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Hi, yes. The OS converter probably expects results in a very specific format.
What do your exported results look like? Depending on the software you use to analyze data, maybe the best is to use an R package (like jsonlite) to read your data in
Thank you for your help, Elisa.
So far, I have run just a couple of trials to test data export and conversion.
If I export the single trial from JATOS, I am able to access it without issues using python's pandas.read_json function. However, when I looked the file which I get when I click "export all", it seems to me that each trial is reported sequentially, with a newline as separator. When I tried opening it in python as described above, I got "ValueError: Trailing data".
I am unfamiliar with R, so I was trying to look for alternatives. Since I plan to collect a lot of trials per participant, I was hoping to analyse the data using python, and processing each raw output individually would be unfeasible.
I have attached the output file I am currently having trouble with, maybe it can help you find the issue more easily.
It contains the results of 3 trials.
Hi, I have to run and can look at this later but yes, the problem is most likely that you have the following:
]
[{"url":{},"meta":{"labjs_version":"20.2.4","labjs_build":
etc. That's not a valid json.
In the evening I can send you a solution, now I have to run
Hi again,
Sorry for the chaotic reply sooner :)
I don't use python, I use R. However, I was wrong: the format where each line is a valid JSON is apparently called jsonlines, and there's a library for that in Python. Have you tried this already?
https://jsonlines.readthedocs.io/en/latest/
If that doesn't work, what I've done in the past in R is to turn the separate lines into a single json string, like this
In your case that doesn't do it, not sure why. I'm sure I/you can figure it out, let me know if that python library doesn't help
Best
Thank you, I was able to load the JSON file as pandas dataframes using the jsonlines package as you suggested. Here is the code I used:
This allows you to display all individual JSON outputs as tables. I then came up with a quick solution to concatenate all files in a single dataframe using the following code.
I am sure someone will come up with a better solution for this, but for now this works well enough for me. Thanks again for your help!