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Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility

Authors :
Dylan Martin
Sunitha Basodi
Sandeep Panta
Kelly Rootes-Murdy
Paul Prae
Anand D. Sarwate
Ross Kelly
Javier Romero
Bradley T. Baker
Harshvardhan Gazula
Jeremy Bockholt
Jessica A. Turner
Nathalia B. Esper
Alexandre R. Franco
Sergey Plis
Vince D. Calhoun
Source :
Frontiers in Neuroinformatics, Vol 17 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

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.

Details

Language :
English
ISSN :
16625196
Volume :
17
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neuroinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.0aeb0c6c38a9444aad29dd4c5b554eb6
Document Type :
article
Full Text :
https://doi.org/10.3389/fninf.2023.1207721