Keeping up to date with bibliometrics: the latest functions on Journal Citation Reports (InCites)

I recently registered for a recent free, live, online training session on the latest functions of Journal Citation Reports (JCR) on InCites, from Thomson Reuters (TR). I got called away during the session, but the great thing is that they e-mail you a copy so you can catch up later. You can’t ask questions, but at least you don’t miss out entirely! If you want to take part in a session yourself, then take a look at the Web of Science training page. Or just read here to find out what I picked up and reflected on.

At the very end of the session, we learnt that 39 journal titles have been supressed in the latest edition. I mention it first because I think it is fascinating to see how journals go in and out of the JCR collection, since having a JCR impact factor at all is sometimes seen as a sign of quality. These supressed titles are suspended and their editors are informed why, but it is apparently because of either a high self-cite rate, or something called “stacking”, whereby two journals are found to be citing each other in such a way that they significantly influence the latest impact factor calculations. Journals can come out of suspension, and indeed new journals are also added to JCR from year to year. Here are the details of the JCR selection process.

The training session began with a look at Web of Science: they’ve made it easier to see JCR data when you’re looking at the results of a Web of Science search, by clicking on the journal title: it’s good to see this link between TR products.

Within JCR, I like the visualisation that you get when you choose a subject category to explore: this tells you how many journals are in that category and you can tell the high impact factor journals because they have larger circles on the visualisation. What I particularly like though, is the lines joining the journals: the thicker the line, the stronger the citing relationship between the journals joined by that line.

It is the librarian in me that likes to see that visualisation: you can see how you might get demand for journals that cite each other, and thus get clues about how to manage your collection. The journal profile data that you can explore in detail for an individual journal (or compare journal titles) must also be interesting to anyone managing a journal, or indeed to authors considering submitting to a journal. You can look at a journal’s performance over time and ask yourself “is it on the way up?” You can get similar graphs on SJR, of course, based on Elsevier’s Scopus data and available for free, but there are not quite so many different scores on SJR as on JCR.

On JCR, for each journal there are new “indicators”, or measures/scores/metrics that you can explore. I counted 13 different types of scores. You can also explore more of the data behind the indicators presented than you used to be able to on JCR.

One of the new indicators is the “JIF percentile”. This is apparently introduced because the quartile information is not granular or meaningful enough: there could be lots of journals in the same quartile for that subject category. I liked the normalised Eigenfactor score in the sense that the number has meaning at first glance: higher than 1 means higher than average, which is more meaningful than a standard impact factor (IF). (The Eigenfactor is based on JCR data but not calculated by TR. You can find out more about it at Eigenfactor.org, where you can also explore slightly older data and different scores, for free.)

If you want to explore more about JCR without signing up for a training session, then you could explore their short video tutorials and you can read more about the updates in the JCR Help file.

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