Convergence – Background & Conceptual Framework: Convergence science is an intrepid form of interdisciplinarity defined by the US National Research Council as ‘the coming together of insights and approaches from originally distinct fields’ to strategically address grand challenges (NRC, 2014). Given its increasing relevance to science policy and institutional design, here we showcase a practical framework for measuring convergence – one that operationalizes measure of disciplinary distance based upon disciplinary boundaries delineated by hierarchical ontologies. We apply this approach using two widely used ontologies— the Classification of Instructional Programs (CIP) and the Medical Subject Headings (MeSH) — which are each comprised of thousands of entities that facilitate classifying two distinct research dimensions – namely, social & conceptual. The social dimension codifies the disciplinary pedigree of individual scholars, connoting core expertise associated with traditional modes of mono-disciplinary graduate education. The conceptual dimension codifies the knowledge, methods, and equipment fundamental to a given target problem, which together may exceed the researchers’ core expertise.

Methodological Framework & Descriptive Analysis – historical convergence trends in the conceptual dimension: To achieve a high-resolution representation of the conceptual space, we leverage the knowledge network representation of the Medical Subject Heading (MeSH) ontology implemented in PubMed to infer conceptual distances between roughly 30,000 distinct MeSH keywords each being prescribed to particular knowledge domains in order to quantify the origins of cross-domain biomedical convergence. Analysis of MeSH co-occurrences at the publication-level shows how distinct conceptual domains integrated from the 1990s onward via technological and informatic capabilities captured by MeSH belonging to the “Technology, Industry, and Agriculture" (branch J) and “Information Science" (branch L) domains, which together represent highly controllable, scalable and permutable research processes and invaluable imaging techniques for illuminating fundamental yet transformative structure–function–behavior questions. Our results show that 8.2% of biomedical research from 2000 to 2018 include MeSH terms from both the J and L MeSH branches, representing a 291% increase from 1980s levels. Article-level MeSH analysis further identifies the increasing prominence of cross-domain integration, and confirms a positive relationship between team size and topical diversity.

Econometric results quantifying the citation premium associated with social-conceptual alignment: And finally, considered in tandem, this social-conceptual decomposition facilitates measuring social-conceptual alignment and optimizing team assembly around domain-spanning problems—a key aspect that eludes other approaches. To this end, econometric analysis of 655,386 publications derived from 9,121 distinct HBS scholars reveals a 11.4% article-level citation premium attributable to research featuring full topical convergence, and an additional 2.7% citation premium if the social (disciplinary) configuration of scholars is maximally aligned with the conceptual (topical) configuration of the research.