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Citation Metrics: A Primer on How (Not) to Normalize

Citation metrics are increasingly used to appraise published research. One challenge is whether and how to normalize these metrics to account for differences across scientific fields, age (year of publication), type of document, database coverage, and other factors. We discuss the pros and cons for...

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Detalles Bibliográficos
Autores principales: Ioannidis, John P. A., Boyack, Kevin, Wouters, Paul F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012555/
https://www.ncbi.nlm.nih.gov/pubmed/27599158
http://dx.doi.org/10.1371/journal.pbio.1002542
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author Ioannidis, John P. A.
Boyack, Kevin
Wouters, Paul F.
author_facet Ioannidis, John P. A.
Boyack, Kevin
Wouters, Paul F.
author_sort Ioannidis, John P. A.
collection PubMed
description Citation metrics are increasingly used to appraise published research. One challenge is whether and how to normalize these metrics to account for differences across scientific fields, age (year of publication), type of document, database coverage, and other factors. We discuss the pros and cons for normalizations using different approaches. Additional challenges emerge when citation metrics need to be combined across multiple papers to appraise the corpus of scientists, institutions, journals, or countries, as well as when trying to attribute credit in multiauthored papers. Different citation metrics may offer complementary insights, but one should carefully consider the assumptions that underlie their calculation.
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spelling pubmed-50125552016-09-27 Citation Metrics: A Primer on How (Not) to Normalize Ioannidis, John P. A. Boyack, Kevin Wouters, Paul F. PLoS Biol Perspective Citation metrics are increasingly used to appraise published research. One challenge is whether and how to normalize these metrics to account for differences across scientific fields, age (year of publication), type of document, database coverage, and other factors. We discuss the pros and cons for normalizations using different approaches. Additional challenges emerge when citation metrics need to be combined across multiple papers to appraise the corpus of scientists, institutions, journals, or countries, as well as when trying to attribute credit in multiauthored papers. Different citation metrics may offer complementary insights, but one should carefully consider the assumptions that underlie their calculation. Public Library of Science 2016-09-06 /pmc/articles/PMC5012555/ /pubmed/27599158 http://dx.doi.org/10.1371/journal.pbio.1002542 Text en © 2016 Ioannidis et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Perspective
Ioannidis, John P. A.
Boyack, Kevin
Wouters, Paul F.
Citation Metrics: A Primer on How (Not) to Normalize
title Citation Metrics: A Primer on How (Not) to Normalize
title_full Citation Metrics: A Primer on How (Not) to Normalize
title_fullStr Citation Metrics: A Primer on How (Not) to Normalize
title_full_unstemmed Citation Metrics: A Primer on How (Not) to Normalize
title_short Citation Metrics: A Primer on How (Not) to Normalize
title_sort citation metrics: a primer on how (not) to normalize
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012555/
https://www.ncbi.nlm.nih.gov/pubmed/27599158
http://dx.doi.org/10.1371/journal.pbio.1002542
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