Abstract
Systems biology is a new field of biology that has great implications for agriculture, medicine, and sustainability. In this article we explore the contributions of Chinese authors to systems biology through analysis of the metadata of more than 9000 articles on systems biology. Our big-data approach includes scientometric analysis, GIS analysis, co-word network analysis, and comparative analysis. By 2013 China has become second in the number of publications on systems biology. Similar to previous studies on Chinese science, we find an unequal distribution of research power in China, favoring big cities and coastal cities. Overall, 75% of the articles in systems biology were published by scholars from universities, 15% by scholars from the Chinese of Academy of Sciences institutions, and 9% from other institutions. Many Chinese scholars’ research topics are similar to those in the US, Japan, and Germany, but one salient difference is that traditional Chinese medicine is an important topic among Chinese systems biologists. 25% of Chinese systems biologists cooperate with scientists abroad, suggesting that they take advantage of the opening-up policy. From the year 2011–2013, the average impact factor of the journals that Chinese scholars publish in is generally lower than that of their counterparts in the US, but the trend points to a gradual increase in impact.
Similar content being viewed by others
Notes
For the rankings of more countries, see http://www.scimagojr.com/countryrank.php.
For more information about the personnel and finances of the Max Planck Institute, see https://www.mpg.de/facts-and-figures.
For more information about this tool, see http://www.affymetrix.com/catalog/131492/AFFY/Rat+Genome+230+2.0+Array#1_1.
References
Barrett, C. L., Kim, T. Y., Kim, H. U., Palsson, B. Ø., & Lee, S. Y. (2006). Systems biology as a foundation for genome-scale synthetic biology. Current Opinion in Biotechnology, 17(5), 488–492.
Callebaut, W. (2012). Scientific perspectivism: A philosopher of science’s response to the challenge of big data biology. Studies in History and Philosophy of Biological and Biomedical Sciences, 43(1), 69–80.
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.
Chinese Academy of Sciences. (2014). Annual budget of Chinese academy of sciences. Available at http://www.cas.cn/xxgkml/zgkxyyb/czjf/ysjs/201407/P020140718685723194826.pdf. (Accessed Dec 1, 2016).
Church, G. M. (2005). From systems biology to synthetic biology. Molecular Systems Biology, 1(1):E1–E2.
Dahlman, C. J., & Aubert, J. E. (2001). China and the knowledge economy: Seizing the 21st century. Washington, DC: World Bank Publications.
Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: Strengths and weaknesses. The FASEB Biology Journal, 22(2), 338–342.
Garfield, E. (1955). Citation indexes for science. Science, 122(3159), 108–111.
Garfield, E. (2006). The history and meaning of the journal impact factor. JAMA, 295(1), 90–93.
Goldman, A. W. (2014). Conceptualizing the interdisciplinary diffusion and evolution of emerging fields: The case of systems biology. Journal of Informetrics, 8(1), 43–58.
Google. (2016). Google fusion table web page. Available online: https://fusiontables.google.com/data?dsrcid=implicit. (Accessed Dec 1, 2016).
He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends, 48(1), 133–159.
He, T., Zhang, J., & Teng, L. (2005). Basic research in biochemistry and molecular biology in China: A bibliometric analysis. Scientometrics, 62(2), 249–259.
Hood, L. (2003). Systems biology: Integrating technology, biology, and computation. Mechanisms of Ageing and Development, 124(1), 9–16.
Hood, L., Heath, J. R., Phelps, M. E., & Lin, B. (2004). Systems biology and new technologies enable predictive and preventative medicine. Science, 306(5696), 640–643.
Hu, X., & Rousseau, R. (2015). From a word to a world: The current situation in the interdisciplinary field of synthetic biology. PeerJ, 3, e728.
Hu, T., & Sun, W. (2013). Tuberculosis in China. Journal of Tuberculosis Research, 1(02), 9.
Huang, J., Rozelle, S., Pray, C., & Wang, Q. (2002). Plant biotechnology in China. Science, 295(5555), 674–676.
International Human Genome Sequencing Consortium. (2001). Initial sequencing and analysis of the human genome. Nature, 409(6822), 860–921.
Lao, Y. M., Jiang, J. G., & Yan, L. (2009). Application of metabonomic analytical techniques in the modernization and toxicology research of traditional Chinese medicine. British Journal of Pharmacology, 157(7), 1128–1141.
Levine, J. A. (2011). Poverty and obesity in the US. Diabetes, 60(11), 2667–2668.
Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the American Society for Information Science and Technology, 58(9), 1303–1319.
Liu, X., & Zhi, T. (2010). China is catching up in science and innovation: The experience of the Chinese Academy of Sciences. Science and Public Policy, 37(5), 331–342.
Ministry of Science and Technology of the People’s Republic of China (MSTPRC). (2014). National science and technology funding statistics bulletin. http://www.most.gov.cn/tztg/201410/t20141030_116370.htm.
Nature Publishing Group. (2015). Turning point: Chinese science in transition. London: Nature Publishing Group.
Qiu, J. (2007). China plans to modernize traditional medicine. Nature, 446(7136), 590–591.
Russ-Eft, D. (2008). SSCI, ISI, JCR, JIF, IF, and journal quality. Human Resource Development Quarterly, 19(3), 185–189.
Saha, S., Saint, S., & Christakis, D. (2003). Impact factor: A valid measure of journal quality? Journal of the Medical Library Association, 91, 42–46.
Seglen, P. O. (1997). Why the impact factor of journals should not be used for evaluating research. BMJ British Medical Journal, 314(7079), 498.
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504.
State Council of China. (2006). National guidelines on the planning of midterm and long term development of science and technology (2006 to 2020). http://www.gov.cn/gongbao/content/2006/content_240244.htm.
Su, H. N., & Lee, P. C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight. Scientometrics, 85(1), 65–79.
The Royal Society. (2011). Knowledge, networks and nations: Global scientific collaboration in the 21st century. London: The Royal Society.
Wang, L. (2016). The structure and comparative advantages of China’s scientific research: Quantitative and qualitative perspectives. Scientometrics, 106(1), 435–452.
Wang, Y., Wu, Y., Pan, Y., Ma, Z., & Rousseau, R. (2005). Scientific collaboration in China as reflected in co-authorship. Scientometrics, 62(2), 183–198.
Wu, J., Xiao, J., Zhang, R., & Yu, J. (2011). DNA sequencing leads to genomics progress in China. Science China Life Sciences, 54(3), 290–292.
Xue, L. (2008). China: The prizes and pitfalls of progress. Nature, 454(7203), 398–401.
Young, D., Stark, J., & Kirschner, D. (2008). Systems biology of persistent infection: Tuberculosis as a case study. Nature Review Microbiology, 6(7), 520–528.
Zhang, A., Sun, H., Wang, P., Han, Y., & Wang, X. (2012). Future perspectives of personalized medicine in traditional Chinese medicine: A systems biology approach. Complementary Therapies in Medicine, 20(1), 93–99.
Zhi, Q., & Meng, T. (2015). Funding allocation, inequality, and scientific research output: An empirical study based on the life science sector of Natural Science Foundation of China. Scientometrics, 106(2), 1–26.
Zhou, P., & Glänzel, W. (2010). In-depth analysis on China’s international cooperation in science. Scientometrics, 82(3), 597–612.
Zhou, P., & Leydesdorff, L. (2006). The emergence of China as a leading nation in science. Research Policy, 35(1), 83–104.
Acknowledgements
The authors would like to thank many people who have contributed to this project, including Jane Maienschein, Erick Peirson, Julia Damerow, Kenneth D. Aiello, and Deryc Painter, and anonymous reviewers for their suggestions. The authors also would like to acknowledge financial support from National Science Foundation (SES 1243575), China Scholarship Council (2011635028), and the ASU-SFI Center for Biosocial Complex Systems.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zou, Y., Laubichler, M.D. Measuring the contributions of Chinese scholars to the research field of systems biology from 2005 to 2013. Scientometrics 110, 1615–1631 (2017). https://doi.org/10.1007/s11192-016-2213-x
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-016-2213-x