- -

Universities through the eyes of bibliographic databases: a retroactive growth comparison of Google Scholar, Scopus and Web of Science

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Universities through the eyes of bibliographic databases: a retroactive growth comparison of Google Scholar, Scopus and Web of Science

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Orduña Malea, Enrique es_ES
dc.contributor.author Aytac, Selenay es_ES
dc.contributor.author Tran, Clara Y. es_ES
dc.date.accessioned 2023-04-19T18:01:16Z
dc.date.available 2023-04-19T18:01:16Z
dc.date.issued 2019-10 es_ES
dc.identifier.issn 0138-9130 es_ES
dc.identifier.uri http://hdl.handle.net/10251/192847
dc.description.abstract [EN] The purpose of this study is to ascertain the suitability of GS's url-based method as a valid approximation of universities' academic output measures, taking into account three aspects (retroactive growth, correlation, and coverage). To do this, a set of 100 Turkish universities were selected as a case study. The productivity in Web of Science (WoS), Scopus and GS (2000-2013) were captured in two different measurement iterations (2014 and 2018). In addition, a total of 18,174 documents published by a subset of 14 research-focused universities were retrieved from WoS, verifying their presence in GS within the official university web domain. Findings suggest that the retroactive growth in GS is unpredictable and dependent on each university, making this parameter hard to evaluate at the institutional level. Otherwise, the correlation of productivity between GS (url-based method) and WoS and Scopus (selected sources) is moderately positive, even though it varies depending on the university, the year of publication, and the year of measurement. Finally, only 16% out of 18,174 articles analyzed were indexed in the official university website, although up to 84% were indexed in other GS sources. This work proves that the url-based method to calculate institutional productivity in GS is not a good proxy for the total number of publications indexed in WoS and Scopus, at least in the national context analyzed. However, the main reason is not directly related to the operation of GS, but with a lack of universities' commitment to open access. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Scientometrics es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Universities es_ES
dc.subject Google Scholar es_ES
dc.subject Bibliometrics es_ES
dc.subject Web of Science es_ES
dc.subject Scopus es_ES
dc.subject Academic search engines es_ES
dc.subject Research productivity es_ES
dc.subject Retroactive growth es_ES
dc.subject Bibliographic databases es_ES
dc.subject Turkey es_ES
dc.subject.classification BIBLIOTECONOMIA Y DOCUMENTACION es_ES
dc.title Universities through the eyes of bibliographic databases: a retroactive growth comparison of Google Scholar, Scopus and Web of Science es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11192-019-03208-7 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Facultad de Bellas Artes - Facultat de Belles Arts es_ES
dc.description.bibliographicCitation Orduña Malea, E.; Aytac, S.; Tran, CY. (2019). Universities through the eyes of bibliographic databases: a retroactive growth comparison of Google Scholar, Scopus and Web of Science. Scientometrics. 121(1):433-450. https://doi.org/10.1007/s11192-019-03208-7 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11192-019-03208-7 es_ES
dc.description.upvformatpinicio 433 es_ES
dc.description.upvformatpfin 450 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 121 es_ES
dc.description.issue 1 es_ES
dc.relation.pasarela S\401967 es_ES
dc.description.references Aguillo, I. F. (2012). Is Google Scholar useful for bibliometrics? A webometric analysis. Scientometrics, 91(2), 343–351. https://doi.org/10.1007/s11192-011-0582-8 . es_ES
dc.description.references Aguillo, I. F., Ortega, J. L., & Fernández, M. (2008). Webometric ranking of world universities: Introduction, methodology, and future developments. Higher Education in Europe, 33(2–3), 233–244. https://doi.org/10.1080/03797720802254031 . es_ES
dc.description.references Amara, N., Landry, R., & Halilem, N. (2015). What can university administrators do to increase the publication and citation scores of their faculty members? Scientometrics, 103(2), 489–530. https://doi.org/10.1007/s11192-015-1537-2 . es_ES
dc.description.references Arlitsch, K., & O’Brian, P. S. (2012). Invisible institutional repositories: Addressing the low indexing ratios of IRs in Google. Library Hi Tech, 30(1), 60–81. https://doi.org/10.1108/07378831211213210 . es_ES
dc.description.references Aytac, S. (2010). An examination of international scientific collaboration in a developing country (Turkey) in the post Internet era. Brookville, NY: Long Island University. es_ES
dc.description.references De Winter, J. C., Zadpoor, A. A., & Dodou, D. (2014). The expansion of Google Scholar versus Web of Science: A longitudinal study. Scientometrics, 98(2), 1547–1565. https://doi.org/10.1007/s11192-013-1089-2 . es_ES
dc.description.references Delgado López-Cózar, E., Orduna-Malea, E., & Martín-Martín, A. (2019). Google scholar as a data source for research assessment. In W. Glänzel, H. F. Moed, U. Schmoch, & M. Thelwall (Eds.), Springer handbook of science and technology indicators. Heidelberg: Springer. es_ES
dc.description.references Franceschini, F., Maisano, D., & Mastrogiacomo, L. (2016a). Empirical analysis and classification of database errors in Scopus and Web of Science. Journal of Informetrics, 10(4), 933–953. https://doi.org/10.1016/j.joi.2016.07.003 . es_ES
dc.description.references Franceschini, F., Maisano, D., & Mastrogiacomo, L. (2016b). The museum of errors/horrors in Scopus. Journal of Informetrics, 10(1), 174–182. https://doi.org/10.1016/j.joi.2015.11.006 . es_ES
dc.description.references Gusenbauer, M. (2019). Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases. Scientometrics, 118(1), 177–214. https://doi.org/10.1007/s11192-018-2958-5 . es_ES
dc.description.references Harzing, A. W. (2013). A preliminary test of Google Scholar as a source for citation data: a longitudinal study of Nobel prize winners. Scientometrics, 94(3), 1057–1075. https://doi.org/10.1007/s11192-012-0777-7 . es_ES
dc.description.references Harzing, A. W. (2014). A longitudinal study of Google Scholar coverage between 2012 and 2013. Scientometrics, 98(1), 565–575. https://doi.org/10.1007/s11192-013-0975-y . es_ES
dc.description.references Hook, D. W., Porter, S. J., & Herzog, C. (2018). Dimensions: Building context for search and evaluation. Frontiers in Research Metrics and Analytics, 3, 23. https://doi.org/10.3389/frma.2018.00023 . es_ES
dc.description.references Jacsó, P. (2010). Metadata mega mess in Google Scholar. Online Information Review, 34(1), 175–191. https://doi.org/10.1108/14684521011024191 . es_ES
dc.description.references Martín-Martín, A., Orduna-Malea, E., Ayllón, J. M., & Delgado López-Cozar, E. (2016). A two-sided academic landscape: snapshot of highly-cited documents in Google Scholar (1950-2013). Revista española de documentación científica, 39(4), 1–21. https://doi.org/10.3989/redc.2016.4.1405 . es_ES
dc.description.references Mingers, J., & Meyer, M. (2017). Normalizing Google Scholar data for use in research evaluation. Scientometrics, 112(2), 1111–1121. https://doi.org/10.1007/s11192-017-2415-x . es_ES
dc.description.references Mingers, J., O’Hanley, J. R., & Okunola, M. (2017). Using Google Scholar institutional level data to evaluate the quality of university research. Scientometrics, 113(3), 1627–1643. https://doi.org/10.1007/s11192-017-2532-6 . es_ES
dc.description.references Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5 . es_ES
dc.description.references Moskovkin, V. M. (2009). The potential of using the Google Scholar search engine for estimating the publication activities of universities. Scientific and Technical Information Processing, 36(4), 198–202. https://doi.org/10.3103/S0147688209040029 . es_ES
dc.description.references Moskovkin, V. M., Delux, T., & Moskovkina, M. V. (2012). Comparative analysis of university publication activity by google scholar: (On Example of Leading Czech and Germany Universities). Cybermetrics: International Journal of Scientometrics, Informetrics and Bibliometrics, 16(1), 1–9. http://hdl.handle.net/10261/174558 es_ES
dc.description.references Orduna-Malea, E., Ayllon, J. M., Martín-Martín, A., & Delgado López-Cózar, E. (2017a). The lost academic home: Institutional affiliation links in Google Scholar Citations. Online Information Review, 41(6), 762–781. https://doi.org/10.1108/OIR-10-2016-0302 . es_ES
dc.description.references Orduna-Malea, E., & Delgado López-Cózar, E. (2015). The dark side of Open Access in Google and Google Scholar: The case of Latin-American repositories. Scientometrics, 102(1), 829–846. https://doi.org/10.1007/s11192-014-1369-5 . es_ES
dc.description.references Orduna-Malea, E., & Delgado-López-Cózar, E. (2018). Dimensions: Re-discovering the ecosystem of scientific information. El Profesional de la Información, 27(2), 420–431. https://doi.org/10.3145/epi.2018.mar.21 . es_ES
dc.description.references Orduña-Malea, E., Martín-Martín, A., Ayllón, Juan M., & Delgado López-Cózar, E. (2016). La revolución Google Scholar: Destapando la caja de Pandora académica. UNE: Granada. es_ES
dc.description.references Orduna-Malea, E., Martín-Martín, A., & Delgado López-Cozar, E. (2017b). Google Scholar as a source for scholarly evaluation: A bibliographic review of database errors. Revista española de documentación científica, 40(4), 1–33. https://doi.org/10.3989/redc.2017.4.1500 . es_ES
dc.description.references Orduña-Malea, E., Serrano-Cobos, J., & Lloret-Romero, N. (2009). Las Universidades públicas españolas en Google Scholar: Presencia y evolución en su publicación académica web. El profesional de la información, 5(18), 493–501. https://doi.org/10.3145/epi.2009.sep.02 . es_ES
dc.description.references Ortega, J. L. (2014). Academic search engines: A quantitative outlook. Oxford: Chandos Publishing. es_ES
dc.description.references Ramsden, P. (1994). Describing and explaining research productivity. Higher Education, 28(2), 207–226. https://doi.org/10.1007/BF01383729 . es_ES
dc.description.references Ranjbar-Sahraei, B., van Eck, N. J., & de Jong, R. (2018). Accuracy of affiliation information in Microsoft Academic: Implications for institutional level research evaluation. In R. Costas, T. Franssen, & A. Yegros-Yegros (Eds.), STI 2018 conference proceedings: Proceedings of the 23rd international conference on science and technology indicators (pp. 1065–1067). Leiden: Centre for Science and Technology Studies (CWTS), Leiden University. http://hdl.handle.net/1887/65339 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem