Where Is the Automation Discourse Heading? Exploring Evolution and Trends of RPA Research Through Topic Modeling

Description of the Dataset The dataset consists of two Excel files: data.xlsx and RPA_LDA_RESULTS_(k=100).xlsx. A description of each file is provided below. Excel File: data.xlsxThis file provides a comprehensive resource for bibliometric and topic modeling analyses. It includes multiple sheets containing Scopus bibliographic data, topic distributions, and derived metrics, enabling detailed exploration of thematic structures and trends within scholarly publications. Data from ScopusThis sheet contains bibliographic metadata exported from Scopus, designed for use in Latent Dirichlet Allocation (LDA) analyses. Key fields include authors, abstracts, publication years, and other relevant publication information. Additionally, each paper is annotated with manually or algorithmically assigned categories and topics, supporting thematic classification and subsequent analysis. Figures 8A and 9AThis sheet provides a detailed table of all identified topics, including associated terms, total paper counts, and citation counts per topic. These data correspond to visualizations presented in the appendix of the related publication, offering insight into topic prevalence and impact. Calculation of Combined ScoreHere, topics are ranked according to a calculated combined score, which reflects their relative importance or influence within the dataset. The scoring integrates multiple metrics, allowing for prioritization of key topics for deeper investigation. Excel File: RPA_LDA_RESULTS_(k=100).xlsx This file contains the results of an LDA analysis with 100 topics, organized into two sheets: top_100_terms_in_each_topic – Lists the top 100 most representative terms for each topic, facilitating interpretation of thematic content. probabilities_with_each_topic – Provides the topic probabilities for all 3,275 documents across the 100 topics, enabling exploration of topic distributions at the document level.