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Neuroimaging Data Analysis on the Cloud
Most of the software tools for neuroimaging analysis are released and distributed as standalone desktop applications. Latest trends in neuroimaging studies are focusing more on scalability issues both related to larger data samples and cpu intensive computational methods. The open challenge is how to combine data management and data analysis. We may consider LONI, NeuGrid and INCF-AWS as the leading solutions to combine large data repositories and scalable computational architecture. However these projects address a particular usecase where data are shared among several institutions and researchers. We argue that this kind of scenario doesn’t cover the common usecase where data are locally managed by the principal investigator. We focus our contribution on a scenario where a neuroscientist has the following requirements. First, data are managed locally according to the user needs; secondly, the user has access to a remote HPC infrastructure where it is available an execution environment compliant with the required data analysis. Our proposal is a solution based on a seamless integration of the two environments, the local one and the remote one. The main advantage is not require the management of data according to some framework for meta- data annotation. The additional benefit is to preserve the flexibility of pipeline deployment in a user friendly interface to HPC infrastructure.
EnginSoft, APSS, University of Trento, Trentino Networks