Nipoppy: A framework for standardizing neuroimaging studies to facilitate international derived-data sharing
Nikhil BhagwatiD, Michelle WangiD, Mathieu DugréiD, Julia‐Katharina PfarriD, Alyssa DaiiD, Sebastian Georg Wenzel UrchsiD, Brent Caleb McPhersoniD, Rémi GauiD, Eva M. van HeeseiD, Emile d’AngremontiD, Max A. LaansmaiD, Shweta PrasadiD, Jacob Sanz-RobinsoniD, Mohammad TorabiiD, Arman JahanpouriD, Matthew DanyluikiD, Alice Joubert, Austin MacdonaldiD, Lea WalleriD, Ashley Wilton StewartiD, Matthieu JoulotiD, Erin W. DickieiD, Gabriel A. DevenyiiD, Sylvain Bouix AiD, Steffen BollmanniD, , Jean‐Baptiste PolineiD
Abstract
Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.
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