Sleeping Beauties of Coronavirus Research.

Beauty score COVID-19 activity index bibliometric coronavirus relative specialization index sleeping beauty

Journal

IEEE access : practical innovations, open solutions
ISSN: 2169-3536
Titre abrégé: IEEE Access
Pays: United States
ID NLM: 101639462

Informations de publication

Date de publication:
2021
Historique:
received: 23 11 2020
accepted: 15 12 2020
entrez: 17 11 2021
pubmed: 18 11 2021
medline: 18 11 2021
Statut: epublish

Résumé

A "Sleeping Beauty" (SB) in science is a metaphor for a scholarly publication that remains relatively unnoticed by the related communities for a long time; - the publication is "sleeping". However, suddenly due to the appearance of some phenomenon, such a "forgotten" publication may become a center of scientific attention; - the SB is "awakened". Currently, there are specific scientific areas for which sleeping beauties (SBs) are awakened. For example, as the world is experiencing the COVID-19 global pandemic (triggered by SARS-CoV-2), publications on coronaviruses appear to be awakened. Thus, one can raise questions of scientific interest: are these publications coronavirus related SBs? Moreover, while much literature exists on other coronaviruses, there seems to be no comprehensive investigation on COVID-19, - in particular in the context of SBs. Nowadays, such SB papers can be even used for sustaining literature reviews and/or scientific claims about COVID-19. In our study, in order to pinpoint pertinent SBs, we use the "beauty score" (B-score) measure. The Activity Index (AI) and the Relative Specialization Index (RSI) are also calculated to compare countries where such SBs appear. Results show that most of these SBs were published previously to the present epidemic time (triggered by SARS-CoV or SARS-CoV-1), and are awakened in 2020. Besides outlining the most important SBs, we show from what countries and institutions they originate, and the most prolific author(s) of such SBs. The citation trend of SBs that have the highest B-score is also discussed.

Identifiants

pubmed: 34786306
doi: 10.1109/ACCESS.2021.3052918
pmc: PMC8545245
doi:

Types de publication

Journal Article

Langues

eng

Pagination

21192-21205

Informations de copyright

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

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Auteurs

Mohsen Fazeli-Varzaneh (M)

Department of Knowledge and Information ScienceFaculty of ManagementUniversity of Tehran Tehran 1417466191 Iran.

Ali Ghorbi (A)

Department of Knowledge and Information ScienceFaculty of ManagementUniversity of Tehran Tehran 1417466191 Iran.

Marcel Ausloos (M)

School of BusinessUniversity of Leicester Leicester LE2 1RQ U.K.
Department of Statistics and EconometricsBucharest University of Economic Studies 010552 Bucharest Romania.
GRAPES 4031 Liege Belgium.

Emanuel Sallinger (E)

Faculty of InformaticsTU Wien 1040 Vienna Austria.
Department of Computer ScienceUniversity of Oxford Oxford OX1 3QD U.K.

Sahar Vahdati (S)

Nature-Inspired Machine IntelligenceInstitute of Applied Informatics (InfAI) 01069 Dresden Germany.

Classifications MeSH