Neuroimaging, Genetics, and Clinical Data Sharing in Python Using the CubicWeb Framework. Grigis, A., Goyard, D., Cherbonnier, R., Gareau, T., Papadopoulos Orfanos, D., Chauvat, N., Di Mascio, A., Schumann, G., Spooren, W., Murphy, D., & Frouin, V. Frontiers in Neuroinformatics, 11:18, 2017. doi abstract bibtex In neurosciences or psychiatry, the emergence of large multi-center population imaging studies raises numerous technological challenges. From distributed data collection, across different institutions and countries, to final data publication service, one must handle the massive, heterogeneous, and complex data from genetics, imaging, demographics, or clinical scores. These data must be both efficiently obtained and downloadable. We present a Python solution, based on the CubicWeb open-source semantic framework, aimed at building population imaging study repositories. In addition, we focus on the tools developed around this framework to overcome the challenges associated with data sharing and collaborative requirements. We describe a set of three highly adaptive web services that transform the CubicWeb framework into a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform endowed with massive-download capabilities. Two major European projects, IMAGEN and EU-AIMS, are currently supported by the described framework. We also present a Python package that enables end users to remotely query neuroimaging, genetics, and clinical data from scripts.
@article{grigis_neuroimaging_2017,
title = {Neuroimaging, {Genetics}, and {Clinical} {Data} {Sharing} in {Python} {Using} the {CubicWeb} {Framework}},
volume = {11},
issn = {1662-5196},
doi = {10.3389/fninf.2017.00018},
abstract = {In neurosciences or psychiatry, the emergence of large multi-center population imaging studies raises numerous technological challenges. From distributed data collection, across different institutions and countries, to final data publication service, one must handle the massive, heterogeneous, and complex data from genetics, imaging, demographics, or clinical scores. These data must be both efficiently obtained and downloadable. We present a Python solution, based on the CubicWeb open-source semantic framework, aimed at building population imaging study repositories. In addition, we focus on the tools developed around this framework to overcome the challenges associated with data sharing and collaborative requirements. We describe a set of three highly adaptive web services that transform the CubicWeb framework into a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform endowed with massive-download capabilities. Two major European projects, IMAGEN and EU-AIMS, are currently supported by the described framework. We also present a Python package that enables end users to remotely query neuroimaging, genetics, and clinical data from scripts.},
language = {eng},
journal = {Frontiers in Neuroinformatics},
author = {Grigis, Antoine and Goyard, David and Cherbonnier, Robin and Gareau, Thomas and Papadopoulos Orfanos, Dimitri and Chauvat, Nicolas and Di Mascio, Adrien and Schumann, Gunter and Spooren, Will and Murphy, Declan and Frouin, Vincent},
year = {2017},
pmid = {28360851},
pmcid = {PMC5352661},
keywords = {Python, data sharing, database, genetics, medical informatics, neuroimaging, web service},
pages = {18},
}
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