Authors:Katy Börner, Weixia Huang, Micah Linnemeier, Russell Duhon, Patrick Phillips, Nianli Ma, Angela Zoss, Hanning Guo, and Mark Price
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.
Authors:Kevin Boyack, Katy Börner, and Richard Klavans
How does our collective scholarly knowledge grow over time? What major areas of science exist and how are they interlinked?
Which areas are major knowledge producers; which ones are consumers? Computational scientometrics — the application of bibliometric/scientometric
methods to large-scale scholarly datasets — and the communication of results via maps of science might help us answer these
questions. This paper represents the results of a prototype study that aims to map the structure and evolution of chemistry
research over a 30 year time frame. Information from the combined Science (SCIE) and Social Science (SSCI) Citations Indexes
from 2002 was used to generate a disciplinary map of 7,227 journals and 671 journal clusters. Clusters relevant to study the
structure and evolution of chemistry were identified using JCR categories and were further clustered into 14 disciplines.
The changing scientific composition of these 14 disciplines and their knowledge exchange via citation linkages was computed.
Major changes on the dominance, influence, and role of Chemistry, Biology, Biochemistry, and Bioengineering over these 30
years are discussed. The paper concludes with suggestions for future work.