Project description
This project builds data and analytic infrastructure for the Department of Defense in its role as a science funder. The work links policy, grant text and metadata, scientific workforce data, publications, and downstream outcomes so agency leaders can identify research portfolio risks and opportunities.
The project extends Ian Hutchins’s work in scientific portfolio analysis, open citation infrastructure, and machine-learning analysis of knowledge flow. It develops computational and AI approaches for detecting overlap, strategic misalignment, and movement of research discoveries into applied goals such as health, invention, and patenting. The broader aim is to provide a model for scalable evidence-building infrastructure within science funding agencies.