Nothing feels cleaner than storing everything (notebook, raw data, cleansed data, misc scripts, etc.) in a docker image when you're finished with the project. Data science and docker are meant to be besties.
Docker could be that stable build process but it requires the additional assertion that there wasn't, say, a truckload of changes made using `docker exec` or a bunch of customizations to files which were copied into the image. Simply putting a note on the source repo which says that might be enough.
(I really like what C Titus Brown has written about reproducibility in computation research over the years: http://ivory.idyll.org/blog/tag/reproducibility.html)
There needs to be an immutable, high performance read data store that has a 30+ year plan for survival if we're really going to retool our world around expert systems.
Our use of data has grown so much faster than our network capacity (and indeed, it seems like we're going to hit a series of physical laws and practical engineering constraints here). "Data has gravity" but the only way to "sustainable" hold a non-trivial volume of data for 20 years right now is to run a data center with a big dht that detects faults and replicates data.
http://www.newyorker.com/tech/elements/voyager-golden-record...
A lack of reproducibility is a major problem for DSEs and practitioners right now. In fact, I'd argue its the single biggest problem.