Latest revision as of 00:43, 23 March 2021

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.

Design Type(s)data integration objective • database creation objectiveMeasurement Type(s)brainTechnology Type(s)magnetic resonance imagingFactor Type(s)Voxel SizeSample Characteristic(s)Homo sapiens • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)

Document type: Preprint

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The different versions of the original document can be found in:

• http://dx.doi.org/10.1038/s41597-019-0073-y under the license https://creativecommons.org/licenses/by-nc-nd

• http://dx.doi.org/10.31234/osf.io/y82n7 under the license https://creativecommons.org/licenses/by/4.0

• http://europepmc.org/articles/PMC6533280 under the license https://creativecommons.org/licenses/by/4.0/legalcode

• http://hdl.handle.net/11336/106100

• https://psyarxiv.com/y82n7

• https://scholarworks.iupui.edu/bitstream/1805/20359/1/41597_2019_Article_73.pdf

• https://www.nature.com/articles/s41597-019-0073-y.pdf under the license cc-by-nc-nd

• http://link.springer.com/article/10.1038/s41597-019-0073-y,

https://doaj.org/toc/2052-4463 under the license cc-by

• http://www.nature.com/articles/s41597-019-0073-y.pdf,

http://www.nature.com/articles/s41597-019-0073-y,

http://dx.doi.org/10.1038/s41597-019-0073-y

• https://www.ncbi.nlm.nih.gov/pubmed/31123325,

http://www.nature.com/articles/s41597-019-0073-y,

https://www.nature.com/articles/s41597-019-0073-y.pdf,

https://indiana.pure.elsevier.com/en/publications/the-open-diffusion-data-derivatives-brain-data-upcycling-via-inte,

https://academic.microsoft.com/#/detail/2946239819 under the license https://creativecommons.org/licenses/by/4.0

DOIS: 10.1038/s41597-019-0073-y 10.31234/osf.io/y82n7