Dr. Heidi Seibold

Setting up a FAIR and reproducible project

Published 3 months agoย โ€ขย 2 min read

Making your research or code project FAIR (Findable, Accessible, Interoperable, Reusable) and reproducible can feel like a chore. But if you have access to the right templates and resources, it can be quite the simple and rewarding task. Let's make it easy for ourselves to do the right thing!

So today let me share some templates for setting up #FAIR and #reproducible projects. I asked folks on social media what suggestions they had and got so many that I was not able to put them all here without making everyone's head explode ;). Please do follow the respective social media links if the links here are not enough for you (especially Mastodon). Thanks to everyone who contributed to this list! ๐Ÿซถ


Please note that the following research project templates generally assume that you work with data and code. The templates for code projects further down focus on research software projects.

๐Ÿ—ƒ๏ธ Research projects

๐Ÿ“ฆ Code projects


Setting up a FAIR and reproducible project repository

There are a couple of building blocks that you will see in (almost) all templates above:

  • A README describing the project
  • A coherent folder structure
  • Names for files and folders that are (1) human readable, (2) computer readable, and (3) consistent

One more thing I want to mention: Since we are talking about project standards, check out the YODA principles which are a set of organizational standards for datasets used for data analysis projects.

Did you find this post useful? Please help me spread the word by forwarding it to your colleagues and friends or by sharing the newsletter website link.

Today's post is probably the richest post I've shared so far. Full of super useful templates. I hope that makes up for the past weeks of silence from my end. I was very busy and in the upcoming weeks will share with you what has kept me from writing newsletter posts. I promise: it was worth it!

All the best,


P.S. If you're enjoying this newsletter, please consider supporting my work by leaving a tip.

Heidi Seibold, MUCBOOK Clubhouse, Elsenheimerstr. 48, Munich, 81375
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Dr. Heidi Seibold

All things open and reproducible data science.

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