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Article . 2025 . Peer-reviewed
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Integrating Visualisation Tools into the Text of an Original Research Manuscript: Lexical Bundles and Textual Comments

Authors: Elena V. Tikhonova; Daria A. Mezentseva;

Integrating Visualisation Tools into the Text of an Original Research Manuscript: Lexical Bundles and Textual Comments

Abstract

Introduction. Correct and meaningful integration of tables, graphs and other visual elements into the text of the original research manuscript significantly increases its readability index and improves the perception of complex concepts and empirical data. However, many authors face difficulties in choosing the best ways to mention and describe visualisation tools, which leads to textual redundancy or insufficient informative text. The aim of this study was to identify the most effective strategies for integrating visualisation tools into academic text by analysing textual references to visual elements. Materials and Methods. A corpus of 80 scientific articles (empirical and review articles) published in Russian, British and American journals indexed by the international database Scopus was analysed. The study included the evaluation of 432 visualisation units (tables and figures) and their 470 mentions, which were analysed in terms of their integration into the text of manuscripts. To evaluate the ways of mentioning and describing visualisations, a comprehensive procedure was applied, including search and selection of articles, extraction of raw data, their coding, analysis and systematisation. Results. The study confirmed that the existing classifications of lexical bundles act as precursors and guides to Hyland’s functional typology of lexical bundles, which is the most effective for analysing the mechanisms of integrating visual elements into a scientific text. The analysis of the corpus of scientific articles allowed to trace three main groups of Hyland’s lexical bundles: research-oriented, text-orien­ted, and participant-oriented, and to analyse the effectiveness of their references to visualization tools. The most frequent references were to the order of presentation, quantitative data and descriptive visua­lisations. However, it was found that later classifications, no longer focusing on lexical bundles but on the general textual commentary of visual elements, did not maximise the functionality and meaningful integration of visualisation tools in scientific texts. As a result, a new, content-driven classification of textual references to visualisation tools was described, which includes formal, descriptive, analytical and interpretative types. Discussion and Conclusion. The obtained results demonstrate that it is the combination of effective lexical links and meaningful textual references that can lay the foundation for the formation of a systematic approach to the effective integration of visual elements into the text of academic publications. Such symbiosis allows to significantly reduce the redundancy of the text, increase its functionality and enhance the informative and persuasive nature of academic discourse. The developed classification is recommended as a methodological tool for authors and editors seeking to optimise scientific communication.

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Keywords

text redundancy, visualisation tools, lexical bundles, academic writing, L, text informativeness, Education

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold