By Daniel S. Katz, Assistant Director for Scientific Software and Applications at NCSA
Reposted with the author's permission. This article was originally published in Daniel S. Katz's blog.
This blog is based on part of a talk I gave in January 2017, and the thinking behind it, in turn, is based on my view of a series of recent talks and blogs, and how they might be fit together. The short summary is that general software reproducibly is hard at best, and may not be practical except in special cases.
Software reproducibility here means the ability for someone to replicate a computational experiment that was done by someone else, using the same software and data, and then to be able to change part of it (the software and/or the data) to better understand the experiment and its bounds.
I’m borrowing from Carole Goble (slide 12), who defines:
- Repeat: the same lab runs the same experiment with the same set up
- Replicate: an independent lab runs the same experiment with the same set up
- Reproduce: an independent lab varies the…