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On the calculation of percentile-based bibliometric indicators.

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Title: On the calculation of percentile-based bibliometric indicators.
Authors: Waltman, Ludo1 waltmanlr@cwts.leidenuniv.nl, Schreiber, Michael2 schreiber@physik.tu-chemnitz.de
Source: Journal of the American Society for Information Science & Technology. Feb2013, Vol. 64 Issue 2, p372-379. 8p. 2 Charts, 1 Graph.
Subjects: Bibliometrics, Mathematics methodology, Publishing
Abstract: A percentile-based bibliometric indicator is an indicator that values publications based on their position within the citation distribution of their field. The most straightforward percentile-based indicator is the proportion of frequently cited publications, for instance, the proportion of publications that belong to the top 10% most frequently cited of their field. Recently, more complex percentile-based indicators have been proposed. A difficulty in the calculation of percentile-based indicators is caused by the discrete nature of citation distributions combined with the presence of many publications with the same number of citations. We introduce an approach to calculating percentile-based indicators that deals with this difficulty in a more satisfactory way than earlier approaches suggested in the literature. We show in a formal mathematical framework that our approach leads to indicators that do not suffer from biases in favor of or against particular fields of science. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: On the calculation of percentile-based bibliometric indicators.
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  Data: <searchLink fieldCode="AR" term="%22Waltman%2C+Ludo%22">Waltman, Ludo</searchLink><relatesTo>1</relatesTo><i> waltmanlr@cwts.leidenuniv.nl</i><br /><searchLink fieldCode="AR" term="%22Schreiber%2C+Michael%22">Schreiber, Michael</searchLink><relatesTo>2</relatesTo><i> schreiber@physik.tu-chemnitz.de</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science+%26+Technology%22">Journal of the American Society for Information Science & Technology</searchLink>. Feb2013, Vol. 64 Issue 2, p372-379. 8p. 2 Charts, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+methodology%22">Mathematics methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Publishing%22">Publishing</searchLink>
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  Data: A percentile-based bibliometric indicator is an indicator that values publications based on their position within the citation distribution of their field. The most straightforward percentile-based indicator is the proportion of frequently cited publications, for instance, the proportion of publications that belong to the top 10% most frequently cited of their field. Recently, more complex percentile-based indicators have been proposed. A difficulty in the calculation of percentile-based indicators is caused by the discrete nature of citation distributions combined with the presence of many publications with the same number of citations. We introduce an approach to calculating percentile-based indicators that deals with this difficulty in a more satisfactory way than earlier approaches suggested in the literature. We show in a formal mathematical framework that our approach leads to indicators that do not suffer from biases in favor of or against particular fields of science. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/asi.22775
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              Text: Feb2013
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