Impact, productivity, and scientific excellence

Citation metrics are becoming pervasive in the quantitative evaluation of schol-ars, journals and institutions. More then ever before, hiring, promotion, and funding decisions rely on a variety of impact metrics that cannot disentan-gle quality from productivity, and are biased by factors such as discipline and academic age. Biases affecting the evaluation of single papers are com-pounded when one aggregates citation-based metrics across an entire publica-tion record. It is not trivial to compare the quality of two scholars that during their careers have published at different rates in different disciplines in dif-ferent periods of time. We propose a novel solution based on the generation of a statistical baseline specifically tailored on the academic profile of each re-searcher. By decoupling productivity and impact, our method can determine whether a certain level of impact can be explained by productivity alone, or additional ingredients of scientific excellence are necessary. The method is flexible enough to allow for the evaluation of, and fair comparison among, ar-bitrary collections of papers — scholar publication records, journals, and en-tire institutions; and can be extended to simultaneously suppresses any source of bias. We show that our method can capture the quality of the work of Nobel 1 a