Research assessment by percentile-based double rank analysis

TL;DR

This paper introduces a universal, percentile-based double rank analysis method for research assessment that models publication distributions with power laws, applicable across all citation levels and enabling evaluation of breakthroughs.

Contribution

It presents a novel, universal approach to research evaluation using double rank analysis fitted to power laws, applicable to all publication percentiles.

Findings

Distribution of publications follows a double rank power law.

Method applies to all percentiles, including low ones.

Enables estimation of high-impact publication probabilities.

Abstract

In the double rank analysis of research publications, the local rank position of a country or institution publication is expressed as a function of the world rank position. Excluding some highly or lowly cited publications, the double rank plot fits well with a power law, which can be explained because citations for local and world publications follow lognormal distributions. We report here that the distribution of the number of country or institution publications in world percentiles is a double rank distribution that can be fitted to a power law. Only the data points in high percentiles deviate from it when the local and world $\mu$ parameters of the lognormal distributions are very different. The likelihood of publishing very highly cited papers can be calculated from the power law that can be fitted either to the upper tail of the citation distribution or to the percentile-based double rank distribution. The great advantage of the latter method is that it has universal application, because it is based on all publications and not just on highly cited publications. Furthermore, this method extends the application of the well-established percentile approach to very low percentiles where breakthroughs are reported but paper counts cannot be performed.