Bibliometric data from various databases are crucial for exploring research trends through a bibliometric analysis. Usually, deduplicating records and merging several citation index databases for bibliometric research is tedious, particularly when dealing with larger datasets. Although several manual and automatic merging processes are available in the academic literature, some key issues were identified during the implementation of existing merging processes. To address such issues, this paper proposes an open-source preprocessing pipeline developed using R programming for a simple merging of bibliometric data collected from multiple databases. This open-source reproducible preprocessing pipeline precompiles and deduplicates records based on a Digital Object Identifier (DOI). To implement this proposed research work, bibliometric data are considered from Scopus, Web of Science and Lens databases. The key outcomes of this research work are identifying multiple DOIs and Titles, standardizing the DOIs, and deduplicating records to obtain a merged dataset without noisy data. This enables researchers to conduct an effective bibliometric analysis.
Prakash et al. (Mon,) studied this question.