This week in learning to be an open scientist | Practical Data Management for Bug Counters

" ... Given there is a possibility that I may start my own lab someday, I’m trying very hard to nail my own way of doing things down to ensure I can start things out using the best practices possible. In conversations with senior scientists, one of the things I find that always comes up is tackling inertia to change practices. Many people are open science advocates in principle, I find- but once the infrastructure to do things in a certain way is there, it’s much harder to change. A good example of this is the SAS vs R thing*: both packages come with steep learning curves, and for the casual stats user**, let’s be honest, both can do the job equally well. R is preferred by the open science community for a number of reasons- not the least of which is it’s free. But if you’ve got money for a SAS license, it’s already installed on your computer, you feel reasonably competent at scripting what you need to script in SAS, you’ve got a colleague who teaches the “Intro SAS for Grad students in organismal biology” in your department, even if you have the best intentions, there is a heck of a lot of infrastructural inertia to overcome to try something new (and often, if someones insists you should do [task x] in R instead, you may regard them as an annoying time waster). This is why I feel it’s very important for me to be thoughtful about building my science house’s foundation on open practice ..."