Rete-netzwerk-red: analyzing and visualizing scholarly networks using the Network Workbench Tool
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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.
KeywordsScientometrics Science of science Evolution of science Computational scientometrics Data access Algorithm scalability Cyberinfrastructure Scholarly Database Network Workbench Related tools Open source Open access
We would like to acknowledge the contributions and support by the NWB team and advisory board. This work is funded by the School of Library and Information Science and the Cyberinfrastructure for Network Science Center at Indiana University, the James S. McDonnell Foundation, and the National Science Foundation under Grants No. IIS-0715303, IIS-0534909, and IIS-0513650. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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