Code Review Culture in Research Labs
Cats can do science, too! Source: Volodymyr Dobrovolskyy on Unsplash
Code review in research settings is a classic Goldilocks problem: too little and you risk irreproducible science, too much and you kill discovery momentum.
I’ve experienced this challenge across teams of all sizes, and the solutions are surprisingly different:
➡️ The Solo Bioinformatician Dilemma: You’re embedded in a wetlab team. Who’s your peer? The postdoc who knows R (while you code in Python)? The PI who coded in FORTRAN 20 years ago?
Honestly, I’m still figuring this one out. Maybe external code review partnerships? Monthly virtual code clubs? I’d love to hear how the community solves this.
➡️ The stretched small team (3 people): Everyone’s on different projects, everyone’s oversubscribed. Code review feels like a luxury you can’t afford. We tried it. It failed. The reality? When you’re the only person who understands both the biology AND the pipeline, peer review becomes performative rather than protective.
➡️ The sweet spot (5-6 people): This was where peer review actually worked—but only because management explicitly protected our time for it. Key insight: leadership has to VALUE code review, not just require it.
We established “review debt” as a real metric. If your reviews were backlogged, you couldn’t start new features. Sounds harsh, but it worked.
Here’s what I learned: code review culture isn’t just about catching bugs. It’s about knowledge sharing, preventing single points of failure, and building team standards.
But in research, speed often beats perfection. The trick is finding review practices that ADD velocity instead of killing it.
Maybe we need research-specific review standards? Maybe pair programming works better than async reviews? Maybe some analyses deserve different review rigor than production pipelines?
What’s your experience with code review in research settings? How do you balance rigor with discovery speed? And solo bioinformaticians—how do you handle this challenge?