BIDScoin is a user-friendly Python application suite that converts ("coins") source-level (raw) neuroimaging data sets to standardized data sets that are organized according to the Brain Imaging Data Structure (BIDS) specification. Rather than depending on complex programmatic logic for source data-type identification, BIDScoin uses a mapping approach to discover the different source data types in your repository and convert them into BIDS data types. Different runs of source data are uniquely identified by their file system properties (e.g. file name or size) and by their attributes (e.g. ProtocolName from the DICOM header). Mapping information can be pre-specified (e.g. per site), allowing BIDScoin to make intelligent first suggestions on how to classify and convert the data. While this command-line procedure exploits all information available on disk, BIDScoin offers a Graphical User Interface (GUI) for researchers to check and edit these mappings -- bringing in the missing knowledge that often exists in their heads only. This interactive step can also be skipped for employment in fully automated dataflow pipelines.
Data conversions are performed within plugins, such as plugins that employ dcm2niix, spec2nii or nibabel.
BIDScoin requires no programming knowledge in order to use it, but users can use regular expressions and plug-ins to further enhance BIDScoin's power and flexibility, and readily handle a wide variety of source data types.
BIDScoin is developed at the Donders Institute of the Radboud University.
[x] DICOM source data
[x] PAR/REC source data (Philips)
[x] NIfTI source data
[x] Physiological logging data*
[x] MR Spectroscopy data**
[x] PET/CT data
[x] Field maps*
[x] Multi-echo data*
[x] Multi-coil data*
[x] Plug-ins
[ ] Stimulus/behavioural logfiles
* = Only DICOM source data** = Only Twix, SDAT/SPAR and P-file source data
Note
- All source code is hosted at GitHub and freely available under the GPL-3.0-or-later license.
- The full BIDScoin documentation is hosted at Read the Docs
- For citation and more information, see our BIDScoin publication in Frontiers in Neuroinformatics (DOI: 10.3389/fninf.2021.770608). See here for citations of the plugins. A custom-made citation report can be generated with the help of duecredit
- You are encouraged to post issues or questions at GitHub or NeuroStars (tag: #bidscoin)
Are you a Python programmer with an interest in BIDS who knows about GE and/or Philips data? Are you experienced with parsing stimulus presentation log-files? Or do you have ideas to improve BIDScoin or its documentation? Have you come across bugs? In any case, you are more than welcome to provide feedback and to contribute to this project.