The needs and opportunities for improved data and methods for making and analyzing science and technology policies are the principal focus of the NSF Science of Science and Innovation Policy (SciSIP) Program. SciSIP aims to foster the development of relevant knowledge, theories, data, tools, and human capital. According to the agency’s description, “the SciSIP program underwrites fundamental research that creates new explanatory models, analytic tools and datasets designed to inform the nation’s public and private sectors about the processes through which investments in science and engineering (S&E) research are transformed into social and economic outcomes. SciSIP’s goals are to understand the contexts, structures and processes of S&E research, to evaluate reliably the tangible and intangible returns from investments in R&D, and to predict the likely returns from future R&D investments within tolerable margins of error and with attention to the full spectrum of potential consequences” (National Science Foundation, 2008c).

Metrics from such sources as the federal funds and federal support surveys are essential inputs to such analysis. To further develop the vision of a new science of science policy, which was originally articulated by John H. Marburger III, the former director of the Office of Science and Technology Policy and presidential science adviser, the National Science and Technology Council Interagency Task Group developed “The Science of Science Policy: A Federal Research Roadmap” (National Science and Technology Council and Office of Science and Technology Policy, 2008). The roadmap document points out the importance of public investments in science, technology, and innovation but notes that a rationale for scientific investment decisions has insufficient theoretical and empirical bases. The roadmap calls for the development of more rigorous tools, methods, and data to help arrive at sound and cost-effective investment strategies.

The portfolio of statistics prepared by SRS is central to these science and innovation policy initiatives. However, the development of an infrastructure for the science of science and innovation policy cannot be accomplished by SRS alone. It will require contributions from academic research and from a multitude of other federal agencies and departments. The workshop summary in Appendix C describes the kind of information that users need to support an assessment of science and innovation policy. In addition to the current input indicators that are offered by the federal funding data, these needs include output indicators (e.g., publications, graduate students, citations, patents) that support “return on investment” studies and other science policy analyses. To best take advantage of the dynamic nature of these investments, the data would need to be retrievable in new ways. Ideally, it should be possible to select a bar graph, a geospa-

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