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alternatively, scientific results should be robust enough to still be observable with marginally different software versions

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Not sure if that’s possible in practice. Every analysis would have to be written using several different software stacks... It’s probably doable only if the software is fairly simple or if someone has a lot if extra funding to burn through.

Key word there is marginally. If your finding holds up using NumPy 1.10 but not 1.11... you do not have a finding.

That's probably true for numpy 1.10-1.11, but I"ve seen other libraries make dramatic changes (for example changing the default behavior of a function)

http://pandas.pydata.org/pandas-docs/version/0.17.0/generate...

pandas changed NaN handling for ewma pre 0.15, which can greatly change the output if your data is sparse

But again, if there are NaN's cropping up in your analysis, and your results depend on what an intermediate tool decides to do with them, you have overlooked something substantial.

THIS

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