The Bergstrom lab’s Eigenfactors site has just come out of preview mode and you can now query the whole dataset.

Eigenfactors are a novel alternative to journal impact factors. They’re calculated using random walks through a network of citations:

The Eigenfactor algorithm corresponds to a simple model of research in which readers follow chains of citations as they move from journal to journal. Imagine that a researcher goes to the library and selects a journal article at random. After reading the article, the researcher selects at random one of the citations from the article. She then proceeds to the journal that was cited, reads a random article there, and selects a citation to direct her to her next journal volume. The researcher does this ad infinitum.

The amount of time that the researcher spends with each journal gives us a measure of that journal’s importance within network of academic citations.

One advantage of this approach is that it can handle the fact that different disciplines have different citation patterns:

The average article in a leading cell biology journal might receive 10-30 citations within two years; the average article in leading mathematics journal would do very well to receive 2 citations over the same period.

The list of the top 10 journals by eigenfactor looks pretty much as you’d expect – Nature and Science are sitting pretty at the top, natch.

One issue: in an attempt to include more material from the social sciences the eigenfactor dataset includes articles from newspapers and popular magazines. As newspaper articles don’t typically have reference lists attached I’m not sure how they are incorporated into the network, but in any case don’t they skew eigenfactors towards those journals that have the best press releases? Could I start a Journal of Sensational Medicine and start publishing pseudo-scientific quackery, be spurned by academia but have a high eigenfactor simply because I feed the London Lite headlines?

(via Three Toed Sloth)