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BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.

Authors :
Krzysztof J Gorgolewski
Fidel Alfaro-Almagro
Tibor Auer
Pierre Bellec
Mihai Capotă
M Mallar Chakravarty
Nathan W Churchill
Alexander Li Cohen
R Cameron Craddock
Gabriel A Devenyi
Anders Eklund
Oscar Esteban
Guillaume Flandin
Satrajit S Ghosh
J Swaroop Guntupalli
Mark Jenkinson
Anisha Keshavan
Gregory Kiar
Franziskus Liem
Pradeep Reddy Raamana
David Raffelt
Christopher J Steele
Pierre-Olivier Quirion
Robert E Smith
Stephen C Strother
Gaël Varoquaux
Yida Wang
Tal Yarkoni
Russell A Poldrack
Source :
PLoS Computational Biology, Vol 13, Iss 3, p e1005209 (2017)
Publication Year :
2017
Publisher :
Public Library of Science (PLoS), 2017.

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.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X and 15537358
Volume :
13
Issue :
3
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.3104ecfd7dcb4833982f436f752842e0
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1005209