The Canadian Open Neuroscience Platform—An open science framework for the neuroscience community

Harding, Rachel; Bermudez, Patrick; Bernier, Alexander; Beauvais, Michael J.S.; Bellec, Pierre; Hill, Sean; Karakuzu, Agâh; Knoppers, Bartha Maria; Pavlidis, Paul; Poline, Jean‐Baptiste; Roskams, Jane; Stikov, Nikola; Stone, Jessica; Strother, Stephen C.; Evans, Alan C.

doi:10.1371/journal.pcbi.1011230

Cited by 9 publications

(3 citation statements)

References 44 publications

(55 reference statements)

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“…For more information on integrated research objects (e.g., NRPs) that bundle narrative and executable content for reproducible and transparent publications, please refer to (DuPre et al, 2022). NeuroLibre is sponsored by the Canadian Open Neuroscience Platform (CONP) (Harding et al, 2023).…”

Section: Notementioning

confidence: 99%

Paper is not enough: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge

Boudreau,

Karakuzu,

Cohen-Adad

et al. 2024

Preprint

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We present the results of the ISMRM 2020 joint Reproducible Research and Quantitative MR study groups reproducibility challenge on T1 mapping in phantom and human brain. T1 mapping, a widely used quantitative MRI technique, exhibits inconsistent tissue-specific values across protocols, sites, and vendors. The challenge aimed to assess the reproducibility of a well-established inversion recovery T1 mapping technique, with acquisition details published solely as a PDF, on a standardized phantom and in human brains. Participants acquired T1 mapping data from MRIs of three manufacturers at 3T, resulting in 39 phantom datasets and 56 datasets from healthy human subjects. The T1 inter-submission variability was twice as high as the intra-submission variability in both phantoms and human brains, indicating that the acquisition details in the selected paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent Boudreau et al. ( 2024). Paper is not enough: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge. NeuroLibre Reproducible Preprints, 23. https://doi.org/10.55458/neurolibre.00023. Additionally, Kathryn Keenan, Zydrunas Gimbutas, and Andrew Dienstfrey from NIST provided their code to generate the ROI template for the ISMRM/NIST phantom. Dylan Roskams-Edris and Gabriel Pelletier from the Tanenbaum Open Science Institute (TOSI) offered valuable insights and guidance related to data ethics and data sharing in the context of this international multi-center conference challenge. The 2020 RRSG study group committee members who launched the challenge,

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Section: Notementioning

confidence: 99%

Paper is not enough: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge

Boudreau,

Karakuzu,

Cohen-Adad

et al. 2024

Preprint

Self Cite

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“…It should be noted that, although some platforms such as the Canadian Open Neuroscience Platform (CONP; https://conp.ca/;Harding et al, 2023;Poline et al, 2023) may also use DataLad and containerized software (e.g., BIDS Apps) for enhancing reproducibility, with BABS, it is not necessary for users to upload or share the data on another platform; instead, they can directly process the data on the HPC clusters they have access to.…”

mentioning

confidence: 99%

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps

Zhao,

Jarecka,

Covitz

et al. 2023

Preprint

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Neuroimaging 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) - BIDS Apps - have 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.

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“…This jupyter book will allow interested readers to reproduce this analysis, as a proof of concept for reproducible publications using jupyter books and the Neurolibre preprint server. Bellec et al (2023). Parcellating the parcellation issue -a proof of concept for reproducible analyses using Neurolibre.…”

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confidence: 99%

Parcellating the parcellation issue - a proof of concept for reproducible analyses using Neurolibre

Bellec,

Jbabdi,

Craddock

2023

Preprint

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Back in 2017, a special issue on the topic of brain parcellation and segmentation was published in the journal Neuroimage. We acted as guest editors for this special issue, and wrote an editorial (Craddock et al., 2018) providing an overview of all papers, sorted into categories. The categories were generated using a data-driven parcellation analysis, based on the words contained in the abstract of the articles. This jupyter book will allow interested readers to reproduce this analysis, as a proof of concept for reproducible publications using jupyter books and the Neurolibre preprint server. Bellec et al. (2023). Parcellating the parcellation issue -a proof of concept for reproducible analyses using Neurolibre. NeuroLibre Reproducible Preprints, 10.

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The Canadian Open Neuroscience Platform—An open science framework for the neuroscience community

Cited by 9 publications

References 44 publications

Paper is not enough: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge

Paper is not enough: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps

Parcellating the parcellation issue - a proof of concept for reproducible analyses using Neurolibre

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