收稿日期: 2015-06-01
修回日期: 2015-06-18
网络出版日期: 2015-07-05
基金资助
本文系中国科学院知识创新工程重要方向项目"工业生物技术知识服务研究与应用"(项目编号:KSCX2-KW-G-9)和国家高技术研究发展计划(863计划)"微生物数字资源知识管理系统构建及关键技术研究"(项目编号:2014AA021503)研究成果之一。
Research on Construction and Application of CCS Index
Received date: 2015-06-01
Revised date: 2015-06-18
Online published: 2015-07-05
[目的/意义] 构建一个基于科学家合作行为来评价科学家科研表现的复合合作强度(CCS)指数,该指数的特点在于不仅反映作者合作网络的特征,而且揭示科学家的合作机构及分布特征。[方法/过程] 基于科学家的合作与科研表现的正相关性,提出六大假设构建CCS指数理论模型。选取来自中国科学院2007-2011年间发表SCI论文在30篇以上的40位工业生物技术领域科学家为实证分析对象,并对实证结果与合作网络度数、H指数和篇均被引频次进行比较。[结果/结论] CCS指数与合作网络度数具有较高的相关性,而与H指数和篇均被引频次的相关性很弱,CCS指数的价值在于揭示科学家的合作者及合作机构的分布广度、深度和密度,反映科学家的国际合作参与度。这些指标之间存在互补关系,可进一步丰富科学家评价的指标,有助于开展科学家的科研表现评价和专家发现工作。
陈云伟 , 邓勇 , 陈方 , 丁陈君 , 郑颖 , 刘春江 , 方曙 . 复合合作强度指数构建及应用研究[J]. 图书情报工作, 2015 , 59(13) : 96 -103 . DOI: 10.13266/j.issn.0252-3116.2015.13.014
[Purpose/significance] This paper constructs the CCS index for evaluating scientists' performance based on their cooperation behaviors, which is a comprehensive index reflecting not only the features of co-authors network, but also the ones of the co-authors affiliations and distributions.[Method/process] Based on the positive correlation between scientists' collaboration and performances, a CCS index theoretical model is constructed following six hypotheses. 40 authors from the Chinese Academy of Sciences in the field of industrial biotechnology who has at least 30 publications during the period of 2007-2011 are used for case study. It makes a comparison of empirical results and co-author network degrees, H index, times cited per paper.[Result/conclusion] This paper finds that while CCS index has high positive correlation to co-author network degrees, it has weak correlation to H index and times cited per paper. Therefore, CCS index can uncover the distribution breadth, depth and density of scientists' cooperators and cooperation agencies, and respond the international cooperation participation of scientists. These indexes could be mutually complementary when used for experts finding and scientist evaluation, and can further enrich the scientist evaluation index.
Key words： CCS; collaboration; co-author; performance
[1] Guimerà R, Uzzi B, Spira J, et al. Team assembly mechanisms determine collaboration network structure and team performance[J]. Science,2005, 308(5722):697-702.
[2] Whitfield J. Collaboration: Group theory[J]. Nature,2008, 455(7214):720-723.
[3] Wuchty S, Jones B F, Uzzi B. The increasing dominance of teams in production of knowledge[J]. Science,2007, 316(5827):1036-1039.
[4] Panzarasa P, Opsahl T. Patterns of scientific collaboration in business and management: The effects of network structure and interdisciplinarity on research performance[EB/OL]. [2014-03-08]. http://www.ifr.ac.uk/netsci08/Download/CT_Abstracts/CT314.pdf.
[5] Abramo G, D'angelo C A, Solazzi M. The relationship between scientists' research performance and the degree of internationalization of their research[J]. Scientometrics, 2011, 86(3):629-643.
[6] Kato M, Ando A. The relationship between research performance and international collaboration in chemistry[J]. Scientometrics, 2013, 97(3):535-553.
[7] Palla G, Barabási A L, Vicsek T. Quantifying social group evolution[J].Nature,2007, 446(7136):664-667.
[8] Lin Lili, Xu Zhuoming, Ding Ying, et al. Finding topic-level experts in scholarly networks[J]. Scientometrics, 2013, 97(3):797-819.
[9] Melin G, Persson O.Studying research collaboration using co-authorships[J]. Scientometrics,1996, 36(3):363-377.
[10] Otte E, Rousseau R. Social network analysis: A powerful strategy, also for the information sciences[J]. Journal of Information Science,2002, 28(6): 441-453.
[11] Borner K, Dall'Asta L, Ke Weimao, et al. Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams[J]. Complexity: Special Issue on Understanding Complex Systems,2005, 10(4): 57-67.
[12] Abbasi A, AltmannJ, Hossain L. Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures[J]. Journal of Informetrics,2011, 5(4):594-607.
[13] McCarty C, Jawitz J, Hopkins A, et al. Predicting author h-index using characteristics of the co-author network[J]. Scientometrics, 2013, 96(2):467-483.
[14] Chen Yunwei, Borner K, Fang Shu.Evolving collaboration networks in Scientometrics in 1978-2010: A micro-macro analysis[J]. Scientometrics,2013, 95(3):1051-1070.
[15] Liu Xiaoming, Bollen J, Nelson M L, et al. Co-authorship networks in the digital libraryresearch community[J]. Information Processing and Management, 2005, 41(6):1462-1480.
[16] Yan E, Ding Ying. Discovering author impact: A PageRank perspective[J]. Information Processing and Management, 2011, 47(1):125-134.
[17] Guns R. Bipartite networks for link prediction: Can they improve prediction performance[C]//Proceedings of the ISSI 2011. Durban: ISSI 2011 Conference Organising Committee,2011:249-260.
[18] Zhou Yanbo, Lu Linyuan, Li Menghui. Quantifying the influence of scientists and their publications: Distinguishing between prestige and popularity[J]. New Journal of Physics,2012, 14(3):033033.
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