Evidence map›Paper›PMID 34226873›Full record

ArticleApplied network science2021

Large-scale analysis of delayed recognition using sleeping beauty and the prince.

Takahiro Miura, Kimitaka Asatani, Ichiro Sakata

Abstract read
In one paragraph

Article in Applied network science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Takahiro MiuraDepartment of Technology Management for Innovation, School of Engineering, The University of Tokyo, 7-3-1 Faculty of Engineering Bldg 3, Hongo, Bunkyo, Tokyo, 113-8656 Japan.ORCID 0000-0002-3409-3044
Kimitaka AsataniDepartment of Technology Management for Innovation, School of Engineering, The University of Tokyo, 7-3-1 Faculty of Engineering Bldg 3, Hongo, Bunkyo, Tokyo, 113-8656 Japan.ORCID https://orcid.org/0000-0003-3595-8940
Ichiro SakataDepartment of Technology Management for Innovation, School of Engineering, The University of Tokyo, 7-3-1 Faculty of Engineering Bldg 3, Hongo, Bunkyo, Tokyo, 113-8656 Japan.ORCID https://orcid.org/0000-0001-5881-3790

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Delayed recognition in which innovative discoveries are re-evaluated after a long period has significant implications for scientific progress. 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's awakeness calculated based on their citation history. This research provides the methods to extract valid and large SB-PR 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-PR 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-PR 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-PR 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.

Indexed as

BibliometricsCitation networkDelayed recognitionInterdisciplinary fusionPrinceProduction of knowledgeSleeping beauty

Identifiers

PMID34226873
PMCPMC8242290

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.