Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/121963
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Type: Journal article
Title: BIDS apps: improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
Author: Gorgolewski, K.J.
Alfaro-Almagro, F.
Auer, T.
Bellec, P.
Capotă, M.
Chakravarty, M.M.
Churchill, N.W.
Cohen, A.L.
Craddock, R.C.
Devenyi, G.A.
Eklund, A.
Esteban, O.
Flandin, G.
Ghosh, S.S.
Guntupalli, J.S.
Jenkinson, M.
Keshavan, A.
Kiar, G.
Liem, F.
Raamana, P.R.
et al.
Citation: PLoS Computational Biology, 2017; 13(3):e1005209-e1005209
Publisher: Plos One
Issue Date: 2017
ISSN: 1553-734X
1553-7358
Editor: Schneidman, D.
Statement of
Responsibility: 
Krzysztof J. Gorgolewski, Fidel Alfaro-Almagro, Tibor Auer, Pierre Bellec, Mihai Capotă ... Mark Jenkinson ... et al.
Abstract: The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.
Keywords: Magnetic Resonance Imaging
Rights: © 2017 Gorgolewski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI: 10.1371/journal.pcbi.1005209
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Computer Science publications

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