Cargando…

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

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 m...

Descripción completa

Detalles Bibliográficos
Autores principales: Covitz, Sydney, Tapera, Tinashe M., Adebimpe, Azeez, Alexander-Bloch, Aaron F., Bertolero, Maxwell A., Feczko, Eric, Franco, Alexandre R., Gur, Raquel E., Gur, Ruben C., Hendrickson, Timothy, Houghton, Audrey, Mehta, Kahini, Murtha, Kristin, Perrone, Anders J., Robert-Fitzgerald, Tim, Schabdach, Jenna M., Shinohara, Russell T, Vogel, Jacob W., Zhao, Chenying, Fair, Damien A., Milham, Michael P., Cieslak, Matthew, Satterthwaite, Theodore D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981813/
https://www.ncbi.nlm.nih.gov/pubmed/36064140
http://dx.doi.org/10.1016/j.neuroimage.2022.119609
_version_ 1784900190169202688
author Covitz, Sydney
Tapera, Tinashe M.
Adebimpe, Azeez
Alexander-Bloch, Aaron F.
Bertolero, Maxwell A.
Feczko, Eric
Franco, Alexandre R.
Gur, Raquel E.
Gur, Ruben C.
Hendrickson, Timothy
Houghton, Audrey
Mehta, Kahini
Murtha, Kristin
Perrone, Anders J.
Robert-Fitzgerald, Tim
Schabdach, Jenna M.
Shinohara, Russell T
Vogel, Jacob W.
Zhao, Chenying
Fair, Damien A.
Milham, Michael P.
Cieslak, Matthew
Satterthwaite, Theodore D.
author_facet Covitz, Sydney
Tapera, Tinashe M.
Adebimpe, Azeez
Alexander-Bloch, Aaron F.
Bertolero, Maxwell A.
Feczko, Eric
Franco, Alexandre R.
Gur, Raquel E.
Gur, Ruben C.
Hendrickson, Timothy
Houghton, Audrey
Mehta, Kahini
Murtha, Kristin
Perrone, Anders J.
Robert-Fitzgerald, Tim
Schabdach, Jenna M.
Shinohara, Russell T
Vogel, Jacob W.
Zhao, Chenying
Fair, Damien A.
Milham, Michael P.
Cieslak, Matthew
Satterthwaite, Theodore D.
author_sort Covitz, Sydney
collection PubMed
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.
format Online
Article
Text
id pubmed-9981813
institution National Center for Biotechnology Information
language English
publishDate 2022
record_format MEDLINE/PubMed
spelling pubmed-99818132023-05-07 Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets Covitz, Sydney Tapera, Tinashe M. Adebimpe, Azeez Alexander-Bloch, Aaron F. Bertolero, Maxwell A. Feczko, Eric Franco, Alexandre R. Gur, Raquel E. Gur, Ruben C. Hendrickson, Timothy Houghton, Audrey Mehta, Kahini Murtha, Kristin Perrone, Anders J. Robert-Fitzgerald, Tim Schabdach, Jenna M. Shinohara, Russell T Vogel, Jacob W. Zhao, Chenying Fair, Damien A. Milham, Michael P. Cieslak, Matthew Satterthwaite, Theodore D. Neuroimage Article 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. 2022-11 2022-09-03 /pmc/articles/PMC9981813/ /pubmed/36064140 http://dx.doi.org/10.1016/j.neuroimage.2022.119609 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Article
Covitz, Sydney
Tapera, Tinashe M.
Adebimpe, Azeez
Alexander-Bloch, Aaron F.
Bertolero, Maxwell A.
Feczko, Eric
Franco, Alexandre R.
Gur, Raquel E.
Gur, Ruben C.
Hendrickson, Timothy
Houghton, Audrey
Mehta, Kahini
Murtha, Kristin
Perrone, Anders J.
Robert-Fitzgerald, Tim
Schabdach, Jenna M.
Shinohara, Russell T
Vogel, Jacob W.
Zhao, Chenying
Fair, Damien A.
Milham, Michael P.
Cieslak, Matthew
Satterthwaite, Theodore D.
Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets
title Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets
title_full Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets
title_fullStr Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets
title_full_unstemmed Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets
title_short Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets
title_sort curation of bids (cubids): a workflow and software package for streamlining reproducible curation of large bids datasets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981813/
https://www.ncbi.nlm.nih.gov/pubmed/36064140
http://dx.doi.org/10.1016/j.neuroimage.2022.119609
work_keys_str_mv AT covitzsydney curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT taperatinashem curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT adebimpeazeez curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT alexanderblochaaronf curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT bertoleromaxwella curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT feczkoeric curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT francoalexandrer curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT gurraquele curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT gurrubenc curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT hendricksontimothy curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT houghtonaudrey curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT mehtakahini curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT murthakristin curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT perroneandersj curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT robertfitzgeraldtim curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT schabdachjennam curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT shinohararussellt curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT vogeljacobw curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT zhaochenying curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT fairdamiena curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT milhammichaelp curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT cieslakmatthew curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets
AT satterthwaitetheodored curationofbidscubidsaworkflowandsoftwarepackageforstreamliningreproduciblecurationoflargebidsdatasets