Fear of missing out

There are so many tools, techniques, communication platforms and methods out there for ecologists that it can be hard to keep track of them all. Whether it be LaTeX, Linux, or Twitter, someone has probably suggested that your research life will improve if you use their favourite tool.

Online science has allowed fantastic networking and information exchange. However, one side effect of the increased usage of social media can be the feeling that you can’t keep up. The phrase ‘Fear of Missing Out’ in social psychology was coined to describe the anxiety arising from looking at social media and fearing that you have chosen how to spend our time poorly. But this can extend to academic working life too – that nagging fear that your colleagues are doing cooler things (going to more conferences, using more cutting edge techniques, publishing more papers) than you. It can be disconcerting to see that there’s a cool R package you know nothing about, or to receive yet another invite to a work-based social media platform you’ve never heard of.

There are people who are the early adopters and the technological trendsetters; people who derive their success from their methodological/technological skill sets. These people are fantastic, since they provide the examples for others in the field to follow. But for most researchers, it is practical to recognize that knowing every new technique and tool doesn’t promise success.

Graduate students may suffer this fear of falling behind most intensely, given how closely it relates to imposter syndrome and the general skills gap that grad students have to overcome. But I’d argue for most students, that balance is key. Otherwise it can be a slippery slope: maybe you learn R, then find that people recommend knitR and R Markdown, then you learn Shiny to show your results beautifully, then someone suggests things would be faster in a lower level language – and maybe months have gone by. That’s not necessarily bad, intellectual freedom and self-teaching are some of the best parts of graduate school, and if you are going to learn a bunch of new techniques, that is the perfect time. But aficionados will make arguments for everything from Python, Linux, Emacs (apparently the one true operating system), Github, to LaTeX/BibTeX, etc, etc. And every one of these is a useful tool, but maybe not always a necessary tool.

Researchers can never learn everything, and some things will fall out of fashion as quickly as they arose. Sure, it is less than ideal to be a researcher who hasn’t learned a new approach since the advent of SAS. But we need to balance necessity (is it fundamental to my research?), the time investment, and the utility of these skills for future endeavours (e.g. if you are applying for a data analyst job after grad school, learning a few languages might be smart - but if you will rarely use it again, perhaps it is not so important). The danger for many students is that either the time investment in learning a less-than-fundamental tool is very high, or else they learn a number of tools but never master any of them. This isn't to say you can shirk on quantitative skills - on the contrary, having those skills is far more important than mastering any specific program or language. I can think of fantastic ecologists who don’t know what Twitter is, who still use SAS, who have never learned R, and who produce brilliant work. The questions, the ideas, and the knowledge matter too, after all.

(*I should note that I'm a quantitative/theoretical ecologist, and I think every ecologist should have a strong quantitative education! Just that you can do that using lots of different methods and doing it well should always be the first priority.)