Publication metrics and success on the academic job market
David van Dijk, Ohad Manor, Lucas B. Carey
Current Biology 2014 Vol 24 No 11 R516
I caught this paper in a TOC email from Current Biology. “Predict who becomes a PI” they say. “Hmm,” I think, “This should be interesting.”
The abstract sets out the problem simply: “so far there has been no quantitative analysis of who becomes a principal investigator (PI). We here use a machine-learning approach to predict who becomes a PI, based on data from over 25,000 scientists in PubMed. We show that success in academia is predictable.”
One of the reasons I didn’t immediately carry on from my PhD into a research career was because of my understanding of the job prospects. It seemed a very insecure, stressful, badly rewarded, and poorly balanced work-life choice of career path. It is definitely a labour of love. Seeing my friends from university earn megabucks in the City grates after a while of doing qPCR. So, having got to the end of my first stint in research, I went off to see what else is out there, and ended up doing two very different, but very interesting jobs in science communication.
But that doesn’t mean I’m shutting the door on a research career; I would like to try again, perhaps with a new topic, or a new location. I can now see, having spent some time in the ‘real world’, that there are definite benefits to an academic career: you get to do something you’re interested in every day; it’s flexible – if you work well 11am – 8pm, then do 11am – 8pm; you can travel all over the world, work in so many different environments and meet so many different people. Sure, some of those people are a bit quirky, but it’s truly a global career.
So, when a paper suggests that your career path is predictable, I had to have a go myself.
In the paper they calculate a number of different metrics which correlate well with becoming a PI. As expected, publication record is highly important: authors with more first author papers, and with more papers in “high impact factor journals”, have a greater probability of becoming PIs. Impact factor ranks the most predictive, above number of publications, gender and the number of times your papers are cited relative to the impact factor of the journal they are in. But you can get around this: publish twice as many first author papers in lower impact journals relative to someone who publishes in higher impact journals, and you’ll have a good chance.
The calculation isn’t perfect: the data set includes all authors on a paper – including technicians and those who may never consider being a PI. It doesn’t even differentiate between authors with a PhD versus those without. This is most stark in the statistic (Figure 1A) of ‘authors who become PIs’ of 6.2%. This is a small number! Other surveys aren’t much more optimistic, but a 2009 NSF survey suggests that around 14 percent of those with a PhD in biology and the life sciences land a coveted academic position (within five years).
So, what is my probability? 50%, right in the middle. Does that swing my thoughts about whether I pursue re-entry into academia? A little, but it also says to me that if I want it then perhaps I can push that chance to 51%, and be more likely than not. Maybe I’ll have to lean a little harder on my ‘outside academia’ skills and experience, but I think it’s worth a gamble.
– Read this blog for another take on academic job prospects and the attitude problem of academia.