Nipoppy: A framework for standardizing neuroimaging studies to facilitate international derived-data sharing
Bhagwat, N.; Wang, M.; Dugre, M.; Pfarr, J.-K.; Dai, A.; Urchs, S.; McPherson, B.; Gau, R.; van Heese, E. M.; d'Angremont, E.; Laansma, M. A.; Prasad, S.; Sanz-Robinson, J.; Torabi, M.; Jahanpour, A.; Danyluik, M.; Joubert, A.; Macdonald, A.; Waller, L.; Stewart, A.; Joulot, M.; Dickie, E.; Devenyi, G. A.; Bouix, S.; Bollmann, S.; Jahanshad, N.; Thompson, P. M.; Burgos, N.; Chakravarty, M. M.; Halchenko, Y. O.; van der Werf, Y. D.; Poline, J.-B.
Show 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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