On the calculation of percentile-based bibliometric indicators

TL;DR

This paper introduces a new method for calculating percentile-based bibliometric indicators that addresses the challenges posed by discrete citation distributions and ensures unbiased comparisons across scientific fields.

Contribution

The paper proposes a mathematically rigorous approach to compute percentile-based indicators, improving accuracy and fairness over previous methods.

Findings

The new method reduces biases in field comparisons.

It effectively handles the discrete nature of citation data.

The approach is validated within a formal mathematical framework.

Abstract

A percentile-based bibliometric indicator is an indicator that values publications based on their position within the citation distribution of their field. The most straightforward percentile-based indicator is the proportion of frequently cited publications, for instance the proportion of publications that belong to the top 10% most frequently cited of their field. Recently, more complex percentile-based indicators were proposed. A difficulty in the calculation of percentile-based indicators is caused by the discrete nature of citation distributions combined with the presence of many publications with the same number of citations. We introduce an approach to calculating percentile-based indicators that deals with this difficulty in a more satisfactory way than earlier approaches suggested in the literature. We show in a formal mathematical framework that our approach leads to indicators that do not suffer from biases in favor of or against particular fields of science.