This project develops a set of tools that allow organizations investing in Science and Engineering to identify and predict the emergence of innovative research. Such a capacity would permit organizations to efficiently allocate resources to stimulate rapid and effective research process in these areas. Several key attributes are needed: the tool should be able to operate in real-time, be representative of the widest possible sample of scientific activity, and support a cost-benefit analysis of allocated resources.
Intellectual merit: This research aims to support the development of such tools by focusing on two scientific and methodological issues. First, the project studies the potential of early indicators of scientific activity such as usage data and search query logs. Second, the project aims to develop models that can, on the basis of such early indicators, identify and predict emerging trends in real-time.
The project leverages the efforts of two well-established projects, namely the MESUR project (www.mesur.org) and the Eigenfactor project (www.eigenfactor.org). The MESUR project has, over the course of the past 2 years, captured a significant sample of the world?s scientific activity, via a collection of more than 1 billion article-level usage events acquired from some of the world's most significant publishers, aggregators and university consortia. The Eigenfactor project has demonstrated the power of mathematical network models (cf. Google's PageRank) to rank disciplines and journals according to the lattice work of scientific citations that records the collective history of S&E research. Predictions of the "flow" of scientific activity have been used to produce detailed maps of scientific activity that may identify potential foci of scientific innovation.
This project expands the Eigenfactor models to include MESUR's indicators of actual, real-time scientific activity. On that basis the project develops a set of early indicators that can detect the emergence of scientific innovation in real-time - before such trends are visible in citation data - and relates these indicators to public policy and decision making. The project also develops explanatory and predictive frameworks that connect observations of individual behavior with emergent, collective phenomena such as scientific innovation. Since the focus of the research is whether it is possible to develop analytic and predictive tools that indicate why, how and where scientific innovation is most likely to occur, the existing eigenfactor.org services will be leveraged to produce freely available, expandable tools that rank, analyze, predict and chart areas of scientific innovation.
Broader Impact: this research project produces freely available, expandable services to form an "early warning" system for scientific innovation that are expected to lead to a better public understanding of science as a complex, dynamic system. Such services should foster public participation in efforts to establish a more diverse, innovative research landscape that can meet the challenges of the 21st century.
