select a subset of clusters based on metadata that we believe indicates important research activity. Fourth, we share the selected clusters with subject matter experts (SMEs) and facilitate a discussion about the research and its potential impact for the organization.
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