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.

We describe each of these steps in detail in the sections that follow and use a proof-ofconcept experiment to evaluate our approach.

This paper is intended for individuals developing research or investment portfolios and priorities within their organizations. It should also be useful to SMEs interested in exploring or revealing research to which they may not otherwise be exposed as a consequence of increasing specialization.