Defining and identifying Sleeping Beauties in science
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Abstract (arXiv)
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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 1; 2025: 4; 2026: 2.
11 papers.
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- 2021A Measure of Research Tastecited by 1
- 2023A multiple k-means cluster ensemble framework for clustering citation trajectoriescited by 1
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- 2025Benchmark Datasets for Lead-Lag Forecasting on Social Platformscited by 0
- 2025Deep versus Broad Technology Search and the Timing of Innovation Impactcited by 0
- 2026Catalyst Papers in Artificial Intelligence Research: A Landscape on ICLR from 2017 to 2025cited by 0
- 2026Catalyst Papers in Artificial Intelligence Research: A Landscape on ICLR from 2017 to 202524 May 2026
- 2026pAI/MSc: ML Theory Research with Humans on the Loop22 Apr 2026
- 2025Deep versus Broad Technology Search and the Timing of Innovation Impact30 Dec 2025
- 2025Benchmark Datasets for Lead-Lag Forecasting on Social Platforms5 Nov 2025
- 2025Emergent evaluation hubs in a decentralizing large language model ecosystem30 Sep 2025
- 2025A review on the novelty measurements of academic papers29 Jan 2025
- 2024Forecasting high-impact research topics via machine learning on evolving knowledge graphs13 Feb 2024
- 2023A multiple k-means cluster ensemble framework for clustering citation trajectories10 Sep 2023
- 2023Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction27 May 2023
- 2023Disjunctive Branch-and-Bound for Certifiably Optimal Low-Rank Matrix Completion20 May 2023
- 2021A Measure of Research Taste17 May 2021