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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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the two tools. The first tool was the Network WorkBench (Pullen 2000 ), which is considered a strong complex network analysis tool and was primarily used for global network analysis. The second was CiteSpace (Chen 2006 ), a recent tool which has been

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have no GCC value, if they haven't appeared as Web of Science records, in which case GCC of these references is set to −1 by Network Workbench analysis tool. Data collection and preparation Today, existence of well

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All data were pre-processed using the Network Workbench Tool or NWB (Börner 2009 ), freely available from http://nwb.slis.indiana.edu . Several steps have been taken to ensure a high-quality data set. First, duplicate records were removed, such that

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and Lifang Mu for the English revision of the manuscript. This can be achieved in Network Workbench. We can get a comparatively stable result via Fruchterman–Reingold algorithm and optimize with

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