The MNI data-sharing and processing ecosystem

- We outline the MNI data-sharing ecosystem, which include the LORIS and CBRAIN platforms.
- We detail what tools and pipelines these platforms use (e.g., CIVET, MINC, FSL).
- A step-by-step delineation of the MNI ecosystem is given from data acquisition to dissemination.
- Five examples of public data-sharing repositories from the MNI ecosystem services are outlined.
- We discuss a number of important data-sharing challenges and considerations.

Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of “Big Data”, there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.