If these data are an important piece of your computational workflow it
might make sense to archive & describe them in an actual data repository
(Dryad, figshare, OSF, etc.) and pull them / cache locally when you run
your calibration.

--
Allen Lee
Associate Research Professional
Center for Behavior, Institutions, and the Environment <http://cbie.asu.edu>
Network for Computational Modeling in the Social and Ecological Sciences
<http://comses.net>
Arizona State University
Mail Code: 4804
Tempe, AZ 85287
*p: *480-727-4646
*email: *[email protected]
*web: *https://github.com/alee


On Fri, Jul 20, 2018 at 2:48 PM thompson.m.j via discuss <
[email protected]> wrote:

> Hello all,
> I am a member of a computational biology lab that models processes in
> developmental biology and cell signaling and calibrates these models with
> microscopy data. I've recently gotten into using version control using git
> for our codes, and I am now trying to determine the best course of action
> to take for the data. These are the tools I'm aware of but have not tested:
>
> The Dat Project https://datproject.org/
> Git Large File Storage https://git-lfs.github.com/
> Git Annex https://git-annex.branchable.com/
> Data Version Control (DVC) https://dvc.org/
>
> All projects seem to be aimed at researchers trying to integrate data
> versioning into their workflow and collaboration, and some seem to have a
> few other bells and whistles.
>
> Now, the only reason I settled on using git for my work is that it seems
> to be the de facto standard version control just about the whole world
> uses. Using this same reasoning, does anyone here have a keen insight into
> which of the data versioning tools listed here or otherwise is (or will
> most likely become) the standard for data version control?
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>

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