Breaking Barriers in Collaborative Neuroimaging Research with COINSTAC Vaults

Published on June 19, 2023

Imagine you’re trying to collaborate on a massive puzzle, but you can’t physically share the pieces with your teammates. Frustrating, right? Well, that’s how it feels for researchers in collaborative neuroimaging studies. But fear not! COINSTAC is here to save the day. Think of COINSTAC as a high-tech puzzle solver that allows researchers to analyze datasets without actually sharing the data itself. Now, COINSTAC has unveiled its latest innovation: COINSTAC Vaults (CVs). These Vaults act like treasure chests filled with standardized, readily available datasets, making collaboration easier than ever! They seamlessly integrate with the federated analysis capabilities of COINSTAC, allowing researchers to dig into these datasets without hassle. It’s like having a secret stash of puzzle pieces that everyone can access. And here’s the best part: CVs can work alongside open data too, bridging a gap in the sharing ecosystem. By creating a CV and hosting open data, researchers can amplify their findings. These CVs have been put to the test in functional and structural neuroimaging studies, revealing their potential to enhance reproducibility and boost sample sizes. So let’s join forces and dive into the world of COINSTAC Vaults!

Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC (The Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC’s federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration, and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.

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