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Automatic Merging of Scopus and Web of Science Data for Simplified and Effective Bibliometric Analysis

Author

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  • HimaJyothi Kasaraneni

    (VIT-AP University)

  • Salini Rosaline

    (VIT-AP University)

Abstract

The desideratum of organizing and synthesizing the rising corpus of publications has prompted an escalation in bibliometric studies. Bibliometric analysis is an essential statistical tool that ascertains critical information for identifying research prospects for researchers. Besides, it acts as evidence to support scientific findings. Researchers primarily use either Scopus or Web of Science (WoS) databases for conducting bibliometric analysis. The individual usage of these databases in the bibliometric analysis does not achieve the desired outcome, which requires the merging of these two databases. There are several manual processes defined in the literature for merging Scopus and WoS data. However, all these manual procedures consume more time and may lead to an inaccurate merging of the databases, as they often involve human errors due to difficulty in data scrutinization. Hence, to avoid the manual process, this paper proposes an automatic process for merging Scopus and WoS data. To understand the importance of the proposed process, a small (40 records) and large (2344 records) dataset cases are considered on which both the manual and automatic processes are implemented. From the simulation results, it is observed that the proposed process consumed 0.4497659 s on small dataset and 1.715981 s on large dataset for merging process. Thus, it can be said that the proposed automatic merging process is an effective and time-saving approach that significantly reduces human effort and the risk of committing an error. The outcome of this process is a merged dataset that includes unique data of both Scopus and WoS databases.

Suggested Citation

  • HimaJyothi Kasaraneni & Salini Rosaline, 2024. "Automatic Merging of Scopus and Web of Science Data for Simplified and Effective Bibliometric Analysis," Annals of Data Science, Springer, vol. 11(3), pages 785-802, June.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:3:d:10.1007_s40745-022-00438-0
    DOI: 10.1007/s40745-022-00438-0
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    References listed on IDEAS

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