PyBIDS: Python tools for BIDS datasets

Abstract

Brain imaging researchers regularly work with large, heterogeneous, high-dimensional datasets. Historically, researchers have dealt with this complexity idiosyncratically, with every lab or individual implementing their own preprocessing and analysis procedures. The resulting lack of field-wide standards has severely limited reproducibility and data sharing and reuse.To address this problem, we and others recently introduced the Brain Imaging Data Standard (BIDS; (Gorgolewski et al., 2016)), a specification meant to standardize the process of representing brain imaging data. BIDS is deliberately designed with adoption in mind; it adheres to a user-focused philosophy that prioritizes common use cases and discourages complexity. By successfully encouraging a large and ever-growing subset of the community to adopt a common standard for naming and organizing files, BIDS has made it much easier for researchers to share, re-use, and process their data .The ability to efficiently develop high-quality spec-compliant applications itself depends to a large extent on the availability of good tooling. Because many operations recur widely across diverse contexts-for example, almost every tool designed to work with BIDS datasets involves regular file-filtering operations-there is a strong incentive to develop utility libraries that provide common functionality via a standardized, simple API.PyBIDS is a Python package that makes it easier to work with BIDS datasets. In principle, its scope includes virtually any functionality that is likely to be of general use when working with BIDS datasets (i.e., that is not specific to one narrow context). At present, its core and most widely used module supports simple and flexible querying and manipulation of BIDS datasets. PyBIDS makes it easy for researchers and developers working in Python to search for BIDS files by keywords and/or metadata; to consolidate and retrieve file-associated metadata spread out across multiple levels of a BIDS hierarchy; to construct BIDS-valid path names for new files; and to validate projects against the BIDS specification, among other applications.

Evaluating the reliability of human brain white matter tractometry

“…We rely heavily on implementations of dMRI analysis algorithms implemented in DIPY (27). Reproducibility and interoperability are also facilitated by relying on the BIDS format (65) and the pyBIDS software (63,64). Requiring a BIDS-like input makes integration with other software in the ecosystem easier.…”

“…The first step in analysis is to find the files that the software will use. pyAFQ relies on pyBIDS (63,64) to query data that is provided in the BIDS format (65). It looks for dMRI, b-value, and b-vector files stored in standard formats (see https://yeatmanlab.github.io/pyAFQ/usage/data.html for details).…”

BIDSonym - a BIDSapp for the pseudo-anonymization of neuroimaging datasets

“…BIDSonym provides a straightforward and flexible way to pseudo-anonymize neuroimaging datasets by a variety of means, operating on both small and large datasets through its implementation following the BIDS-App template (Gorgolewski et al 2017). BIDSonym depends on the nibabel (Brett et al 2020), nipype (Gorgolewski et al 2011), nilearn (Abraham et al 2014), pybids (Yarkoni et al 2019) and pandas (McKinney 2010) python packages (all are well maintained and tested) and is licensed under the BSD-3 license (https://opensource. org/licenses/BSD-3-Clause).…”

fMRIflows: a consortium of fully automatic univariate and multivariate fMRI processing pipelines

“…The first notebook contained in fMRIflows, called 01_spec_preparation.ipynb, can be used to create those JSON files, based on the provided dataset and some standard default parameters. It does so by using Nibabel v2.3.0 (Brett et al, 2018), PyBIDS v0.8 (Yarkoni et al, 2019) and other standard Python libraries. It is up to the user to change any potential processing parameter should they be different from the used default values.…”