Full documentation at https://pennlinc-babs.readthedocs.io
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 job submissions and audits on SLURM high performance computing (HPC) clusters.
If you use BABS, please cite it using the metadata from the CITATION.cff file, as well as the following papers:
Zhao, C., Jarecka, D., Covitz, S., Chen, Y., Eickhoff, S. B., Fair, D. A., ... & Satterthwaite, T. D. (2024). A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps. Imaging Neuroscience, 2, 1-19. doi:10.1162/imag_a_00074.
Zhao, C., Chen, Y., Jarecka, D., Ghosh, S., Cieslak, M., & Satterthwaite, T. D. (2024). BABS in Action: Merging Reproducibility and Scalability for Large-Scale Neuroimaging Analysis With BIDS Apps. Biological Psychiatry, 95(10), S21-S22. doi:10.1016/j.biopsych.2024.02.056.
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.