Repeatedly, we find ourselves in need of software tools to implement ideas and research projects. This can be software to help deal with large amounts of data or the implementation of specific algorithms. Typically, such software tools are tailored to an individual project and are, consequently, not very interesting for other groups. However, over the years, we have created a number of software projects that specifically target a broader audience and have reached significant adoption. This page offers a compact overview of such projects — all of which are Free and Open Source.

NeuroDebian is a complete software platform for neuroscientific research (MRI-based research in particular). NeuroDebian achieves its unique combination of versatility and stability by integrating all relevant software components with the Debian operating system. At the same time, NeuroDebian can be used with all popular operating systems such as MS Windows, Mac OS X, as well as Linux-based computers. NeuroDebian is used on thousands of machines world-wide on a daily basis.

An article on the scope and implementation of NeuroDebian is published in Frontiers in Neuroinformatics. More information on NeuroDebian is available at

PyMVPA is a toolbox for multivariate pattern analysis of neuroscientific data. It is a Python-based, platform-independent, and open-source solution that aids the application of algorithms from machine learning research to data from various modalities. Since its first publication in 2009, PyMVPA has been employed in studies performed by numerous research groups.

An overview article on PyMVPA has been published in the journal Frontiers in Neuroinformatics. More information on PyMVPA, including a detailed tutorial, are available at

DataLad aims to provide access to scientific data available from various sources (e.g. lab or consortium websites such as Human Connectome; data sharing portals such as OpenfMRI and CRCNS) through a single convenient interface and integrated with your software package managers (such as APT in Debian). Although initially targeting neuroimaging and neuroscience data in general, it is designed for broader use and we welcome a wide range of contributions from other domains.

PyNIfTI was one of the first packages to access data in the NIfTI format with the Python programming language. In the meantime, NiBabel has absorbed and superseded PyNIfTI, offering support for many more file formats. More information on NiBabel is available at