Neuroscientific data management using DataLad

Robust data management from raw data to result publication is necessary to make scientific research more widely reusable. This remains a challenge, particularly in projects that involve a variety of subcomponents and large data. In this talk, I provide a user perspective on using DataLad procedures for structuring, managing, and sharing complex cognitive neuroscience projects. By showcasing example multimodal neuroimaging projects that include e.g., electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and behavioral data, I will highlight workflows that are uniquely enabled by the distributed nature of DataLad. Based on my experiences, I will also indicate remaining roadblocks I perceive to widespread adoption.