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Revista Cubana de Información en Ciencias de la Salud (ACIMED)

2020, Number 3

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Revista Cubana de Información en Ciencias de la Salud (ACIMED) 2020; 31 (3)

Metric analysis of the scientific production about COVID-19 in Scopus

Ortiz-Núñez R

Language: Spanish
References: 45
Page: 1-20
PDF size: 969.80 Kb.


ABSTRACT

Research about COVID-19 is the main scientific activity carried out at present. It is crucial to determine the productivity and visibility of research results associated to this disease. The purpose of the present study was to characterize the scientific production about COVID-19 recorded in the database Scopus in the period 2019 - April 2020. The study universe was all the open access papers about COVID-19 included in the database. The analysis was based on bibliometric indicators (number of papers, authors, year of publication, SJR, co-authorship networks and co-occurrence of terms and H index) and altmetric indicators (number of citations and mentions in social and scientific networks). Total scientific production was 676 papers. A predominance was found of the English language, multiple authorship and publication in SCImago first quartile journals (n= 655). A high rate of collaboration was observed (67 clusters of authors with co-authorship ratios of 1 to 7). Term co-occurrence analysis yielded 3 broad thematic groups, the main emerging research foci about COVID-19, related to description of the new coronavirus, clinical studies and treatments proposed. 95% of the publications have had at least one mention in social networks and a large number of citations. Production of scientific papers about COVID-19 has increased exponentially. It is characterized by a predominance of scientific collaboration, publication in high-impact journals and great visibility in social networks.


REFERENCES

  1. Zhou W. The Coronavirus Prevention Handbook. New York: Skyhorse Publishing; 2020.

  2. Wu F. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579:265-9.

  3. Guanche-Garcell H. COVID-19: Un reto para los profesionales de la salud. Rev Haban Cienc Méd. 2020 [acceso: 28/04/2020];19(2):e_3284. Disponible en: http://www.revhabanera.sld.cu/index.php/rhab/article/view/3284/2484

  4. Torres-Salinas D. Ritmo de crecimiento diario de la producción científica sobre Covid-19. Análisis en bases de datos y repositorios en acceso abierto. El Profes Inform. 2020;29(2):e290215.

  5. Kupferschmidt K. Preprints bring 'firehose'of outbreak data. Science. 2020;367(6481):963-4.

  6. Chahrour M. A Bibliometric Analysis of COVID-19 Research Activity: A call for increased output. cureus. 2020;12(3):e7357.

  7. Hossain MM. Current status of global research on novel coronavirus disease (Covid-19): A bibliometric analysis and knowledge mapping. SSRN Electronic Journal; 2020 http://doi.org/10.2139/ssrn.3547824

  8. Chen Q, Allot A, Lu Z. Keep up with the latest coronavirus research. Nature. 2020;579(7798):193.

  9. Inglesi-Lotz R, Pouris A. Scientometric impact assessment of a research policy instrument: the case of rating researchers on scientific outputs in South Africa. Scientometrics. 2011;88:747-60.

  10. Pouris A. Scientometric research in South Africa and successful policy instruments. Scientometrics. 2012;91(2):317-25.

  11. King DA. The scientific impact of nations. Nature. 2004;430:311-6.

  12. Solarin SA, Yen YY. A global analysis of the impact of research output on economic growth. Scientometrics. 2016;108(2):855-74.

  13. Coccia M, Rolfo S. How research policy changes can affect organization and productivity of public research institutes? Analysis within the Italian national system of innovation. J Comparat Pol Analysis. 2007;9(3):215-33.

  14. Heitor M, Horta H, Mendonça J. Developing human capital and research capacity: Science policies promoting brain gain. Technol Forecast Soc Change. 2014;82:6-22.

  15. Corrales-Reyes IE, Dorta-Contreras AJ. Producción científica cubana en Estomatología en el período 1995-2016: análisis bibliométrico en Scopus. Rev Cubana Estomatol. 2019 [acceso: 25/04/2020];56(3):1-14. Disponible en: http://scielo.sld.cu/pdf/est/v56n3/1561-297X-est-56-03-e1738.pdf

  16. Glänzel W. National characteristics in international scientific co-authorship relations. Scientometrics. 2001;51(1):69-115.

  17. Kwiek M. The internationalization of research in Europe: A quantitative study of 11 national systems from a micro-level perspective. J Stud Internat Educ. 2015;19(4):341-59.

  18. Pacheco-Mendoza J, Alhuay-Quispe J. Unidades de Bibliometría, espacios necesarios para el monitoreo de producción científica en la universidad moderna. Rev Haban Cienc Méd. 2019 [acceso: 15/04/2020];18(3):376-80. Disponible en: http://www.revhabanera.sld.cu/index.php/rhab/article/view/2874

  19. Sanz-Valero J, Tomás Casterá V, Wanden-Berghe C. Estudio bibliométrico de la producción científica publicada por la Revista Panamericana de Salud Pública/Pan American Journal of Public Health en el período de 1997 a 2012. Rev Panam Salud Pública. 2014 [acceso: 09/04/2020];35(2):81-8. Disponible en: http://www.scielosp.org/pdf/rpsp/v35n2/a01v35n2.pdf

  20. Allen L, Jones C, Dolby K, Lynn D, Walport M. Looking for landmarks: the role of expert review and bibliometric analysis in evaluating scientific publication outpus. PloSOne. 2009;4(6):e5910.

  21. Smith DR. Impact factors, scientometrics and the history of citation-based research. Scientometrics. 2012;92:419-27.

  22. Qiang-Hong P, Qiu-Ju L, Huai-Yu S. Bibliometric analysis of scientific publications in transplantation journals from Mainland China, Japan, South Korea and Taiwan between 2006 and 2015. BMJ Open. 2016 [acceso: 10/04/2020];6:1-7. Disponible en: http://bmjopen.bmj.com/content/6/8/e011623.short

  23. Torres-Salinas D, Cabezas-Clavijo A. Altmetrics: no todo lo que se puede contar, cuenta. Anuario ThinkEPI. 2013 [acceso: 10/04/2020];7:114-7. Disponible en: http://digibug.ugr.es/bitstream/10481/26361/1/114-117-Torres-Salinas-Cabezas-Almetrics.pdf

  24. Goodman D, Deis L. Web of Science (2004 version) and Scopus. The Charleston Advisor. 2005 [acceso: 15/04/2020];6(3):5-21. Disponible en: http://charlestonco.com/comp.cfm?id=43

  25. LaGuardia C. E-Views and Reviews: Scopus vs. Web of Science. Libr J. 2005 [acceso: 15/04/2020];15. Disponible en: http://www.libraryjournal.com/article/CA491154.html

  26. Bar-Ilan J. Citations to the `Introduction to infometrics` indexed by WOS, Scopus and Google Scholar. Scientometrics. 2010;3(82):495-506.

  27. Escalona-Fernández MI, Lagar-Barbosa P, Pulgarín-Guerrero A. Web of Science vs. SCOPUS: un estudio cuantitativo en Ingeniería Química. An Docum. 2010 [acceso: 15/04/2020];13:159-75. Disponible en: https://revistas.um.es/analesdoc/article/view/107121

  28. Leydesdorff L. World shares of publications of the USA, EU-27, and China compared and predicted using the new Web of Science interface versus Scopus. El Profes Inform. 2012;1(21):43-9.

  29. Chadegani AA. A comparison between two main academic literature collections: Web of Science and Scopus databases. Asian Soc Sci. 2013;9(5):18-26.

  30. Cavacini A. What is the best database for computer science journal articles? Scientometrics. 2015;102(3):2059-71.

  31. Mongeon P, Paul-Hus A. The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics. 2016;106(1):213-28.

  32. Glanzel W, Persson O. H-index for Prize medalist. ISI Newsletter. 2005;1(4):15-8.

  33. Braun T, Glanzel W, Schubert A. A Hirsch-type index for journals. Scientometrics. 2006;69(1):169-73.

  34. León L, Pérez F, Sánchez C, Damas D. Análisis bibliométrico de la retinopatía diabética en revistas médicas cubanas: un producto de información con valor agregado. Rev Cubana Inf Cienc Salud. 2019;30(4):e1381.

  35. Chaple-Gil AM, Corrales-Reyes IE, Quintana-Muñoz L, Fernández E. Indicadores bibliométricos sobre evaluación de programas de estudio de ciencias médicas en revistas biomédicas cubanas. Rev Haban Cienc Méd. 2020 [acceso: 20/04/2020];19(1):154-66. Disponible en: http://www.revhabanera.sld.cu/index.php/rhab/article/view/2668

  36. Grupo Scimago. Análisis de la cobertura de la base de datos Scopus. El Profes Inform. 2006 [acceso: 20/04/2020];15(2):144-5. Disponible en: http://www.elprofesionaldelainformacion.com/contenidos/2006/marzo/7.pdf

  37. Sugimoto CR, Larivière V. Measuring research. What everyone needs to know. New York: Oxford University Press; 2018.

  38. Van-Eck NJ, Waltman L. VOS: A new method for visualizing similarities between objects. En: Lenz HJ, Decker R, editores. Studies in classification, data analysis and knowledge organization. Berlin: Springer; 2007. p. 299-306.

  39. Borrego A. Altmétricas para la evaluación de la investigación y el análisis de necesidades de información. El Profes Inform. 2014;23(4):352-7

  40. Hammarfelt B. Using altmetrics for assessing research impact in the humanities. Scientometrics. 2014;101(2):1419-30.

  41. Dorta-Contreras AJ. Colaboración internacional y visibilidad de la producción científica por las métricas alternativas. Rev Haban Cienc Méd. 2016 [acceso: 28/04/2020];15(6):859-63. Disponible en: http://www.revhabanera.sld.cu/index.php/rhab/article/view/1260

  42. Glänzel W, Schubert A. Analysing Scientific Networks Through Co-Authorship. En: Moed HF, Glänzel W, Schmoch U, editores. Handbook of Quantitative Science and Technology Research. Netherlands: Springer; 2005. p. 257-76.

  43. Adams J. Collaborations: The rise of research networks. Nature. 2012;490:335-6.

  44. Ulnicane I. Why do international research collaborations last? Virtuous circle of feedback loops, continuity and renewal. Sci Publ Pol. 2015;42(4):433-47.

  45. López-Cózar ED, Martín-Martín A. La viralidad de la ciencia defectuosa: el contagioso impacto mediático de un preprint en bioRxiv sobre el coronavirus y sus efectos en la comunicación científica. Universidad de Granada; 2020 [acceso: 28/05/2020]. Disponible en: https://digibug.ugr.es/handle/10481/60872




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