New paper: “Top Tips to Make Your Research Irreproducible”

It is an unfortunate convention of science that research should pretend to be reproducible; we have noticed (and contributed to) a number of manifestos, guides and top tips on how to make research reproducible, but we have seen very little published on how to make research irreproducible.

Irreproducibility is the default setting for all of science, and irreproducible research is particularly common across the computational sciences (for example, here and here). The study of making your work irreproducible without reviewers complaining is a much neglected area; we feel therefore that by encapsulating our top tips on irreproducibility, we will be filling a much-needed gap in the domain literature. By following our tips, you can ensure that if your work is wrong, nobody will be able to check it; if it is correct, you can make everyone else do disproportionately more work than you to build upon it. Our top tips will also help you salve the conscience of certain reviewers still bound by the fussy conventionality of reproducibility, enabling them to enthusiastically recommend acceptance of your irreproducible work. In either case you are the beneficiary.

  1. Think “Big Picture”. People are interested in the science, not the experimental setup, so don’t describe it.
  2. Be abstract. Pseudo-code is a great way of communicating ideas quickly and clearly while giving readers no chance to understand the subtle implementation details that actually make it work.
  3. Short and sweet. Any limitations of your methods or proofs will be obvious to the careful reader, so there is no need to waste space on making them explicit.
  4. The deficit model. You’re the expert in the domain, only you can define what algorithms and data to run experiments with.
  5. Don’t share. Doing so only makes it easier for other people to scoop your research ideas, understand how your code actually works instead of why you say it does, or worst of all to understand that your code doesn’t work at all.

Read the full version of our high-impact paper on arXiv.

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