TY - JOUR T1 - BIDS Apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods JF - bioRxiv DO - 10.1101/079145 SP - 079145 AU - Krzysztof J. Gorgolewski AU - Fidel Alfaro-Almagro AU - Tibor Auer AU - Pierre Bellec AU - Mihai Capotă AU - M. Mallar Chakravarty AU - Nathan W. Churchill AU - Alexander Li Cohen AU - R. Cameron Craddock AU - Gabriel A. Devenyi AU - Anders Eklund AU - Oscar Esteban AU - Guillaume Flandin AU - Satrajit S. Ghosh AU - J. Swaroop Guntupalli AU - Mark Jenkinson AU - Anisha Keshavan AU - Gregory Kiar AU - Franziskus Liem AU - Pradeep Reddy Raamana AU - David Raffelt AU - Christopher J. Steele AU - Pierre-Olivier Quirion AU - Robert E. Smith AU - Stephen C. Strother AU - Gaël Varoquaux AU - Tal Yarkoni AU - Yida Wang AU - Russell A. Poldrack Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/01/29/079145.abstract N2 - 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 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.Author Summary Magnetic Resonance Imaging (MRI) is a non-invasive way to measure human brain structure and activity that has been used for over 25 years. There are thousands MRI studies performed every year generating a substantial amount of data. At the same time, many new data analysis methods are being developed every year. The potential of using new analysis methods on the variety of existing and newly acquired data is hindered by difficulties in software deployment and lack of support for standardized input data. Here we propose to use container technology to make deployment of a wide range of data analysis techniques easy. In addition, we adapt the existing data analysis tools to interface with data organized in a standardized way. We hope that this approach will enable researchers to access a wider range of methods when analyzing their data which will lead to accelerated progress in human neuroscience. ER -