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Reproducibility is about understanding the result. It is the modern version of "showing your work". One of the unsung and wonderful properties of reproducible
by ISL 6y ago
Reproducibility is about understanding the result. It is the modern version of "showing your work".
One of the unsung and wonderful properties of reproducible workflows is the fact that it can allow science to be salvaged from an analysis that contains an error. If I had made an error in my thesis data analysis (and I did, pre-graduation), the error can be corrected and the analysis re-run. This works even if the authors are dead (which I am not :) ).
Reproducibility abstracts the analysis from data in a rigorous (and hopefully in the future, sustainable) fashion.
- konjin 6y ago>Reproducibility is about understanding the result. It is the modern version of "showing your work". That is something no one outside of highschool cares about. The idea that you can show work in general is ridiculous. Do I need to write a few hundred pages of set theory to start using addition in a physics paper? No. The work you need to show is the work a specialist in the field would find new, which is completely different to what a layman would find new. Every large lab, the ones that can actually reproduce results, has decades of specialist code that does not interface with anything outside the lab. Providing the source code is then as useful as giving a binary print out of an executable for an OS you've never seen before.
- ISL 6y agoI heartily disagree -- reproducible analysis is essential for the intercomparison of analyses between specialist research groups. Here is my thesis work, minus the data, which is too large to store within a GitHub repo. By calling `make`, it goes from raw data to final document in a single shot. The entire workflow, warts and all, can be audited. If you see a bug or concern, please let me know: https://github.com/4kbt/PlateWash https://github.com/4kbt/PlateWash