Abstract
Purpose: We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes: First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another. Design/methodology/approach: We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain's structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach. Findings: The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages: conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions. Research limitations: The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications. Practical implications: The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review. Originality/value: Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.
| Original language | English |
|---|---|
| Journal | Journal of Data and Information Science |
| Volume | 2 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2017 May 1 |
Bibliographical note
Funding Information:Dr. Chaomei Chen is a Professor of Informatics in the College of Computing and Informatics at Drexel University, USA. He received a B.Sc. in Mathematics (Nankai University, China), an M.Sc. in Computation (University of Oxford, UK) and a Ph.D. in Computer Science (University of Liverpool, UK). He served as a Visiting Professor at Brunel University, UK and a Chang Jiang Scholar at Dalian University of Technology, China. He served as a member of Thomson Reuter Strategic Advisory Board, the Research Portfolio Analysis Subcommittee of the CISE/SBE Advisory Committee of the National Science Foundation of the USA, a reviewer of the Chang Jiang Scholars Program of the Chinese Ministry of Education, and expert reviewers for national funding agencies of countries such as Austria, Canada, Ireland, the Netherlands as well as the USA. Dr. Chen is the founding editor and the Editor-in-Chief of the journal Information Visualization, the founding editor and the Specialty Chief Editor of Frontiers in Research Metrics and Analytics, and serves on the editorial board of Journal of Data and Information Science. His research and scholarly expertise is in the visual analytic reasoning and assessment of critical information in complex adaptive systems. His work focuses on identifying emerging trends and potentially transformative changes in the development of science and technology, especially through computational and visual analytic approaches. He is the author of The Fitness of Information: Quantitative Assessments of Critical Information (Wiley, 2014), Turning Points: The Nature of Creativity (Springer, 2011), Information Visualization: Beyond the Horizon (Springer 2004, 2006) and Mapping Scientific Frontiers: The Quest for Knowledge Visualization (Springer 2003, 2013). Dr. Chen has published over 200 peerreviewed articles in multiple disciplines, including computer science and information science. His work has been cited over 12,000 times on Google Scholar. His research has been supported by the National Science Foundation (NSF) and other government agencies as well as industrial sponsors such as Elsevier, IMS Health, Lockheed Martin, and Pfizer. His earlier research was funded by the European Commission, the Engineering and Physical Sciences Research Council (UK), and the Library and Information Commission (UK). Dr. Chen has designed and developed the widely used visual analytics software CiteSpace for visualizing and analyzing structural and temporal patterns in scientific literature.
Publisher Copyright:
© 2017 Chaomei Chen.
All Science Journal Classification (ASJC) codes
- Public Administration
- Library and Information Sciences
- Information Systems and Management