People

This work is made available by a community of people, including:

• Bertrand Thirion

• Denis Fouchard

• Elizabeth DuPre

• Hugo Richard

• Kshitij Chawla

• Pierre-Louis Barbarant

• Thomas Bazeille

Funding

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 (HBP SGA2) and Digiteo.

Citing Nilearn and scikit-learn

This package can be lightweight and efficient because it relies on great toolboxes, notably nilearn and scikit-learn.

A huge amount of work goes into both of these packages. Researchers who invest their time in developing and maintaining the package deserve recognition with citations. In addition, the INRIA MIND Project Team needs citations to the paper in order to justify paying a software engineer on the project. To guarantee the future of the toolkit, if you use it, please cite it.

Citing nilearn

If you want to cite Nilearn, we suggest you do it using our Zenodo DOI:

@software{Nilearn,
    author = {Nilearn contributors},
    license = {BSD-4-Clause},
    title = {{nilearn}},
    url = {https://github.com/nilearn/nilearn},
    doi = {https://doi.org/10.5281/zenodo.8397156}
}

Nilearn’s Research Resource Identifier (RRID) is: RRID:SCR_001362

There is no paper published about nilearn. However, the patterns underlying the package have been described in: Machine learning for neuroimaging with scikit-learn.

Citing scikit-learn

To cite scikit-learn, please see the scikit-learn documentation on how to cite.