A bibliometric analysis was performed in this work to evaluate the research publications on Central Asia from 1990 to 2014 based on Science Citation Index and Social Science Citation Index databases. This study presented a comprehensive overview of the field from the aspects of major journals, subject categories, outstanding keywords, leading countries, institutions and authors, as well as the research collaborations. It was identified that a total of 11,025 papers were published in 2356 journals and there had been a steady development over the past 25 years for Central Asia research. Geosciences Multidisciplinary, Geochemistry and Geophysics, Paleontology, Environmental Sciences and Zoology were the most popular subject categories. Keywords analysis indicated that “Tien Shan”, “climate change”, “taxonomy”, “new taxa”, and “health care” were the topics that generated the most interest and concern. Besides, temporal evolution of keywords revealed the rapid growth of “Central Asia Orogenic Belt” and “Zircon UPb dating”. According to research forces analysis, USA and Russian Academy of Sciences came as the leading contributors and had the dominant position in collaboration networks. This paper was a new attempt to better the understanding of the progress in Central Asia research. The findings of this study would help researchers improve the performance.

http://link.springer.com/article/10.1007/s11192-015-1727-y

Author(s): Lixian Wang, Xi Chen, Anming Bao, Xiaoyun Zhang, Miao Wu, Yun Hao, and Jingjing He
Organization: Chinese Academy of Sciences
Source: Scientometrics
Year: 2015

An increasing number of quantitative and qualitative methods have been used for future-oriented technology analysis (FTA) to develop understanding of situations, enable creativity, engage experts, and provide interaction. FTA practitioners have used frequently one or a suitable mixture of these methods for their activities. Changing policy and strategy making contexts as well as enabling technologies increased the need and possibility for performing adaptive Foresight studies in order to improve decision making about the future and using making better use of limited resources. This study performs a scientometric analysis of the publications in the major FTA journals with the aim of understanding the dynamics of using Foresight methods across time. Among the other branches of FTA, including forecasting, futures, and technology assessment, a special emphasis is given on Foresight as a systematic and inclusive way of exploring long term futures, developing visions and formulating policies for action. The study aims at detecting the key Trends and Weak Signals regarding the use of existing methods and emerging ones with potential uses for Foresight activities. Further implications will be achieved with the generation of networks for quantitative and qualitative methods. This will demonstrate the most frequently combined Foresight methods by researchers and practitioners. Where possible the methods will also be cross-fertilised with the key thematic areas to illustrate the relationships between policy domains and industrial sectors covered by the scope of study with methodological choice. This output is considered to be taken as a methodological guide for any researchers, practitioners or policy makers, who might embark upon or involved in a Foresight activity. Further outputs of the study will include the identification of centres of excellence in the use of Foresight methods and collaboration networks between countries, institutions and policy domains. Overall, the paper demonstrates how scientometric tools can be used to understand the dynamics of evolution in a research field. Thus, it provides an overview of the use of methods in Foresight, and how it is distinguished from the other FTA activities; the evolutionary characteristics of methodological design and factors influencing the choice of methods; and finally a discussion on the future potentials for new cutting-edge approaches.

Author(s): Ozcan Saritas, Serhat Burmaoglu
Organization(s): Higher School of Economics (HSE)
Source:  Scientometrics http://link.springer.com/article/10.1007/s11192-015-1671-x
Year: 2015