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The quantitative method to detect delayed recognition is described as the pair of Sleeping Beauty   (SB) and its Prince  (PR),  where SB refers to citation bursts and its PR triggers SB\u2019s awakeness calculated based on their citation history. This research provides the methods to extract valid and large SB\u2013PR pairs from a comprehensive Scopus dataset and analyses how PR discovers SB. We prove that the proposed method can extract long-sleep and large-scale SB and its PR best covers the previous multi-disciplinary pairs,  which enables to observe delayed recognition. Besides,  we show that the high-impact SB\u2013PR pairs extracted by the proposed method are more likely to be located in the same field. This indicates that a hidden SB that your research can awaken may exist closer than you think. On the other hand,  although SB\u2013PR pairs are fat-tailed in Beauty Coefficient and more likely to integrate separate fields compared to ordinary citations,  it is not possible to predict which citation leads to awake SB using the rarity of citation. There is no easy way to limit the areas where SB\u2013PR pairs occur or detect it early,  suggesting that researchers and administrators need to focus on a variety of areas. This research provides comprehensive knowledge about the development of scientific findings that will be evaluated over time.<\/jats:p>","DOI":"10.1007\/s41109-021-00389-0","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T07:08:51Z","timestamp":1625036931000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Large-scale analysis of delayed recognition using sleeping beauty and the prince"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3409-3044","authenticated-orcid":false,"given":"Takahiro","family":"Miura","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3595-8940","authenticated-orcid":false,"given":"Kimitaka","family":"Asatani","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5881-3790","authenticated-orcid":false,"given":"Ichiro","family":"Sakata","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"issue":"5","key":"389_CR1","doi-asserted-by":"publisher","first-page":"0197260","DOI":"10.1371\/journal.pone.0197260","volume":"13","author":"K Asatani","year":"2018","unstructured":"Asatani K,  Mori J,  Ochi M,  Sakata I  (2018) Detecting trends in academic research from a citation network using network representation learning. 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