65 lines
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3.3 KiB
Markdown
65 lines
No EOL
3.3 KiB
Markdown
---
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title: A reproducible and generalizable software workflow for analysis of large-scale neuroimaging
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data collections using BIDS Apps
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persons:
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- orcid:0000-0003-3456-2493
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- simon-eickhoff
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- michael-hanke
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topics:
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- research-software-engineering
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- neuroimaging
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- research-data-management
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- distributed-systems
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- high-throughput-computing
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params:
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graphRootNodePID: xyzrins:publications/a2c0e912-ae8e-48b4-aac5-d023afb4a048
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pid: xyzrins:publications/a2c0e912-ae8e-48b4-aac5-d023afb4a048
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doi: 10.1162/imag_a_00074
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date: 2024-01
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title: A reproducible and generalizable software workflow for analysis of large-scale
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neuroimaging data collections using BIDS Apps
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description: "Neuroimaging research faces a crisis of reproducibility. With massive\
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\ sample sizes and greater data complexity, this problem becomes more acute. Software\
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\ that operates on imaging data defined using the Brain Imaging Data Structure (BIDS)\u2014\
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the BIDS App\u2014has provided a substantial advance. However, even using BIDS Apps,\
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\ a full audit trail of data processing is a necessary prerequisite for fully reproducible\
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\ research. Obtaining a faithful record of the audit trail is challenging\u2014\
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especially for large datasets. Recently, the FAIRly big framework was introduced\
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\ as a way to facilitate reproducible processing of large-scale data by leveraging\
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\ DataLad\u2014a version control system for data management. However, the current\
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\ implementation of this framework was more of a proof of concept, and could not\
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\ be immediately reused by other investigators for different use cases. Here, we\
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\ introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python\
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\ package for reproducible image processing at scale. BABS facilitates the reproducible\
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\ application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly\
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\ big framework, BABS tracks the full audit trail of data processing in a scalable\
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\ way by automatically preparing all scripts necessary for data processing and version\
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\ tracking on high performance computing (HPC) systems. Currently, BABS supports\
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\ jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious\
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\ set of programs. To demonstrate its scalability, we applied BABS to data from\
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\ the Healthy Brain Network (HBN; n = 2,565). Taken together, BABS allows reproducible\
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\ and scalable image processing and is broadly extensible via an open-source development\
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\ model."
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kind: bibo:AcademicArticle
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author:
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- pid: orcid:0000-0003-3456-2493
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given_name: Yaroslav
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family_name: Halchenko
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- pid: xyzrins:persons/simon-eickhoff
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given_name: Simon
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family_name: Eickhoff
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- pid: xyzrins:persons/michael-hanke
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given_name: Michael
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family_name: Hanke
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topic:
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- pid: xyzrins:topics/research-software-engineering
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display_label: Research software engineering (RSE)
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- pid: xyzrins:topics/neuroimaging
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display_label: Neuroimaging
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- pid: xyzrins:topics/research-data-management
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display_label: Research data management (RDM)
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- pid: xyzrins:topics/distributed-systems
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display_label: Distributed systems
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- pid: xyzrins:topics/high-throughput-computing
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display_label: High-throughput computing
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--- |