The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

Paolo Avesani, Brent McPherson, Soichi Hayashi, Cesar F. Caiafa, Robert Henschel, Eleftherios Garyfallidis, Lindsey Kitchell, Daniel Bullock, Andrew Patterson, Emanuele Olivetti, Olaf Sporns, Andrew Saykin, Lei Wang, Ivo Dinov, David Hancock, Bradley Caron, Yiming Qian, Franco Pestilli

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.

Original languageEnglish (US)
Number of pages1
JournalScientific data
Volume6
Issue number1
DOIs
StatePublished - May 23 2019

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Brain
brain
Pipelines
Derivatives
Derivative
Processing
Neuroimaging
Magnetic resonance
Signal to noise ratio
computer scientist
Imaging techniques
Integrated
Fibers
Magnetic Resonance Imaging
Reproducibility
scientific community
open access
Open Source
Repository
Reuse

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

Cite this

The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. / Avesani, Paolo; McPherson, Brent; Hayashi, Soichi; Caiafa, Cesar F.; Henschel, Robert; Garyfallidis, Eleftherios; Kitchell, Lindsey; Bullock, Daniel; Patterson, Andrew; Olivetti, Emanuele; Sporns, Olaf; Saykin, Andrew; Wang, Lei; Dinov, Ivo; Hancock, David; Caron, Bradley; Qian, Yiming; Pestilli, Franco.

In: Scientific data, Vol. 6, No. 1, 23.05.2019.

Research output: Contribution to journalArticle

Avesani, P, McPherson, B, Hayashi, S, Caiafa, CF, Henschel, R, Garyfallidis, E, Kitchell, L, Bullock, D, Patterson, A, Olivetti, E, Sporns, O, Saykin, A, Wang, L, Dinov, I, Hancock, D, Caron, B, Qian, Y & Pestilli, F 2019, 'The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services', Scientific data, vol. 6, no. 1. https://doi.org/10.1038/s41597-019-0073-y
Avesani, Paolo ; McPherson, Brent ; Hayashi, Soichi ; Caiafa, Cesar F. ; Henschel, Robert ; Garyfallidis, Eleftherios ; Kitchell, Lindsey ; Bullock, Daniel ; Patterson, Andrew ; Olivetti, Emanuele ; Sporns, Olaf ; Saykin, Andrew ; Wang, Lei ; Dinov, Ivo ; Hancock, David ; Caron, Bradley ; Qian, Yiming ; Pestilli, Franco. / The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. In: Scientific data. 2019 ; Vol. 6, No. 1.
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