Temporal evolution, most influential studies and sleeping beauties of the coronavirus literature
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DOI: 10.1007/s11192-021-04036-4
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Cited by:
- Ignacio Rodríguez-Rodríguez & José-Víctor Rodríguez & Niloofar Shirvanizadeh & Andrés Ortiz & Domingo-Javier Pardo-Quiles, 2021. "Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining," IJERPH, MDPI, vol. 18(16), pages 1-29, August.
- Jianhua Hou & Hao Li & Yang Zhang, 2023. "Altmetrics-based sleeping beauties: necessity or just a supplement?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5477-5506, October.
- Houcemeddine Turki & Mohamed Ali Hadj Taieb & Mohamed Ben Aouicha, 2022. "Awakening sleeping beauties during the COVID-19 pandemic influences the citation impact of their references," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6047-6050, October.
- Breno Santana Santos & Ivanovitch Silva & Luciana Lima & Patricia Takako Endo & Gisliany Alves & Marcel da Câmara Ribeiro-Dantas, 2022. "Discovering temporal scientometric knowledge in COVID-19 scholarly production," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1609-1642, March.
- Mengjia Wu & Yi Zhang & Mark Markley & Caitlin Cassidy & Nils Newman & Alan Porter, 2024. "COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7229-7259, November.
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Keywords
Covid-19; 2019-nCoV; Coronavirus; Research synthesis; Temporal analysis; Scientometric review; Bibliographic coupling; Document co-citation; Sleeping beauties;All these keywords.
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