What Can Article-Level Metrics Do for You?
Yesterday PLOS Biology published an essay by me: What Can Article Level Metrics Do for You? (Fenner, 2013). I had help from many others in writing the essay, in particular PLOS Biology editor Emma Ganley. I hope that the essay can help researchers get introduced to article-level metrics, and I am honored that the essay is part of the PLOS Biology 10th anniversary collection.
The essay is an Open Access article published under a CC-BY license, so not only can everyone read it, but the text and figures can be freely reused, as long as proper attribution is provided, e.g. Fig. 5:
Although this is an essay and not a research article, I’ve added the data and R scripts used to generate the figures (1, 3-5) as supporting information. As I have said earlier, I think it is important that an article contains more than the text. With the open source software R or RStudio, everyone can recreate the figures, and can look at the data underlying the figures in the essay. One can for example look into the data behind Fig. 5 to better understand how articles with F1000Prime recommendations and Wikipedia mentions differ from those only recommended in F1000Prime. Feel free to ask for help getting started in the comments.
Incidentally this is also my first PLOS article (my wife is way ahead of me with 5 research articles), so that I can finally look at PLOS article-level metrics as an author - after being the technical lead for this project since May 2012.
Fenner, M. (2013). What can article-level metrics do for you? PLoS Biol, 11(10), e1001687. doi:10.1371/journal.pbio.1001687