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Abstract

The Science of Science and Innovation Policy (SciSIP) program at the National Science Foundation (NSF) supports research designed to advance the scientific basis of science and innovation policy. The program was established at NSF in 2005 in response to a call from Dr. John Marburger III, then science advisor to the U.S. President, for a “science” of science policy. As of January 2011, it has co-funded 162 awards that aim to develop, improve, and expand data, analytical tools, and models that can be directly applied in the science policy decision making process. The long-term goals of the SciSIP program are to provide a scientifically rigorous and quantitative basis for science policy and to establish an international community of practice. The program has an active listserv that, as of January 2011, has almost 700 members from academia, government, and industry. This study analyzed all SciSIP awards (through January 2011) to identify existing collaboration networks and co-funding relations between SciSIP and other areas of science. In addition, listserv data was downloaded and analyzed to derive complementary discourse information. Key results include evidence of rich diversity in communication and funding networks and effective strategies for interlinking researcher and science policy makers, prompting discussion, and resource sharing.

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Scientometrics
Authors: Katy Börner, Weixia Huang, Micah Linnemeier, Russell Duhon, Patrick Phillips, Nianli Ma, Angela Zoss, Hanning Guo, and Mark Price

Abstract  

The enormous increase in digital scholarly data and computing power combined with recent advances in text mining, linguistics, network science, and scientometrics make it possible to scientifically study the structure and evolution of science on a large scale. This paper discusses the challenges of this ‘BIG science of science’—also called ‘computational scientometrics’ research—in terms of data access, algorithm scalability, repeatability, as well as result communication and interpretation. It then introduces two infrastructures: (1) the Scholarly Database (SDB) (http://sdb.slis.indiana.edu), which provides free online access to 22 million scholarly records—papers, patents, and funding awards which can be cross-searched and downloaded as dumps, and (2) Scientometrics-relevant plug-ins of the open-source Network Workbench (NWB) Tool (http://nwb.slis.indiana.edu). The utility of these infrastructures is then exemplarily demonstrated in three studies: a comparison of the funding portfolios and co-investigator networks of different universities, an examination of paper-citation and co-author networks of major network science researchers, and an analysis of topic bursts in streams of text. The article concludes with a discussion of related work that aims to provide practically useful and theoretically grounded cyberinfrastructure in support of computational scientometrics research, education and practice.

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