When we browse literature on first-principle study, we see they usually report the methodology, used program with the version number and the results. In some cases, people share their input files, intermediate data files, workflow and even custom scripts (for example, see Giulia Galli's research group's publications here). However, this is not always the case and in some cases, authors cannot provide the data after 5-10 years of publication due to the data being lost. Now, in order to ensure reproducibility (and to back up our results when confronted/asked), which data files should we store after we have done a calculation?
I am doing DFT calculations using Quantum ESPRESSO code. Aside from the input files, versions, scripts, workflows, which data is important to store? QE (and to the best of my knowledge, any other DFT codes too) produce charge density files, and wavefunctions. So far I am backing up all the data in an external drive. But the wavefunctions are really large and often a publication (which might have 100s of DFT calculation) costs several TB of storage. If we delete them and then someone claims our results are wrong or we find something wrong in our post-processing scripts, then we have to rerun the calculations, generate the wavefunctions from the scratch and then do the post-processing. The positive aspect of storing all the data is that in case of post-processing, it is readily doable (band structure, dos, charge density analysis, etc.) and no calculation needs to be repeated.
So, I am conflicted about what to store. My current strategy is to store everything except the wavefunctions for all the calculations (very small size) and only the SCF wavefunctions which is produced inside the outdir/PREFIX.save/
directory in the format wfcup**.hdf5
or wfcdw**.hdf5
where **
ranges over the number of k-points. I am asking this question to know from other researchers who are publishing in the field of DFT calculation. Which files are you storing forever?
/nearline
storage, which is meant for "archiving" the data for long-term storage (this is different from/project
storage which is for fast I/O and fast calculations, so it is more expensive and one could have, for example, 100TB of/project
space for every300 GB
of/nearline
space). $\endgroup$