Freely accessible at http://www.eigenfactor.org.
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.
The Eigenfactor project evaluates journals of science and social science using two indicators the Eigenfactor Score and the Article Influence Score, which are based on PageRank algorithm. The Eigenfactor score and the Article Influence score is calculated based on the citations received over a five year period. The citation data is from Thomson Scientific's Journal Citation Reports (JCR).
EF aims 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.
Scholarly references join journals together in a vast network of citations. The Eigenfactor algorithm uses the structure of the entire network to evaluate the importance and influence of each journal. A journal is considered to be influential if it is cited often by other influential journals. The algorithm used accounts for the fact that a single citation from a high-quality journal may be more valuable than multiple citations from peripheral publications. By using the whole citation network, the Eigenfactor algorithm automatically accounts for citation differences among different fields and allows better comparison across research areas.
The Eigenfactor project evaluates journals of science and social science using two indicators the Eigenfactor Score and the Article Influence Score, which are based on PageRank algorithm. The Eigenfactor score and the Article Influence score is calculated based on the citations received over a five year period. The citation data used at Eigenfactor come from Thomson Scientific's Journal Citation Reports (JCR).
A journal's Eigenfactor score is a measure of the journal's total importance to the scientific community. Eigenfactor scores are scaled so that the sum of the Eigenfactor scores of all journals listed in Thomson Reutres's Journal Citation Reports (JCR) is 100. Eigenfactor are dependant on journal size, thus a very large journal which publishes thousands of articles annually, will have an extremely high Eigenfactor 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.
For further details of indicators see Citation metrics.
The Eigenfactor project has developed a specific procedure for mapping science. The Eigenfactor web site also allows searching by Thomson Reuters's category from the advanced search page. The Eigenfactor categories form a hard partition in which each journal belongs to only one category, whereas the Thomson categories form a soft partition in which journals are allowed multiple category membership. The Eigenfactor project aims to mapping science according to what researchers do, and not to use the preconceived notions about what the structure of clusters or fields within science should be.
In collaboration with journalprices.com , the Eigenfactor web site provides information about price and value for thousands of scholarly periodicals. While the Eigenfactor Scores and Article Influence Scores do not incorporate price information directly, the cost-eEffectiveness search orders journals by a measure of the value per dollar that they provide.
EF motion charts
EF advanced search
Mapping science -page contains lists of top 10 journals in the field
For reading: Rosval, M. & Bergstrom, C.T. (2008). Maps of random walks on complex networks reveal community structure.
Map illustrates medicine and related sciences. The page also includes a top ten list of journals.
Source: Eigenfactor <http://www.eigenfactor.org> 15.9.2009.