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dc.contributor.authorNane, Gabriela F.
dc.contributor.authorLarivière, Vincent
dc.contributor.authorCostas, Rodrigo
dc.date.accessioned2020-04-21T19:31:08Z
dc.date.availableNO_RESTRICTIONfr
dc.date.available2020-04-21T19:31:08Z
dc.date.issued2017-08
dc.identifier.urihttp://hdl.handle.net/1866/23293
dc.publisherElsevierfr
dc.rightsCe document est mis à disposition selon les termes de la Licence Creative Commons Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titlePredicting the age of researchers using bibliometric datafr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. Faculté des arts et des sciences. École de bibliothéconomie et des sciences de l'informationfr
dc.identifier.doi10.1016/j.joi.2017.05.002
dcterms.abstractThe age of researchers is a critical factor necessary to study the bibliometric characteristics of the scholars that produce new knowledge. In bibliometric studies, the age of scientific authors is generally missing; however, the year of the first publication is frequently considered as a proxy of the age of researchers. In this article, we investigate what are the most important bibibliometric factors that can be used to predict the age of researchers (birth and PhD age). Using a dataset of 3574 researchers from Québec for whom their Web of Science publications, year of birth and year of their PhD are known, our analysis falls under the linear regression setting and focuses on investigating the predictive power of various regression models rather than data fitting, considering also a breakdown by fields. The year of first publication proves to be the best linear predictor for the age of researchers. When using simple linear regression models, predicting birth and PhD years result in an error of about 3.7 years and 3.9 years, respectively. Including other bibliometric data marginally improves the predictive power of the regression models. A validation analysis for the field breakdown shows that the average length of the prediction intervals vary from 2.5 years for Basic Medical Sciences (for birth years) up to almost 10 years for Education (for PhD years). The average models perform significantly better than the models using individual observations. Nonetheless, the high variability of data and the uncertainty inherited by the models advice to caution when using linear regression models for predicting the age of researchers.fr
dcterms.isPartOfurn:ISSN:1751-1577fr
dcterms.isPartOfurn:ISSN:1751-1577fr
dcterms.languageengfr
UdeM.ReferenceFournieParDeposantNane, G.F., Larivière, V., Costas, R. (2017). Predicting the age of researchers using bibliometric data. Journal of Informetrics 11 (3), 713-729fr
UdeM.VersionRioxxVersion acceptée / Accepted Manuscriptfr
oaire.citationTitleJournal of informetrics
oaire.citationVolume11
oaire.citationIssue3
oaire.citationStartPage713
oaire.citationEndPage729


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Ce document est mis à disposition selon les termes de la Licence Creative Commons Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0 International License.
RightsCe document est mis à disposition selon les termes de la Licence Creative Commons Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0 International License.

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