Nilearn: Statistics and Machine Learning for Neuroimaging in Python #21
Description
Title
Nilearn: Statistics and Machine Learning for Neuroimaging in Python
Short description and the goals for the OHBM BrainHack
Nilearn is an open-source Python package for fast and easy analysis and visualization of MRI brain images. It provides statistical and machine-learning tools, with instructive documentation and a friendly community. It includes applications such as multi-voxel pattern analysis (MVPA), decoding, predictive modelling, functional connectivity, and brain parcellations.
Recently, we did some analysis of the package's usage (https://github.com/nilearn/poia) and found out that "nilearn.plotting" is the most used module. Therefore, we want to dedicate this year's Brainhack to improving the plotting module by resolving existing issues, welcoming new ones, and improving the documentation. To this end, we encourage users to simply filter the issues by the "Plotting" label (https://github.com/nilearn/nilearn/labels/Plotting). In addition, if you are motivated to work on any other issues, we welcome that as well! We have a lot of open issues in the repository, and we would love to see contributions from the community. New contributors should look for the "Good First Issue" label to get started (https://github.com/nilearn/nilearn/labels/Good%20first%20issue).
Link to the Project
https://github.com/nilearn/nilearn
Image/Logo for the OHBM brainhack website
https://drive.google.com/file/d/1c2AcPvCCRWy80Se2m_lfVIlJc_1laon2/view?usp=sharing
Project lead
Himanshu Aggarwal, Github: @man-shu, Discord: man_shooo
Main Hub
Brisbane
Link to the Project pitch
No response
Other hubs covered by the leaders
- Brisbane
- Hybrid (Asia / Pacific)
- Hybrid (Europe / Middle East / Africa)
- Hybrid (Americas)
Skills
We welcome all users and contributions from various skill sets and levels. This can include opening discussions around improvements to the documentation and/or code base, answering or commenting on questions or issues raised on github and neurostars, reviewing pull requests, and contributing code.
Recommended tutorials for new contributors
We recommend starting with Nilearn's basic tutorials and the introduction to Nilearn. This would help new contributors get familiar with the package and its functionalities. They can even provide feedback on the tutorials and suggest improvements.
Good first issues
We have a "Good first issues" label and the list of issues can be found here
Depending upon your comfort level with the package, issues can also be filtered as follows:
Twitter summary
(We're not on Twitter anymore, we would prefer if you post on Bluesky)
This year at #OHBM_Brainhack_2025 in Brisbane, Australia, @nilearn.bsky.social wants to improve the plotting module of Nilearn by resolving existing issues, welcoming new ones, and improving the documentation. Join us in our efforts from June 21th to 23rd 2025.
Short name for the Discord chat channel (~15 chars)
nilearn
Please read and follow the OHBM Code of Conduct
- I agree to follow the OHBM Code of Conduct during the hackathon