Defining and identifying Sleeping Beauties in science

TL;DR

This study introduces a parameter-free method to identify Sleeping Beauties in science, revealing that delayed recognition is common across disciplines and challenging the reliance on short-term citation metrics.

Contribution

The paper presents a new, unbiased measure for detecting Sleeping Beauties and applies it to a large, multidisciplinary dataset, demonstrating the phenomenon's prevalence.

Findings

Sleeping Beauties are more common than previously thought.

Multidisciplinary analysis reveals many delayed recognition cases.

Short-term citation metrics are unreliable for assessing scientific impact.

Abstract

A Sleeping Beauty (SB) in science refers to a paper whose importance is not recognized for several years after publication. Its citation history exhibits a long hibernation period followed by a sudden spike of popularity. Previous studies suggest a relative scarcity of SBs. The reliability of this conclusion is, however, heavily dependent on identification methods based on arbitrary threshold parameters for sleeping time and number of citations, applied to small or monodisciplinary bibliographic datasets. Here we present a systematic, large-scale, and multidisciplinary analysis of the SB phenomenon in science. We introduce a parameter-free measure that quantifies the extent to which a specific paper can be considered an SB. We apply our method to 22 million scientific papers published in all disciplines of natural and social sciences over a time span longer than a century. Our results reveal that the SB phenomenon is not exceptional. There is a continuous spectrum of delayed recognition where both the hibernation period and the awakening intensity are taken into account. Although many cases of SBs can be identified by looking at monodisciplinary bibliographic data, the SB phenomenon becomes much more apparent with the analysis of multidisciplinary datasets, where we can observe many examples of papers achieving delayed yet exceptional importance in disciplines different from those where they were originally published. Our analysis emphasizes a complex feature of citation dynamics that so far has received little attention, and also provides empirical evidence against the use of short-term citation metrics in the quantification of scientific impact.