Measuring and Tracking Research Knowledge Integration and Transfer

Project Investigator: 
Alan Porter
National Science Foundation, Science of Science and Innovation Policy Program
Project Duration: 
September 2008

Enhanced Science of Science and Innovation Policy depends on better metrics. You can’t manage what you can’t measure. This is particularly true for interdisciplinary research, which currently has few generally agreed-upon measures with acceptable degrees of accuracy. Researchers at the University of Sussex have begun to address this problem by developing a conceptual framework to gauge research diversity. The current project builds upon that conceptual framework to empirically test metrics that gauge the interdisciplinarity of particular bodies of research. One proposed key measure assesses the degree to which particular research papers, or collections of such, integrate research knowledge from diverse research domains. A second measure determines the degree of specialization of collections of research papers (e.g., those published by a particular research center or those of a research area such as quantum dots). The resulting measures help track and characterize the emergence of new (interdisciplinary) research areas.

The project described in this proposal seeks to generate analytical algorithms for indicators of interdisciplinarity. It also seeks to visually depict knowledge interchanges among areas of research activity. Such science maps can help identify and characterize focused areas of research--domains--that are sources of knowledge used by other domains. They can also show the extent of intellectual and social networking among both domains and contributing institutions. This project will, through US-UK collaboration, develop effective means to apply and test these new metrics.

The proposed project focuses on nanoscience and nanoengineering (“nano”), an emergent research domain of considerable significance that extends well beyond traditional disciplinary boundaries. Georgia Tech has assembled a substantial nano dataset that will serve as the main testbed for computing and assessing indicator variations. The research team will generate indicator sets and maps of selected nano sub-topics (e.g., molecular motors research). These indicator sets and maps will be shared with researchers and R&D managers to gauge their validity and utility. Taking into account feedback, the research team will then develop a taxonomy of nanotechnology research activity based on identification of coherent research sub-areas.

These tools better enable scientists, science managers, and Federal science and regulatory agencies to gauge and track cross-domain knowledge transfers. Failure to recognize the full extent and complexity of these patterns could result in major funding and regulatory mistakes. The new indicators and accompanying maps help identify leverage points likely to spark advances in science, technology, and innovation. They can also facilitate graduate education by identifying convergent knowledge domains – potentially emerging “interdisciplines.” Better understanding of research landscapes can help orient graduate curricula and spotlight promising dissertation topics. More accurate interdisciplinarity measures also contribute to an ongoing National Academies initiative to bolster interdisciplinary research across the US.