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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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Abstract

In order to explore new scientific and innovative communities, analyses based on a technological infrastructure and its related tools, for example, ‘Web of science’ database for Scientometric analysis, are necessary. However, there is little systematic documentation of social media data and webometric analysis in relation to Korean and broader Asian innovation communities. In this short communication, we present (1) webometric techniques to identify communication processes on the Internet, such as social media data collection and analysis using an API-based application; and (2) experimentation with new types of data visualization using NodeXL, such as social and semantic network analysis. Our research data is drawn from the social networking site, Twitter. We also examine the overlap between innovation communities in terms of their shared members, and then, (3) calculate entropy values for trilateral relationships.

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educational matters ( Marsh, Pane, & Hamilton, 2006 ). For example, DDDM-related tools are used to measure students’ diagnostic, formative, and summative knowledge in national competency tests. A similar system is the academically certified eDia ( Csapó

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1939 and 2002. Biologia , Bratislava 60 : 1 – 8 . Szűcs , Sándor 2003 A gyékény földolgozása és eszközei a Biharmegyei Sárréten [The processing of bulrushes and the related tools in the Sárrét area of Bihor (Bihar) County] . In Dankó , Imre

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