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BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
Department of Psychology, Stanford University, Stanford, California, United States of America.ORCID iD: 0000-0003-3321-7583
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford University, Oxford, United Kingdom.ORCID iD: 0000-0002-9133-5951
Department of Psychology, Royal Holloway University of London, Egham, United Kingdom.
Centre de Recherche de l’Institut Universitaire Gériatrique de Montréal, Montreal, Canada, Department of computer science and operations research, Université de Montréal, Montreal, Canada.ORCID iD: 0000-0002-9111-0699
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2017 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, no 3, e1005209Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 13, no 3, e1005209
Keyword [en]
Neuroimaging, Functional magnetic resonance imaging, Operating systems, Software development, Data processing, Magnetic resonance imaging, Software tools, Diffusion weighted imaging
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-135445DOI: 10.1371/journal.pcbi.1005209ISI: 000398031900037OAI: oai:DiVA.org:liu-135445DiVA: diva2:1081782
Note

Funding agencies: Laura and John Arnold Foundation; NIH grant - NIH-NIBIB [R01 EB020740]

Available from: 2017-03-15 Created: 2017-03-15 Last updated: 2017-04-20

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Gorgolewski, Krzysztof J.Alfaro-Almagro, FidelBellec, PierreCapotă, MichelChakravarty, M. MallarLi Cohen, AlexanderCraddock, R. CameronDevenyi, Gabriel A.Eklund, AndersEsteban, OscarGhosh, Satrajit S.Jenkinson, MarkKeshavan, AnishaKiar, GregoryLiem, FranziskusReddy Raamana, PradeepSteele, Cristopher J.Quirion, Pierre-OlivierWang, YidaYarkoni, TalPoldrack, Russel A.
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