The Z-index: A geometric representation of productivity and impact which accounts for information in the entire rank-citation profile

TL;DR

The paper introduces the Z-index, a new metric that improves upon the h-index by incorporating total citations, providing a more balanced and robust measure of scientific impact and productivity.

Contribution

It proposes a simple generalization of the h-index that accounts for total citations, reducing bias against highly cited papers and increasing robustness to citation profile perturbations.

Findings

Z-index correlates well with total citations and h-index.

Z-index is less sensitive to local changes in citation profiles.

Z-index provides a more stable ranking for scientists.

Abstract

We present a simple generalization of Hirsch's h-index, Z = \sqrt{h^{2}+C}/\sqrt{5}, where C is the total number of citations. Z is aimed at correcting the potentially excessive penalty made by h on a scientist's highly cited papers, because for the majority of scientists analyzed, we find the excess citation fraction (C-h^{2})/C to be distributed closely around the value 0.75, meaning that 75 percent of the author's impact is neglected. Additionally, Z is less sensitive to local changes in a scientist's citation profile, namely perturbations which increase h while only marginally affecting C. Using real career data for 476 physicists careers and 488 biologist careers, we analyze both the distribution of $Z$ and the rank stability of Z with respect to the Hirsch index h and the Egghe index g. We analyze careers distributed across a wide range of total impact, including top-cited physicists and biologists for benchmark comparison. In practice, the Z-index requires the same information needed to calculate h and could be effortlessly incorporated within career profile databases, such as Google Scholar and ResearcherID. Because Z incorporates information from the entire publication profile while being more robust than h and g to local perturbations, we argue that Z is better suited for ranking comparisons in academic decision-making scenarios comprising a large number of scientists.