Exporting data to Server vs Cloud

Question on exporting data via Server or directly to a Cloud system like OneDrive.

We will be using a handful of fieldworkers to collect SNA data at our university. That data will then need to be transferred to the PI’s institute for analysis. Previously, I ran into an issue where I needed to change a variable in the protocol, and it caused confusion due to a new protocol being created (i.e., a new workspace on Server), and fieldworker’s interviews being exported to different locations (old data vs new/corrected protocol). I emailed Network Canvas and the suggestion was just to skip Server altogether and export to OneDrive.

Basically, I’m just trying to figure out the most efficient way to do this. I’m guessing I could have all fieldworkers export to a shared OneDrive, which could then be shared with the lead institute? This might be easier than using Server…(but I do like that in Server you can export all files per protocol, rather than individual files. If I have a sample of 300, and am exporting to OneDrive, that means I’d have 300 copies of each of the files…which I think exports something like 4 files per person…ego, alter, alter-alter ties, for example?) That sounds like a nightmare to pull into R. If I use Server, I will have 4 csvs, each with all participants data. Or am I missing something obvious about exporting NOT using Server?

Sorry, I think I have several questions embedded in this ramble!

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Hi Emily,

Saving files to a shared cloud system like OneDrive would definitely be an excellent way to share the files across institutions. There shouldn’t be any difference in functionality when exporting directly from Interviewer compared to Server. For example, in Interviewer, you can turn on the “Merge Sessions” option in the “Confirm File Export Options” screen after you select which sessions you’d like to export. This will export multiple sessions into a single file on a per-protocol basis, which I think is what you’re trying to achieve.

That said, as someone who does a lot of data management myself, I like to keep participant files separate for data management transparency. Luckily, there are lots of helper functions in R that can make combining this data quite easy. You can check out one example in this short tutorial here that uses the “list.files”, “lapply”, and “bind_rows” functions to select all data of the same type and combine it together.