Research on Correlation of Evaluation Indexes of Multi-source Data for Highly Cited Papers from Medical Universities

Corresponding author: Zhang Junmin, zhangjm@ccmu.edu.cn

DOI: 10.12201/bmr.202404.00030

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  Abstract: Purpose/Significance Understand the correlation of multi-source evaluation indicators for highly cited papers in medical Universities in different databases, providing reference for the discovery, utilization, and evaluation of literature resources. Method/Process This paper takes the highly cited papers of Capital Medical University from 2013 to 2023 as the research object, and explores 38 evaluation indexes in H1 Connect, Dimensions, ESI, Web of Science, InCites and SciVal, and makes correlation analysis from representative indicators, patent and policy citation indicators, browse and use indicators, relative indicators, journal indicators, cited frequency and Altmetric indicators. Ｒesult/Conclusion It is found that the similar indicators in each group have a high correlation, individual indicators such as peer-reviewed weighted sum of stars and weibo mentions have no significant correlation with most different platform indicators.

  Key words: highly cited papers; paper evaluation index; correlation analysis; multisource data

  Submit time: 26 April 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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