NeuroImageNeurosciencePeer-reviewed journal article
Curation of BIDS (CuBIDS): a workflow and software package for streamlining reproducible curation of large BIDS datasets
Sydney C. CovitziD, Tinashe M. TaperaiD, Azeez AdebimpeiD, Aaron F. Alexander‐BlochiD, Maxwell A. BertoleroiD, Eric FeczkoiD, Alexandre R. FrancoiD, Raquel E. GuriD, Ruben C. GuriD, Timothy John HendricksoniD, Audrey HoughtoniD, Kahini P. MehtaiD, Kristin A. MurthaiD, Anders J. PerroneiD, Timothy Robert‐FitzgeraldiD, Jenna SchabdachiD, Russell Taki ShinoharaiD, Jacob W. VogeliD, Chenying ZhaoiD, Damien A. FairiD, Michael Peter MilhamiD, Matthew CieslakiD, Theodore Daniel SatterthwaiteiD
Abstract
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--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. HIGHLIGHTSO_LICuBIDS is a workflow and software package for curating BIDS data. C_LIO_LICuBIDS summarizes the heterogeneity in a BIDS dataset. C_LIO_LICuBIDS prepares BIDS data for successful preprocessing pipeline runs. C_LIO_LICuBIDS helps users perform metadata-based quality control. C_LI
- assay: imaging
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- approach: benchmark study
- Citations
- 33
- FWCI
- 6.67
- Field percentile
- top 3%
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