How do I interpret a journal’s Eigenfactor score?
A journal’s Eigenfactor score is our measure of the journal’s total importance to the scientific community.
With all else equal, a journal’s Eigenfactor score doubles when it doubles in size. Thus a very large journal such as the Journal of Biological Chemistry which publishes more than 6,000 articles annually, will have extremely high Eigenfactor scores simply based upon its size.
Eigenfactor scores are scaled so that the sum of the Eigenfactor scores of all journals listed in Thomson’s Journal Citation Reports (JCR) is 100. In 2006, the journal Nature has the highest Eigenfactor score, with a score of 1.992. The top thousand journals, as ranked by Eigenfactor score, all have Eigenfactor scores above 0.01.
+ How do I interpret a journal’s Article Influence score?
A journal’s Article Influence score is a measure of the average influence of each of its articles over the first five years after publication.
Article Influence score measures the average influence, per article, of the papers in a journal. As such, it is comparable to Thomson Scientific’s widely-used Impact Factor. Article Influence scores are normalized so that the mean article in the entire Thomson Journal Citation Reports (JCR) database has an article influence of 1.00.
In 2006, the top journal by Article Influence score is Annual Reviews of Immunology, with an article influence of 27.454. This means that the average article in that journal has twenty seven times the influence of the mean journal in the JCR.
+ More Info via the FAQ (Important Reading) and Why eigenfactor?
What’s Available on the Site?
+ eigenfactor Search (Basic and Advanced)
++ Journal Cost-Effectiveness Search (Select eigenfactor Subject and JCR Subject Category)
Note: Data back to 1995 is available.
More After the Jump
+ Maps of Science
++ Philosophy and Method of Maps
++ eigenvector Methodology
++ Data Set Statistics
+ Various eigenfactor Visualizations
The Eigenfactor Project™ is a non-commercial academic research project sponsored by the Bergstrom lab in the Department of Biology at the University of Washington. We aim to use recent advances in network analysis and information theory to develop novel methods for evaluating the influence of scholarly periodicals and for mapping the structure of academic research. We are committed to sharing our findings with interested members of the public, including librarians, journal editors, publishers, and authors of scholarly articles. The Eigenfactor Project can be found on the web at Eigenfactor.org.
Source: eigenfactor.org
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