Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets

Sydney Covitz, Tinashe M. Tapera, Azeez Adebimpe, Aaron F. Alexander-Bloch, Maxwell A. Bertolero, Eric Feczko, Alexandre R. Franco, Raquel E. Gur, Ruben C. Gur, Timothy J Hendrickson, Audrey Houghton, Kahini Mehta, Kristin Murtha, Anders J Perrone, Tim Robert-Fitzgerald, Jenna M. Schabdach, Russell T. Shinohara, Jacob W. Vogel, Chenying Zhao, Damien A. FairMichael P. Milham, Matthew Cieslak, Theodore D. Satterthwaite

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Article number119609
StatePublished - Nov 2022

Bibliographical note

Funding Information:
This study was supported by grants from the National Institutes of Health : R01MH120482 , R37MH125829 , R01MH113550 , R01EB022573 , RF1MH116920 , R01MH112847 , R01MH123550 , R01NS112274 , R01MH123563 . Additional support was provided by the CHOP-Penn Lifespan Brain Institute, the Penn Brain Science Center, and the Center for Biomedical Image Computing and Analytics

Publisher Copyright:
© 2022


  • BIDS
  • Brain
  • Curation
  • Heterogeneity
  • MRI
  • Metadata
  • Neuroimaging
  • Software
  • Validation
  • Version control


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