EDITORIAL
`
`The most influential journals: Impact Factor
`and Eigenfactor
`
`Alan Fersht1
`Medical Research Council Centre for Protein Engineering, Cambridge CB2 0QH, United Kingdom
`
`Plot of the 2007 Eigenfactor rating against total number of citations listed in the Journal Citation
`Fig. 1.
`Reports姞.
`
`article. She then proceeds to the
`journal that was cited, reads a ran-
`dom article there, and selects a cita-
`tion to direct her to her next journal
`volume. The researcher does this ad
`infinitum.
`The Eigenfactor™ is now listed by Jour-
`nal Citation Reports威. In practice, there
`is a strong correlation between Eigen-
`factors and the total number of citations
`received by a journal (2). A plot of the
`2007 Eigenfactors for the top 200 cited
`journals against the total number of ci-
`tations shows some startling results (Fig.
`1). Three journals have by far and away
`the most overall influence on science:
`Nature, PNAS, and Science, closely fol-
`lowed by the Journal of Biological Chem-
`istry. So, publish in PNAS with the full
`knowledge that you are contributing to
`one of the most influential drivers of
`scientific progress.
`The terrible legacy of IF is that it is
`being used to evaluate scientists, rather
`than journals, which has become of
`increasing concern to many of us.
`Judgment of individuals is, of course,
`best done by in-depth analysis by ex-
`pert scholars in the subject area. But,
`some bureaucrats want a simple metric.
`My experience of being on interna-
`tional review committees is that more
`notice is taken of IF when they do not
`
`have the knowledge to evaluate the
`science independently.
`An extreme example of such behavior
`is an institute in the heart of the Euro-
`pean Union that evaluates papers from
`its staff by having a weighting factor of
`0 for all papers published in journals
`with IF ⬍5 and just a small one for 5 ⬍
`IF ⬍ 10. So, publishing in the Journal of
`Molecular Biology counts for naught,
`despite its being at the top for areas
`such as protein folding.
`All journals have a spread of cita-
`tions, and even the best have some pa-
`pers that are never cited plus some
`fraudulent papers and some excruciat-
`ingly bad ones. So, it is ludicrous to
`judge an individual paper solely on the
`IF of the journal in which it is
`published.
`Fortunately, PNAS has both a good
`IF and a high reliability because of its
`access to so many expert National Acad-
`emy of Sciences member–editors. If a
`paper has to be judged by a metric, then
`it should by the citations to it and not to
`the journal. The least evil of the metrics
`for individual scientists is the h-index
`(3), which ranks the influence of a sci-
`entist by the number of citations to a
`significant number of his or her papers;
`an h of 100 would mean that 100 of
`Singapore Exhibit 2008
`Lassen v. Singapore et al.
`PNAS 兩 April 28, 2009 兩 vol. 106 兩 no. 17 兩 6883– 6884
`PGR2019-00053
`
`1E-mail: arf25@cam.ac.uk.
`
`P rogress in science is driven by
`
`the publication of novel ideas
`and experiments, most usually in
`peer-reviewed journals, but
`nowadays increasingly just on the inter-
`net. We all have our own ideas of which
`are the most influential journals, but is
`there a simple statistical metric of the
`influence of a journal? Most scientists
`would immediately say Impact Factor
`(IF), which is published online in
`Journal Citation Reports威 as part of
`the ISI Web of KnowledgeSM (www.
`thomsonreuters.com/products㛭services/
`scientific/Journal㛭Citation㛭Reports). The
`IF is the average number of citations in
`a year given to those papers in a journal
`published in the previous 2 years. But
`what, for example, is the most influen-
`tial of the 3 following journals: A, which
`publishes just 1 paper a year and has a
`stellar IF of 100; B, which published
`1,000,000 papers per year and has a dis-
`mal IF of 0.1 but 100,000 citations; or
`C, which publishes 5,000 papers a year
`with an IF of 10? Unless there is a very
`odd distribution of citations in B, or A
`has a paradigm-shifting paper like the
`Watson and Crick DNA structure, C is
`likely to be the most influential journal.
`Clearly neither IF nor total number of
`citations is, per se, the metric of the
`overall influence of a journal.
`Bibliometricians have introduced vari-
`ous scales of ranking journals; some
`based on publications, some based on
`usage as well, including the internet,
`using social networking analysis. Bollen
`et al. (1) recently concluded that no sin-
`gle indicator adequately measures im-
`pact and the IF is at the periphery of 39
`scales analyzed. But there is a new pa-
`rameter, the Eigenfactor™, which at-
`tempts to rate the influence of journals
`(www.eigenfactor.org). The Eigenfactor™
`ranks journals in a manner similar to that
`used by Google for ranking the impor-
`tance of Web sites in a search. To quote
`from www.eigenfactor.org/methods.htm:
`
`The Eigenfactor™ algorithm corre-
`sponds to a simple model of research
`in which readers follow chains of ci-
`tations as they move from journal to
`journal. Imagine that a researcher
`goes to the library and selects a jour-
`nal article at random. After reading
`the article, the researcher selects at
`random one of the citations from the
`
`www.pnas.org兾cgi兾doi兾10.1073兾pnas.0903307106
`
`Downloaded by guest on November 12, 2019
`
`

`

`EDITORIAL
`
`The most influential journals: Impact Factor
`and Eigenfactor
`
`Alan Fersht1
`Medical Research Council Centre for Protein Engineering, Cambridge CB2 0QH, United Kingdom
`
`P rogress in science is driven by
`
`the publication of novel ideas
`and experiments, most usually in
`peer-reviewed journals, but
`nowadays increasingly just on the inter-
`net. We all have our own ideas of which
`are the most influential journals, but is
`there a simple statistical metric of the
`influence of a journal? Most scientists
`would immediately say Impact Factor
`(IF), which is published online in
`Journal Citation Reports威 as part of
`the ISI Web of KnowledgeSM (www.
`thomsonreuters.com/products㛭services/
`scientific/Journal㛭Citation㛭Reports). The
`IF is the average number of citations in
`a year given to those papers in a journal
`published in the previous 2 years. But
`what, for example, is the most influen-
`tial of the 3 following journals: A, which
`publishes just 1 paper a year and has a
`stellar IF of 100; B, which published
`1,000,000 papers per year and has a dis-
`mal IF of 0.1 but 100,000 citations; or
`C, which publishes 5,000 papers a year
`with an IF of 10? Unless there is a very
`odd distribution of citations in B, or A
`has a paradigm-shifting paper like the
`Watson and Crick DNA structure, C is
`likely to be the most influential journal.
`Clearly neither IF nor total number of
`citations is, per se, the metric of the
`overall influence of a journal.
`Bibliometricians have introduced vari-
`ous scales of ranking journals; some
`based on publications, some based on
`usage as well, including the internet,
`using social networking analysis. Bollen
`et al. (1) recently concluded that no sin-
`gle indicator adequately measures im-
`pact and the IF is at the periphery of 39
`scales analyzed. But there is a new pa-
`rameter, the Eigenfactor™, which at-
`tempts to rate the influence of journals
`(www.eigenfactor.org). The Eigenfactor™
`ranks journals in a manner similar to that
`used by Google for ranking the impor-
`tance of Web sites in a search. To quote
`from www.eigenfactor.org/methods.htm:
`
`The Eigenfactor™ algorithm corre-
`sponds to a simple model of research
`in which readers follow chains of ci-
`tations as they move from journal to
`journal. Imagine that a researcher
`goes to the library and selects a jour-
`nal article at random. After reading
`the article, the researcher selects at
`random one of the citations from the
`
`Plot of the 2007 Eigenfactor rating against total number of citations listed in the Journal Citation
`Fig. 1.
`Reports姞.
`
`article. She then proceeds to the
`journal that was cited, reads a ran-
`dom article there, and selects a cita-
`tion to direct her to her next journal
`volume. The researcher does this ad
`infinitum.
`The Eigenfactor™ is now listed by Jour-
`nal Citation Reports威. In practice, there
`is a strong correlation between Eigen-
`factors and the total number of citations
`received by a journal (2). A plot of the
`2007 Eigenfactors for the top 200 cited
`journals against the total number of ci-
`tations shows some startling results (Fig.
`1). Three journals have by far and away
`the most overall influence on science:
`Nature, PNAS, and Science, closely fol-
`lowed by the Journal of Biological Chem-
`istry. So, publish in PNAS with the full
`knowledge that you are contributing to
`one of the most influential drivers of
`scientific progress.
`The terrible legacy of IF is that it is
`being used to evaluate scientists, rather
`than journals, which has become of
`increasing concern to many of us.
`Judgment of individuals is, of course,
`best done by in-depth analysis by ex-
`pert scholars in the subject area. But,
`some bureaucrats want a simple metric.
`My experience of being on interna-
`tional review committees is that more
`notice is taken of IF when they do not
`
`have the knowledge to evaluate the
`science independently.
`An extreme example of such behavior
`is an institute in the heart of the Euro-
`pean Union that evaluates papers from
`its staff by having a weighting factor of
`0 for all papers published in journals
`with IF ⬍5 and just a small one for 5 ⬍
`IF ⬍ 10. So, publishing in the Journal of
`Molecular Biology counts for naught,
`despite its being at the top for areas
`such as protein folding.
`All journals have a spread of cita-
`tions, and even the best have some pa-
`pers that are never cited plus some
`fraudulent papers and some excruciat-
`ingly bad ones. So, it is ludicrous to
`judge an individual paper solely on the
`IF of the journal in which it is
`published.
`Fortunately, PNAS has both a good
`IF and a high reliability because of its
`access to so many expert National Acad-
`emy of Sciences member–editors. If a
`paper has to be judged by a metric, then
`it should by the citations to it and not to
`the journal. The least evil of the metrics
`for individual scientists is the h-index
`(3), which ranks the influence of a sci-
`entist by the number of citations to a
`significant number of his or her papers;
`an h of 100 would mean that 100 of
`
`1E-mail: arf25@cam.ac.uk.
`
`www.pnas.org兾cgi兾doi兾10.1073兾pnas.0903307106
`
`PNAS 兩 April 28, 2009 兩 vol. 106 兩 no. 17 兩 6883– 6884
`
`Downloaded by guest on November 12, 2019
`
`

`

`their publications have been cited at
`least 100 times each. In terms of a ‘‘us-
`age’’ metric, Hirsch’s h-index paper (3)
`is exceptional in its number of down-
`loads (111,126 downloads versus 262
`
`citations since it was published in No-
`vember 2005).
`While new and emerging measures of
`scientific impact are developed, it is im-
`portant not to rely solely on one standard.
`
`After all, science is about progress, which
`is ultimately assessed by human judgment.
`
`ACKNOWLEDGMENTS. I thank Philip Davis for
`pointing me toward the relevant literature.
`
`1. Bollen J, Van de Sempel H, Hagberg E (2009) A principal
`component analysis of 39 scientific impact measures.
`e-Print Archive, http://xxx.lanl.gov/abs/0902.2183.
`
`2. Davis PM (2008) Eigenfactor: Does the principle of re-
`peated improvement result in better estimates than raw
`citation counts? J Am Soc Info Sci Tech 59:2186–2188.
`
`3. Hirsch JE (2005) An index to quantify an individual’s
`scientific research output. Proc Natl Acad Sci USA
`102:16569 –16572.
`
`6884 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0903307106
`
`Fersht
`
`Downloaded by guest on November 12, 2019
`
`