CRPN DataClub

Daniel Brady University of  Sheffield, #EEGManyLabs initiative) presents us on Curating legacy resting-state EEG datasets using RS-BIDSify [Slides]

Effective data curation and large-scale meta-science in neuroimaging depend heavily on standardising data formats across datasets and modalities. While the Brain Imaging Data Structure (BIDS) offers a well-thought-out standard to build upon, converting existing datasets to be BIDS-compliant still requires a lot of effort. I developed RS-BIDSify in order to reduce this effort as part of an #EEGManyLabs initiative to assemble and curate a large database of legacy resting-state EEG data. This is a Python-based tool that directly tackles the conversion bottleneck by automating the transition of legacy resting-state EEG recordings and metadata into fully BIDS-compliant structures.

This session will begin with a brief introduction to the overarching challenges of legacy data integration, quickly moving into the specific design constraints involved in building an automated conversion pipeline. We will then explore how RS-BIDSify was engineered as a solution to navigate these complexities, followed by a practical presentation demonstrating the software’s workflow in action. Finally, the session will conclude with an open discussion, providing an opportunity for attendees to share feedback, identify common data quirks the pipeline must still adapt to, and help build a development wish-list for future updates.