Published February 28, 2025 | Version 1.2.0
Software Open

Curation of BIDS (CuBIDS)

Authors/Creators

  • 1. University of Pennsylvania
  • 2. Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • 3. Lifespan Brain Institute (LiBI) of the Children's Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, PA
  • 4. Department of Clinical Sciences Malmö, Faculty of Medicine, SciLifeLab, Lund University, Lund, Sweden
  • 5. Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Description

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad- a version control software package for data- as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

Notes

If you use this software, please cite it using the metadata from this file, as well as the NeuroImage paper (doi:10.1016/j.neuroimage.2022.119609).

Files

PennLINC/CuBIDS-1.2.0.zip

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Additional details

Related works

Is supplement to
Software: https://github.com/PennLINC/CuBIDS/tree/1.2.0 (URL)

Software

Repository URL
https://github.com/PennLINC/CuBIDS