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Contributing

One of the wonderful aspects of both the neuroimaging and Python scientific computing communities is the strong commitment to developing and sharing knowledge and tools within the broader community. The goal of this project is to provide a resource for people to learn about how to analyze naturalistic neuroimaging data. We try to incorporate as much open content as we can find that contributes to this goal. Please let us know if we have inadvertently omitted credit for any content generated by others. Though this course was originally developed for an OHBM educational workshop, we welcome contributions from anyone in the broader imaging community. In particular, we welcome tutorials with new methods, recorded talks of applications of interesting methods to naturalistic data, and corrections to any of our materials.

Getting Started

The naturalistic_data_analysis project is hosted on github. If you have any questions, comments, or suggestions, please open an issue.

If you notice any mistakes or have idea for new content, please either open an issue or submit a pull request for us to review.

The website is built using jupyter book, which creates a jekyll website from markdown and jupyter notebooks. Please read their materials to learn more about this neat resource.

License for this book

All content in this book (ie, any files and content in the content/ folder) is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.