Katchanov Yurij L, Markova Yulia V, Shmatko Natalia A
Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow 101000, Russian Federation.
American Association for the Advancement of Science, 1200 New York Ave NW, 20005, Washington, DC, USA.
Heliyon. 2019 Jul 29;5(7):e02089. doi: 10.1016/j.heliyon.2019.e02089. eCollection 2019 Jul.
Recently, there has been a surge of interest in new data emerged due to the rapid development of the information technologies in scholarly communication. Since the 2010s, altmetrics has become a common trend in scientometric research. However, researchers have not treated in much detail the question of the probability distributions underlying these new data. The principal objective of this study was to investigate one of the classic problems of scientometrics-the problem of citation and readership distributions. The study is based on the data obtained from two information systems: Web of Science and Mendeley. Here we based on the concept of the cumulative empirical distribution function to explore the differences and similarities between citations and readership counts of biological journals indexed in Web of Science and Mendeley. The basic idea was to determine, for any journal, a "size" (it is said to be the topological rank) of citation and readership empirical cumulative distributions, and then to compare distributions of the topological ranks of Web of Science and Mendeley. In order to verify our model, we employ it to the bibliometric and altmetric research of 305 biological journals indexed in Journal Citation Reports 2015. The findings show that both distributions of the topological rank of biological journals are statistically close to the Wakeby distribution. The findings presented in this study add to our understanding of information processes of the scholarly communication in the new digital environment.
近年来，由于学术交流中信息技术的快速发展，新出现的数据引发了人们极大的兴趣。自2010年代以来，替代计量学已成为科学计量学研究中的一种普遍趋势。然而，研究人员尚未详细探讨这些新数据背后的概率分布问题。本研究的主要目的是调查科学计量学的一个经典问题——引文和读者群分布问题。该研究基于从两个信息系统获取的数据：科学网（Web of Science）和门德利（Mendeley）。在此，我们基于累积经验分布函数的概念，探讨科学网和门德利索引的生物学期刊的引文和读者数量之间的异同。基本思路是为任何期刊确定引文和读者经验累积分布的“规模”（即拓扑秩），然后比较科学网和门德利的拓扑秩分布。为了验证我们的模型，我们将其应用于2015年《期刊引证报告》索引的305种生物学期刊的文献计量学和替代计量学研究。研究结果表明，生物学期刊的拓扑秩分布在统计上均与韦克比分布接近。本研究的结果增进了我们对新数字环境中学术交流信息过程的理解。
