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 richness 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.

Details

Title
BIDS Apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
Author
Gorgolewski, Krzysztof J; Alfaro-Almagro, Fidel; Auer, Tibor; Bellec, Pierre; Capota, Mihai; M Mallar Chakravarty; Churchill, Nathan W; Alexander Li Cohen; R Cameron Craddock; Devenyi, Gabriel A; Eklund, Anders; Esteban, Oscar; Flandin, Guillaume; Ghosh, Satrajit S; Guntupalli, J Swaroop; Jenkinson, Mark; Keshavan, Anisha; Kiar, Gregory; Liem, Franziskus; Pradeep Reddy Raamana; Raffelt, David; Steele, Christopher J; Pierre-Olivier Quirion; Smith, Robert E; Strother, Stephen C; Varoquaux, Gael; Yarkoni, Tal; Wang, Yida; Poldrack, Russell A
University/institution
Cold Spring Harbor Laboratory Press
Section
New Results
Publication year
2017
Publication date
Jan 29, 2017
Publisher
Cold Spring Harbor Laboratory Press
ISSN
2692-8205
Source type
Working Paper
Language of publication
English
ProQuest document ID
2070154938
Copyright
�� 2017. This article is published under http://creativecommons.org/licenses/by/4.0/ (���the License���). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.