My five cents is, that it really depends on the characteristics of your data (e.g. size) and the goal you try to achieve by versioning your data.
+1 to thinking carefully about what your goals are here before jumping to any particular tool.
My experience: I found myself re-organizing all my lab's neuroimaging data starting from data collected when it was a single grad student up to when it was housing data from multiple studies and multiple sites of data collection. We opted to begin by first organizing the data with sensible naming scheme on a shared drive, as Lars describes, because it was immediately accessible to everyone in the lab regardless of their tech know-how, and was also a necessary starting point regardless of whether we later adopted a fancer data versioning/sharing technology. We did later use a neuroimaging-specific system for sharing our data with others, but retained the filesystem organization in addition because it was familiar, and so darn convenient for scripting, documentation, etc.
Jon. On 07/23/18, [email protected] wrote:
Hi, My five cents is, that it really depends on the characteristics of your data (e.g. size) and the goal you try to achieve by versioning your data. Examples: Size: If e.g. the datasets are "small", they can easily be handled by git. For larger datasets, it depends on what is important to you. E.g. a shared network file system with proper backup and well-defined naming scheme can be totally fine in some cases, while a proper data repository issuing DOIs or similar is needed in other cases. If synchronization speed, as well as optimized storage, is important, something like dat or IPFS is advisable. Purpose: Similarly, if your goal is to share data with collaborators, then a simple HTTPS link is the easiest (hosted on e.g. GitHub, AWS, or a data repository). Cheers, Lars
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