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t-index: entropy based random document and citation analysis using average h-index

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Abstract

There are many attempts have been made to measure the research outcome and impact of any author or institutes using h-index, g-index, e-index, s-index, m-index and other parameters recently. It becomes more complex when the performance is measured from 2 million documents and more than 30 million citations. In this case, domain based expert and performance analysis for the given time phase is indeed requirement for a fair comparison among two institutes (or authors) rather than whole data sets. One of the reason is many institutes (or authors) publishes several papers randomly in distinct domain for document or citation count rather than a projected domain. To solve this issue, a new metric called as ``t-index’’ is introduced in this paper using Shannon entropy and yearly average of h-index. The proposed method is illustrated for computer science domain using the Scopus data. The obtained results from t-index are compared with h-index for validating the results.

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Data availability and material

The data is taken from SCOPUS.

Code availability

There is a Python code for this paper.

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Acknowledgements

Author thanks the anonymous reviewers for their valuable time and comments to improve the quality of this paper.

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  1. Department of Computer Science and Engineering, Gandhi Institute of Technology and Management, Vishakhapatnam, Andhra Pradesh, 530045, India

    Prem Kumar Singh

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  1. Prem Kumar Singh
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The idea was conceptualized by Dr. Prem Kumar Singh.

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Correspondence to Prem Kumar Singh.

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Singh, P.K. t-index: entropy based random document and citation analysis using average h-index. Scientometrics 127, 637–660 (2022). https://doi.org/10.1007/s11192-021-04222-4

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