Abstract

We find evidence for the universality of two relative bibliometric indicators of the quality of individual scientific publications taken from different data sets. One of these is a new index that considers both citation and reference counts. We demonstrate this universality for relatively well cited publications from a single institute, grouped by year of publication and by faculty or by department. We show similar behaviour in publications submitted to the arXiv e-print archive, grouped by year of submission and by sub-archive. We also find that for reasonably well cited papers this distribution is well fitted by a lognormal with a variance of around σ2 = 1.3 which is consistent with the results of Radicchi et al. (Proc Natl Acad Sci USA 105:17268–17272, 2008). Our work demonstrates that comparisons can be made between publications from different disciplines and publication dates, regardless of their citation count and without expensive access to the whole world-wide citation graph. Further, it shows that averages of the logarithm of such relative bibliometric indices deal with the issue of long tails and avoid the need for statistics based on lengthy ranking procedures.

Highlights

  • The use of relative bibliometric indicators to provide robust measures has been discussed in several contexts [2, 3, 4, 5, 6, 1, 7, 8, 9, 10, 11, 12, 13, 14]

  • The number of references is the length of the bibliography even if not all elements in that bibliography are included in Web of Science (WoS)

  • A reference to a book will be counted in r but the citations from that book will not, since books are not part of WoS

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Summary

Introduction

The use of relative bibliometric indicators to provide robust measures has been discussed in several contexts [2, 3, 4, 5, 6, 1, 7, 8, 9, 10, 11, 12, 13, 14]. RFC [1] used Thomson Reuters’s Journal of Citation Reports, which allocates one or more fields to each journal, to assign fields to each paper This index cf gives a measure of the significance of a given paper which can be used compare papers from a wide range of disciplines and published at different times. By showing that in all cases a lognormal distribution is a reasonable model for the data, we have demonstrated that these useful indices can be applied on a large number of smaller datasets. As such data may already be available for other reasons, our results will lead to a reduction in the costs of research assessment, be this for academic research or for administrative reasons. An extensive list of tables and additional plots are given in the supplementary material

Definition of Indicators
Results for a Single Institute
The cf measure for faculties
The cr measure for faculties
Comparison of cf and cr for faculties
Departments
Interpretation
Conclusions
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