Published February 7, 2017 | Version v1
Journal article Open

Gentoo Linux for Neuroscience - a replicable, flexible, scalable, rolling-release environment that provides direct access to development software

  • 1. <div>Institute for Biomedical Engineering, ETH and University of Zürich, Zurich, Switzerland</div>
  • 2. <div>Preclinical Laboratory for Translational Research into Affective Disorders, DPPP, Psychiatric Hospital, University of Zurich, Zurich, Switzerland</div>


Gentoo is a GNU/Linux metadistribution designed to maximize and simplify user control of the software environment. All determinants of a Gentoo environment are recorded in a small number of plain-text configuration files, from which the software make-up of the system can be reconstructed entirely. As such, Gentoo constitutes a replicable and transparent software infrastructure - as mandated by research valuing reproducibility. Of equal scientific interest is the flexibility of Gentoo's package management. All software is distributed in a rolling-release fashion, giving the user full control over which versions (including live versions and branches/tags from version control) of which programs to install, and with which compilation options. All of the above is accompanied by automatic, version-aware dependency resolution, which also tracks static library linking and prompts for rebuilds as necessary.

We believe Gentoo is excellently suited to address many of the challenges in neuroscience software management; including: system replicability, system documentation, data analysis reproducibility, fine-grained dependency management, easy control over compilation options, and seamless access to cutting-edge software releases.

We have made a substantial set of neuroimaging and data analysis packages - including their entire dependency stacks - available for any system using Gentoo's Package Management Standard. Neuroscientific software now usable under Gentoo includes but is not limited to:

    Dipy (Garyfallidis et al. 2014) FSL (Jenkinson et al. 2012) Nipype (Gorgolewski et al. 2016) Nilearn (Abraham et al. 2014) PsychoPy (Peirce 2008)

Herein we describe the implementation and current capabilities of this environment, as well as its ability to accelerate and improve research.



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