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  • Методический подход к оценке цитируемости научных статей российских журналов в разрезе областей науки по данным Web of Science Core Collection. Часть 2
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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Методический подход к оценке цитируемости научных статей российских журналов в разрезе областей науки по данным Web of Science Core Collection. Часть 2

Научные и технические библиотеки. 2021. № 5. С. 73–98.
Lapochkina V., Долгова В. Н., Дикусар К. С., Богатов В. В.

In Part 2, the authors are testing the methods of assessing Russian science journals citation in the natural, agricultural sciences and the humanities based on Web of Science Core Collection. The methodological approach is based on calculating citation coefficient which is a modified version of impact factor, calculated as the ratio of the number of citing documents to the number of scientific articles in Russian journals. The method is tested on the scientific articles published in Russian journals of 2012–2018 and indexed in the Web of Science Core Collection. As a result, recommendations are developed on how to promote Russian journals to the international databases based on Web of Science Core Collection taking into account the specifics of OECD domains under the examina-
tion. The general recommendations are: to decrease the share of mono-national publications thus increasing the share of articles written in the international collaboration; and to increase the share of articles by foreign authors. The study is accomplished through the funding within the State Order of the Ministry of Science and Higher Education of the Russian federation “Organizational, technological, research and methodological support of promoting national
journals to the international science citation bases and information analytical support of the national subscription to international science information resources for scientific and educational organizations”.

Research target: Social Sciences
Language: Russian
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Keywords: OECDcitation analysisciting indexсоавторствоаффилиацияmultiple affiliationsкоэффициент цитированияцитирующие статьицитирующие документыscience domain
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