Publishing or perishing

The single most important metric by which an academic is judged is their peer-reviewed publication record. Promotion, grant applications, and finding new jobs all depends on having a strong publication record. This has long been described as "Publish or perish", because if you don't publish, you perish - either you don't advance in your job, you can't find a job, or you don't keep your job. Now, I've got a reasonably long publication record, but I'm always looking for ways of boosting my research output (see my previous posts on publishing in computational intelligence).

Several years ago, biologist Phil Clapham published an excellent essay on the need for academics to publish their research. One of his rules, that I am applying to my own work, is to have at least one paper under review at all times. Now, this can be pretty hard work - there is always variation in the amount of time that papers spend in review, and reviewer comments can take a long time to address. But, it does lead to building up your publication record quite quickly.

One outcome of this rule, though, is that one should also be writing at least one paper at any given time, while also generating sufficient publishable results for at least one paper at any given time. That is, while at least one paper is in review, you need to be writing at least one paper, and also writing code / designing experiments / performing analyses to go into at least one other paper. So, publishing is like being on a treadmill: as soon as you submit one paper, you need to get to work on getting the next submitted, while lining up the material for the one after that.

While this does encourage the practice of breaking research projects into small, easily published (and easily understood) chunks, I suspect it may also encourage further proliferation of single publon papers. Whether or not this is a bad thing, I've leave to you to decide.

Another way to boost your publication count is to collaborate. A lot. Computational intelligence is a particularly useful field to come from for collaborating, as the algorithms we study can be applied to so many problems. But that's all a topic for another post.