Princeton Handbook for Reproducible Neuroimaging 📙

This is a living resource on best practices in reproducible neuroimaging for the Princeton Neuroscience Institute. The handbook is rendered on the web here (http://brainhack-princeton.github.io/handbook), and is currently under initial development.

The handbook is intended to be a practical, hands-on guide for running an fMRI experiment. The target audience is graduate students and postdocs planning to start their first fMRI experiment, but the principles and tools may be valuable to veterans as well. The handbook can't cover every use-case or experiment, so we focus on the most typical use-cases.

Contributing

Contributions in any form (pull requests, issues, content requests/ideas) are always welcome! If you notice any irregularities in the handbook, please us know by raising an issue. You can find the recommended workflow for contributing here.

Notes for Instructors

Although the some elements of the handbook are Princeton-specific, we hope that many of the lessons will be generally applicable. You are welcome to use this handbook for instructional purpooses or as a template under the license terms below. Contributes and feedback are welcome and appreciated.

License

CC-BY-SA: You are free to

share - copy and redistribute the material in any medium or format
adapt - remix, transform, and build upon the material for any purpose, even commercially

under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

Acknowledgements and contributors

This handbook is collaboratively authored by attendees of the weekly neuroimaging support group at Princeton (also known as "Pygers"). The structure of this handbook borrows heavily from the DataLad Handbook and we thank the DataLad authors for providing such a useful resource. The initial momentum to create a public resource stemmed from the Brainhack Princeton 2019 event sponsored by the ReproNim Center for Reproducible Neuroimaging Computation, Intel Labs, and the Princeton University Department of Psychology Langfeld Fund. We're especially grateful to Matteo Visconti di Oleggio Castello for providing mentorship at the hackathon and advising development of the handbook.

Contributors