DoD grant: Identifying Risks and Opportunities in the Department of Defense Research Portfolio
Project Description
United States military superiority relies on harnessing the extensive technological advantages that are generated by our thriving innovation economy, stimulated by extensive federal investment. Effectively managing the Department of Defense (DoD) research portfolio is one way of ensuring that the United States maintains its technological advantage while achieving specific applied research goals. There is broad support that evidence-based policymaking improves organizational management. Effectively promoting the use of evidence in policymaking has been a challenge for decades. One challenge is that policymakers struggle to find policy research that is high-quality, relevant and timely.
To advance the frontier of evidence-based policymaking, some federal science funding agencies over the last decade have developed an evidence-building approach called scientific portfolio analysis to promote data-driven decision-making. This is the quantitative analysis of linked data describing policy, grant text and metadata, the scientific workforce, their publications, and the flow of knowledge into basic or applied outcomes using citation analysis. It applies scientific approaches to study the scientific enterprise. This methodology introduces timely, relevant, and trusted evidence for consideration by agency leaders and enables funding agencies to characterize the scientific portfolio from both a global and local perspective. Linked data are a prerequisite for this kind of analysis. Unfortunately, at present, DoD does not have a centralized repository with this kind of information.
Our team, including Dr. Hutchins and The ARI, have helped build scientific portfolio analysis capacity at other agencies, and propose to adapt and extend these approaches to the defense environment. In this project, in this project we will address these gaps in three Aims. First, we will construct an end-to-end database of linked DoD data from research funding to applied outcomes. This work will be guided by structured interactions with Defense Program staff to identify key barriers and opportunities for incorporating data into policymaking. Second, we will use these data to identify risks and opportunities in the research portfolio, such as potentially problematic overlap, and measures of (mis)alignment with strategic priorities. Third, using machine learning and analysis of knowledge flow, we will quantify how resultant discoveries feed into downstream applied research goals, such as research into improvements in human health, or downstream invention and patenting activities.
This work is anticipated to lead to three outcomes that can impact Defense capabilities. First, this will yield novel computational and artificial intelligence approaches for identifying research portfolio risks and opportunities. Second, this will provide fundamental insights into the process of knowledge transfer from research discoveries into applied goals like clinical and technological impact. Third, the broad approaches for integrating data into a centralized database and conducting global portfolio analysis can be used by the DoD as a template for scaling from a research project into an in-house centralized information infrastructure and analytics platform.