The advent of Big Data science (BDs) has generated enormous amounts, varieties, and sources of complex datasets that have vast potential for the creation of new knowledge, particularly in relation to primary and secondary disease prevention (Eaton et al., 2012); yet BDs also brings inherent challenges of utilization and value.A critical cross-cutting issue is the creation of a compelling and effective user experience that can empower biomedical researchers and trainees with limited information technology budgets access to powerful and intuitive tools designed to effectively address the challenges posed by the four dimensions of Big Data: (1) volume: the vast amount of data that is generated through source integration; (2) variety: the lack of standardization that is inherent in combining data from different resources; (3) velocity: the high rate at which data is constantly changing; and (4) veracity: the need for reliability measures and safeguards protecting the confidentiality of the individuals involved (Otero, Hersh, & Jai Ganesh, 2014).These challenges are particularly pronounced in neuroscience Big Data, as neuroimaging produces some of the largest and most complex data types (Van Horn & Toga, 2014;Turner & Van Horn, 2012;Bowman, Joshi, & Van Horn, 2012).Through advances in neuroimaging techniques, such as functional magnetic resonance image (fMRI) and positron emission tomography (PET), massive stores of highresolution and high-dimensional brain images
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