Dear neuroimaging community,Most of our daily work involves data - acquiring, organizing, cleaning, analyzing, understanding, and explaining data. MRI scanners produce a lot of complicated outputs with rich metadata needed for further analysis. In addition all of the external measures of behaviour and individual differences contribute to the complexity of acquired data. It is easy to get lost in this chaos especially for early scientists new to the field.
How we store, organize, and describe neuropsychological data has been an individual issue. Each researcher had their own way of describing data. Such approach can be problematic when one dataset needs to be used by more than one person. Imagine a situation when as a PI you want your postdoc to reanalyze an old dataset acquired by a long gone PhD student just to discover that bits are missing and the rest is an unreadable Excel spreadsheet.
Having a common formalized way of organizing and describing data have advantages beyond sharing across members of the same lab. Workflow developers can build tools that will be able to preprocess your data with very little user input. It will be also easier import such formatted dataset into databases (XNAT, COINS, Scitran, NiDB, openfmri etc.) and share it with wider public. We already started writing tools (and OpenfMRI2BIDS converter:
https://github.com/INCF/openfmri2bids and BIDS validator:
https://github.com/Squishymedia/bids-validator) and we hope that mayor pipeline engines (LONI, AA, C-PAC, REST, PSOM, Nipype etc.) will adopt BIDS.
Best,
Chris Gorgolewski and the INCF Data Sharing Task Force