A simple way to improve sustainability, reproducibility and releasability of your code and data
This is a first of a number of posts based upon discussions I had while at the Collaborations Workshop 2013 (#CollabW13 on twitter) in Oxford, UK. During one of the sessions I described a simple technique that I try and use to increase the sustainability, reproducibility and releasability of code that I write, data I collect and the results of my work – and people thought this idea was great, and that I should blog about it…
So, what is this wonderful technique:
On Friday afternoon (when you’re bored and can’t be bothered to do any hard work…) spend an hour or two cleaning up and documenting your work from that week
It’s a very simple idea, but it really does work – and it also gives you something to do during the last afternoon of the week when you’re feeling tired and can’t really be bothered. If you can’t commit to doing it every week, you can try every fortnight or month – and I even go as far as adding it as an event to my calendar, to try and stop people arranging meetings with me then!
So, what sort of things can you do during this time?
- Document your code:Â Depending on the project and the intended use of the documentation, this can be anything from adding some better comments to your code, to documenting individual functions/methods (for example, using docstrings in Python) or writing high-level documentation of the whole system.
- Refactor your code:Â Refactoring is a “disciplined technique for restructuring an existing body of code, altering its internal structure without changing its external behaviour” – that is, basically tidying up, cleaning up, and possibly redesigning you’re code. If you’re anything like me, the code you write when actuallyÂ doing science isn’t very neat, nice or well-designed – because you’re focused on the science at that point. These few hours on a Friday are your time toÂ focus on the code for a bit…
- Generalise your code to create a library:Â There are probably a number of things in your code that could be used in many other programs – things like reading certain file formats, performing statistical operations or applying algorithms to array data. This is a perfect time to take these algorithms and ‘decouple’ them from their immediate application in this code so that they can be easily used in other programs you may write in the future. Ideally, this generalised functionality can be packaged into a library and distributed for others to use. My Py6S library was created in this way: I took code that I was using to run the 6S model, generalised it into a library, documented it well, released it – and it has taken on a life of its own!
- Add README files to your folders:Â If you’re anything like me, you’ll have loads of folders containing data and code – some of which isn’t particularly well named and may not have metadata. One of the easiest (and most effective) ways to deal with this is to create simple README files in each folder explaining what is in the folder, where it came from, what format it’s in – basically anything that you think you’ll want to know about it in a year’s time if you come back to it. I can say from experience just how useful having these README files is!
The key benefit of all of these is that it makes itÂ so much easier to come back to your research later on, and it also makes it so much easier for you to share your research, make it reproducible and allow others to build upon it – and the great thing is that it doesn’t even take that much work. Thirty seconds writing a few notes in a README file could easily save you a week of work in a year’s time, and extending your code into a library would allow you to re-use it in other projects without much extra work.
Another similar idea that was mentioned by someone else at the Collaborations Workshop was for Research Councils to force people to add extra time to the end of their grants to do this sort of thing – although personally, I think it is a far better idea to do this as you go along. Trying to add documentation to some code that you wrote two years ago is often quite challenging…
So, there it is – a simple way to use up some time at the end of the week (when you can’t really be bothered to do anything ‘new’) which will significantly improve the sustainability, reproducibility and releasability of your code and data. Try it out, and let us know how you do in the comments below!