ReproIn: automatic generation of shareable, version-controlled BIDS datasets from MR scanners

Presented During:  ORAL SESSION: Informatics
Tuesday, Jun 19: 10:30 AM  - 10:42 AM 
Oral Sessions 
Singapore Convention Center 
Room: Room 324-326 
Lack of reproducibility is a problem in neuroimaging. Adherence to open standards, easy data sharing, and automation of data conversion and analysis steps are some of the key solutions to address it. The formalization of the Brain Imaging Data Structure (BIDS) [1] made it easier for researchers to collaborate on shared data and to benefit from standardized processing using various BIDS-aware application. At Dartmouth, following the philosophy that science should be open by design [2], we automated the collection of neuroimaging data as a hierarchy of BIDS datasets right from the MR scanner. Thus, individual research groups do not have to convert their data to BIDS manually, eliminating one of the biggest barriers to data sharing. Adherence to the BIDS standard allows investigators to immediately use BIDS-aware applications for data QA (e.g., bids-validator, MRIQC [3]) to catch obvious problems with data acquisition, and to automate preprocessing and analysis. Because the entire process occurs in a Singularity container [4], and all data is version-controlled with DataLad and git, our approach eliminates virtually any ambiguity in data provenance. Here we present details of our setup, named ReproIn (Reproducible Input). All system configurations, software, and material are released under open-source licenses and are provided in a container, so that any institution can easily implement this solution at their imaging centers.
View Abstract 2000


Matteo Visconti di Oleggio Castello, Dartmouth College