Methods and models for the analysis of scientific activity

This article describes methods for analyzing scientific activity and the tools developed on their basis to support informed decision-making at all levels of management in the scientific domain — from individual researchers to the heads of research organizations. A review of domestic and international studies in the field of scientometrics is provided, highlighting key trends and identifying existing gaps that the Institute of Control Sciences of the Russian Academy of Sciences (ICS RAS) project seeks to address through the creation of an information system for the analysis of scientific activity. The project involves the development of models, methods, and algorithms for analyzing the scientific activity of researchers, organizations, journals, and conferences in the field of control theory, using publication data. This problem is approached through an interdisciplinary framework that integrates network analysis, ontological design, big data processing, machine learning techniques, and optimization methods. The proposed system is built upon an ontology of scientific knowledge in control theory, the main principles of its construction and structure being discussed in this article. Combined with the system's technological capabilities, the presented ontology enables a level of analytical detail that surpasses existing scientometric systems (such as Web of Science, Scopus, Russian Science Citation Index, etc). It is demonstrated that the principles underlying the development and structure of the proposed ontology can be applied to other research domains. The paper also considers modern approaches to data collection and visualization, as well as methods of network and content analysis of scientific texts.The main concepts, methods, and models for the analysis of scientific activity are summarized, and their implementation within the developed information system is discussed.