This folder contains toy datasets inspired from real ones but with removed data and keeping meta-information to minimum. The demographic informations and subjects id are randomised.

Each example folder contains 3 subfolders:

• source, containing the original toy dataset
• renamed, containing sorted dataset
• bids, containing bidsified dataset
• resources, containing all nessesairy plugins and files to run the example

Example 1

Example 1 is based on unpublished study on effet of fatigue on memory performance.

How to run

First prepare data from source and place it to renamed

python3 $PATH/coinsort.py -r "nii" -t "MRI" example1/source/ example1/renamed/ -p example1/resources/plugins/rename_plugin.py

Next generate an bidsmap.yaml configuration file:

python $PATH/bidsmapper.py example1/renamed/ example1/bids -p example1/resources/plugins/bidsify_plugin.py

The generated file will be placed to example1/bids/code/bidscoin/bidsmap.yaml. The initial run of bidsmap will produce a lot of warnings (> 500), listing all nessesary modifications to apply to bidsmap.yaml configuration file. For conviniance, the warnings and errors will be placed to example1/bids/code/bidscoin/log/ directory.

Apply nessesary modifications and rerun bidsmapper.py untill you don't see any warnings and/or errors. You don't need to add -p example1/resources/plugins/bidsify_plugin.py after first time, as plugin will be places into configuration file.

Alternatively, you can place already prepeared config file from example1/resources/map to example1/bids/code/bidscoin/

Run bidsifier by executing

python $PATH/bidscoiner.py example1/renamed/ example1/bids

If everything is good, you will retrieve bidsified dataset in example1/bids/