We present the Canadian Open Neuroscience Platform (CONP) portal to answer the research community's need for flexible data sharing resources and provide advanced tools for search and processing infrastructure capacity. This portal differs from previous data sharing projects as it integrates datasets originating from a number of already existing platforms or databases through DataLad, a file level data integrity and access layer. The portal is also an entry point for searching and accessing a large number of standardized and containerized software and links to a computing infrastructure. It leverages community standards to help document and facilitate reuse of both datasets and tools, and already shows a growing community adoption giving access to more than 60 neuroscience datasets and over 70 tools. The CONP portal demonstrates the feasibility and offers a model of a distributed data and tool management system across 17 institutions throughout Canada.

Yaroslav O. Halchenko∗1, Kyle Meyer1, Benjamin Poldrack2, Debanjum Singh Solanky1, Adina S. Wagner2, Jason Gors1, Dave MacFarlane3, Dorian Pustina4, Vanessa Sochat5, Satrajit S. Ghosh6, Christian Mönch2, Christopher J. Markiewicz7, Laura Waite2, Ilya Shlyakhter8, Alejandro de la Vega9, Soichi Hayashi10, Christian Olaf Häusler2, 11, Jean-Baptiste Poline12, Tobias Kadelka2, Kusti Skytén13, Dorota Jarecka6, David Kennedy14, Ted Strauss15, Matt Cieslak16, Peter Vavra17, Horea-Ioan Ioanas18, Robin Schneider19, Mika Pflüger20, James V. Haxby1, Simon B. Eickhoff2, 11, and Michael Hanke†2, 11

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.