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Vincent Michel

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YNIMG Journal 2017 Journal Article

The Brainomics/Localizer database

  • Dimitri Papadopoulos Orfanos
  • Vincent Michel
  • Yannick Schwartz
  • Philippe Pinel
  • Antonio Moreno
  • Denis Le Bihan
  • Vincent Frouin

The Brainomics/Localizer database exposes part of the data collected by the in-house Localizer project, which planned to acquire four types of data from volunteer research subjects: anatomical MRI scans, functional MRI data, behavioral and demographic data, and DNA sampling. Over the years, this local project has been collecting such data from hundreds of subjects. We had selected 94 of these subjects for their complete datasets, including all four types of data, as the basis for a prior publication; the Brainomics/Localizer database publishes the data associated with these 94 subjects. Since regulatory rules prevent us from making genetic data available for download, the database serves only anatomical MRI scans, functional MRI data, behavioral and demographic data. To publish this set of heterogeneous data, we use dedicated software based on the open-source CubicWeb semantic web framework. Through genericity in the data model and flexibility in the display of data (web pages, CSV, JSON, XML), CubicWeb helps us expose these complex datasets in original and efficient ways.

Details DOI

JMLR Journal 2011 Journal Article

Scikit-learn: Machine Learning in Python

  • Fabian Pedregosa
  • Gaël Varoquaux
  • Alexandre Gramfort
  • Vincent Michel
  • Bertrand Thirion
  • Olivier Grisel
  • Mathieu Blondel
  • Peter Prettenhofer

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2011. ( edit, beta )

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