My claim is that people systematically underestimate the value of good code to a research program. Good infrastructure lets the lab focus on the scientific questions, rather than the logistics of moving and processing the data, which in turn allows them to publish more, better, and faster. This is true for a lot of things: some labs have fantastically good imaging pipelines, or have worked out how to rapidly train animals for certain behaviors, or can reliably do an assay that often fails in others’ hands, and derive a huge benefit from it. Some are so good that I wouldn’t even consider competing with them in their niche. My argument is that good code can also have returns like that.

As a personal example, my first paper at McGill took about three years to finish. The next took about a year and a half (and just came out). We’re on track to submit at least one—-and maybe as many as three—-papers this year. Some of this is due to practice, but a lot of it is due to the fact that we built reusable components instead of “the script that gives the numbers”