卷 52, 编号 4 (2018)

The position of international scientific associations (commonwealths, unions, and alliances) in the general structure of scientific organizations, as well as the role of such associations in the formation of the scientific information space, both national and international, are shown. For the associations, we analyzed the basic methodological issues of the construction of their websites, the information content of web pages, provision of the necessary functionality and user experience, and compliance to the rules of information security. This paper discusses the sources and composition of the information published on the websites of international associations working in the science and engineering area and defines the particular features of the structure of certain websites and their functional capabilities. The design and software solutions used on these websites are characterized. A detailed comparative study of webometric ranks is carried out and the main areas of their application in decision making by Russian researchers and research teams, as well as content managers of the websites of Russian research and development establishments, universities, institutes, and scientific communities are defined.

This paper explores the possibility of extracting scientometric data from social media. Basic approaches to typification and classification of altmetrics are presented. The advantages and disadvantages of using altmetrics as scientometric indicators are investigated. The concept of webification of bibliometric indicators is introduced. Several examples of webometric indicators are discussed. Webometric indicators are designed to evaluate academic performance of researchers. A new criterion is introduced for the use in the performance-based ranking of researchers. This criterion applies webometric indicators that are calculated on the basis of altmetrics.

This paper describes the development of a data-binding algorithm. The algorithm is based on a variant of the adaptive record similarity measure (ARSM). Optimization of this variant for solving the problem of data binding is carried out using a machine-learning algorithm. The approach to estimating the adequacy of ARSM for data binding consists in introduction and automated selection of the threshold values by the machine-learning algorithm, which is achieved by introducing the fitness function representing the modification F_measure.