Analyzing the Scholarly Literature of Digital Twin Research: Trends, Topics and Structure

Abstract

Currently, studies involving a digital twin are gaining widespread interest. While the first fields adopting such a concept were in manufacturing and engineering, lately, interest extends also beyond these fields across all academic disciplines. Given the inviting idea behind a digital twin which allows the efficient exploitation and utilization of simulations such a trend is understandable. The purpose of this paper is to use a scientometrics approach to study the early publication history of the digital twin across academia. Our analysis is based on large-scale bibliographic and citation data from Scopus that provides authoritative information about high-quality publications in essentially all fields of science, engineering and humanities. This paper has four major objectives. First, we obtain a global overview of all publications related to a digital twin across all major subject areas. This analysis provides insights into the structure of the entire publication corpus. Second, we investigate the co-occurrence of subject areas appearing together on publications. This reveals interdisciplinary relations of the publications and identifies the most collaborative fields. Third, we conduct a trend and keyword analysis to gain insights into the evolution of the concept and the importance of keywords. Fourth, based on results from topic modeling using a Latent Dirichlet Allocation (LDA) model we introduce the definition of a scientometric dimension (SD) of digital twin research that allows to summarize an important aspect of the bound diversity of the academic literature.

Keywords

• Data science
• digital twin
• scientometrics
• natural language processing