BABS: BIDS App Bootstrap

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BIDS App Bootstrap (BABS) is a reproducible, generalizable, and scalable Python package for BIDS App analysis of large datasets. It uses DataLad and adopts the FAIRly big framework. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm high performance computing (HPC) clusters.

Please cite our paper if you use BABS:

Zhao, C., Jarecka, D., Covitz, S., Chen, Y., Eickhoff, S. B., Fair, D. A., Franco, A. R., Halchenko, Y. O., Hendrickson, T. J., Hoffstaedter, F., Houghton, A., Kiar, G., Macdonald, A., Mehta, K., Milham, M. P., Salo, T., Hanke, M., Ghosh, S. S., Cieslak, M. & Satterthwaite, T. D. (2024). A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps. Imaging Neuroscience. Accepted.

Currently, the paper has been accepted for publication in Imaging Neuroscience. The bioRxiv version can be found here.

BABS programs

Schematic of BABS workflow

Background and Significance

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. The BIDS Apps - the software operating on BIDS data - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework remains challenging to general users.

BABS was developed to address these challenges and to facilitate the reproducible application of BIDS Apps to large-scale datasets.

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