@article{
 title = {The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services},
 type = {article},
 year = {2019},
 keywords = {Brain imaging,Cognitive neuroscience,Computational science,Magnetic resonance imaging,Network models},
 pages = {69},
 volume = {6},
 websites = {http://www.nature.com/articles/s41597-019-0073-y},
 month = {12},
 publisher = {Nature Publishing Group},
 day = {23},
 id = {65ec6c82-9364-3b53-b87f-1f2bff6c2d8e},
 created = {2019-10-01T17:21:28.091Z},
 accessed = {2019-08-14},
 file_attached = {true},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2020-05-11T14:43:26.404Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Avesani2019},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Avesani, Paolo and McPherson, Brent and Hayashi, Soichi and Caiafa, Cesar F. and Henschel, Robert and Garyfallidis, Eleftherios and Kitchell, Lindsey and Bullock, Daniel and Patterson, Andrew and Olivetti, Emanuele and Sporns, Olaf and Saykin, Andrew J. and Wang, Lei and Dinov, Ivo and Hancock, David and Caron, Bradley and Qian, Yiming and Pestilli, Franco},
 doi = {10.1038/s41597-019-0073-y},
 journal = {Scientific Data},
 number = {1}
}

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