(This article was first published on

I was recently introduced to the concept of the h-index and was compelled to find out my own h-index via Scopus. Numbers don't matter, but discussion with my colleagues turned to the issue of author position. We quickly decided that there are three important "positions" in the list of authors for a publication: first, last and everywhere else (let's call it the middle). Three-dimensional plots are never easy to deal with, but the concept of the triangle plot has always stuck in the back of my mind. Armed with this fine plotting mechanism, and the R language, I decided to attack the output from Scopus.**Rigorous Analytics**, and kindly contributed to R-bloggers)First things first... here is code to parse the .csv output provided by Scopus and calculate the h-index with a simple plot.

Now to deal with the authorship position we first parse the author list, determine where the author of interest is located and then convert the three categories into percentages. I have neglected to include the code that explicitly constructs the matrix A. Just let A = rbind(A, triax.data) to build it up for each author.

I happen to be the

**red**dot in the triangle plot, with four of my colleagues also represented. With respect to journals in the field of medical imaging and the like, one would want to have as many first or last author positions as possible and fewer middle positions. First means you did all the work, last means you're the most senior author and (let's face it) nobody cares if you're anywhere else.

So the question is, where do you sit?

To

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