Abstract
COINSTAC: Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation
Highlights
There are significant data transfer, organizational, and computational challenges, the result being that collaborative group research requires a great deal of coordination
COINSTAC is intended to be the ultimate hub by which researchers can build statistical (Ming et al, 2017) or machine learning models (Gazula et al, 2018) collaboratively in a decentralized fashion
Data-specific collaborative efforts have included either aggregating the data via a centralized data sharing repository or sharing data via agreement-based collaborations. Frameworks such as ENIGMA (Thompson et al, 2014) to some extent bypass the need for data agreements by performing a centrally coordinated analysis at each local site
Summary
Central to the field of neuroimaging is the development of techniques for making sense of complex brain data. Rapid technological advancements are pushing the spatial and temporal resolution of imaging in different modalities to an unprecedented level, leading to large datasets which cannot be analyzed in the traditional desktop computing paradigm. This has led to a paradigm shift in scientific research with an increasing emphasis on collaborative data sharing. There are significant data transfer, organizational, and computational challenges, the result being that collaborative group research requires a great deal of coordination. Human and business factors can hamper research from happening at a constructive pace, maybe even forbidding group research to occur at all
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