001024665 001__ 1024665
001024665 005__ 20250912110151.0
001024665 0247_ $$2doi$$a10.1162/imag_a_00074
001024665 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-02338
001024665 0247_ $$2pmid$$a37645999
001024665 0247_ $$2WOS$$aWOS:001531527700014
001024665 037__ $$aFZJ-2024-02338
001024665 082__ $$a050
001024665 1001_ $$0P:(DE-HGF)0$$aZhao, Chenying$$b0
001024665 245__ $$aA reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps
001024665 260__ $$aCambridge, MA$$bMIT Press$$c2024
001024665 3367_ $$2DRIVER$$aarticle
001024665 3367_ $$2DataCite$$aOutput Types/Journal article
001024665 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1712662589_18043
001024665 3367_ $$2BibTeX$$aARTICLE
001024665 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001024665 3367_ $$00$$2EndNote$$aJournal Article
001024665 520__ $$aNeuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS)—the BIDS App—has provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging—especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad—a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here, we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n = 2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.Reproducibility, BIDS Apps, software, MRI, big data, image processing
001024665 536__ $$0G:(DE-HGF)POF4-5254$$a5254 - Neuroscientific Data Analytics and AI (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001024665 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001024665 7001_ $$0P:(DE-HGF)0$$aJarecka, Dorota$$b1
001024665 7001_ $$0P:(DE-HGF)0$$aCovitz, Sydney$$b2
001024665 7001_ $$0P:(DE-HGF)0$$aChen, Yibei$$b3
001024665 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b4$$ufzj
001024665 7001_ $$0P:(DE-HGF)0$$aFair, Damien A.$$b5
001024665 7001_ $$0P:(DE-HGF)0$$aFranco, Alexandre R.$$b6
001024665 7001_ $$0P:(DE-HGF)0$$aHalchenko, Yaroslav O.$$b7
001024665 7001_ $$0P:(DE-HGF)0$$aHendrickson, Timothy J.$$b8
001024665 7001_ $$0P:(DE-Juel1)131684$$aHoffstaedter, Felix$$b9$$ufzj
001024665 7001_ $$0P:(DE-HGF)0$$aHoughton, Audrey$$b10
001024665 7001_ $$0P:(DE-HGF)0$$aKiar, Gregory$$b11
001024665 7001_ $$0P:(DE-HGF)0$$aMacdonald, Austin$$b12
001024665 7001_ $$0P:(DE-HGF)0$$aMehta, Kahini$$b13
001024665 7001_ $$0P:(DE-HGF)0$$aMilham, Michael P.$$b14
001024665 7001_ $$0P:(DE-HGF)0$$aSalo, Taylor$$b15
001024665 7001_ $$0P:(DE-Juel1)177087$$aHanke, Michael$$b16$$ufzj
001024665 7001_ $$0P:(DE-HGF)0$$aGhosh, Satrajit S.$$b17
001024665 7001_ $$0P:(DE-HGF)0$$aCieslak, Matthew$$b18
001024665 7001_ $$0P:(DE-HGF)0$$aSatterthwaite, Theodore D.$$b19$$eCorresponding author
001024665 773__ $$0PERI:(DE-600)3167925-0$$a10.1162/imag_a_00074$$gVol. 2, p. 1 - 19$$p1 - 19$$tImaging neuroscience$$v2$$x2837-6056$$y2024
001024665 8564_ $$uhttps://juser.fz-juelich.de/record/1024665/files/imag_a_00074.pdf$$yOpenAccess
001024665 8564_ $$uhttps://juser.fz-juelich.de/record/1024665/files/imag_a_00074.gif?subformat=icon$$xicon$$yOpenAccess
001024665 8564_ $$uhttps://juser.fz-juelich.de/record/1024665/files/imag_a_00074.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001024665 8564_ $$uhttps://juser.fz-juelich.de/record/1024665/files/imag_a_00074.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001024665 8564_ $$uhttps://juser.fz-juelich.de/record/1024665/files/imag_a_00074.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001024665 909CO $$ooai:juser.fz-juelich.de:1024665$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
001024665 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b4$$kFZJ
001024665 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b4
001024665 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131684$$aForschungszentrum Jülich$$b9$$kFZJ
001024665 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177087$$aForschungszentrum Jülich$$b16$$kFZJ
001024665 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, United States †Corresponding Author: Theodore D. Satterthwaite (sattertt@pennmedicine.upenn.edu)$$b19
001024665 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5254$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001024665 9141_ $$y2024
001024665 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001024665 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-02
001024665 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2024-09-26T09:40:26Z
001024665 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2024-09-26T09:40:26Z
001024665 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2024-09-26T09:40:26Z
001024665 920__ $$lyes
001024665 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001024665 980__ $$ajournal
001024665 980__ $$aVDB
001024665 980__ $$aUNRESTRICTED
001024665 980__ $$aI:(DE-Juel1)INM-7-20090406
001024665 9801_ $$aFullTexts