Why make it reproducible?

One of the tenets of science is to be reproducible.

But if we always did what we’re supposed to, the world would be a better and easier place. However, there are benefits to making science reproducible that directly benefit researchers, especially in computational science and machine learning. These benefits are like the title says:

• More citations

• Easier review cycles

• Better collaborations

But it goes further. Reproducibility is a marketable skill outside of academia. When we work in companies that apply data science or machine learning, these companies know that technical debt can slowly degrade a code base and in some cases like Amazon and Google, the machine learning system has to be so reproducible that we expect the entire training and deployment to work automatically on a press of a button. Technical debt is also a problem in academia, but here it is more framed in the devastating prospect of the only postdoc leaving that knows how to operate the code base.

Luckily, we have a lot of work cut out for us already!

These benefits, and a few others, like making iteration, and therefore frequent publication easier, do not come at a proportional cost. Most of the methods to increase code quality in machine learning projects of applied scientists are in fact fairly easy to set up and run!

So how do we actually go about obtaining these goals?