Highly Cited Documents of Ukrainian Scholars in Citation Databases: Correlation of Bibliometric Indicators
Year: 2020
Type: article
Abstract: The purpose of this work is to review the potential use of scientometric and bibliometric indicators to assess the results of scientific activities and outline current trends and correlations of data and indicators of highly cited documents of Ukrainian authors in leading citation databases.Based on the analysis of selected documents and citations of Ukrainian researchers in three citation databases Dimensions, Scopus, Web of Science Core Collection, as well as taking into account some indicators of Altmetrics and using indicators such as Scopus Citation benchmarking, Plum X Metrics for time period of 2017–2019, this paper highlights the features, trends and correlations of individual indicators for highly cited publications of Ukrainian researchers. There are a number of examples illustrating the study. It was found that the first ten highly cited documents of Ukrainian authors are the work of a large number of co-authors, in particular foreign ones, at the same time there are documents where scientists with Ukrainian affiliation are the main authors; top-list journals are not Ukrainian sources; most Ukrainian-affiliated documents obtained from searches in leading citation databases are published in journals with open access publication model, although the absence of full text and mention of the document on the Internet does not mean the absence of citations, if it is of interest to the scientific community; documents from Ukrainian authorsreceived the most citations in the following fields of knowledge: medical, physical, mathematical, computer and chemical sciences.The authors of this study confirm, in particular, the obtained data correlate between the number of citations and the international academic partnership, access type and type of publication, source and place of publication, etc.
Authors: Tetiana Yaroshenko, Oleksandra Yaroshenko
Institution: National University of Kyiv-Mohyla Academy
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W3038260294