What topics do we want to cover in this Jupyter book?

The nimare-paper covers a lot of the functionality of NiMARE, but it focuses mostly on the background of the different methods.

Here are just a few thoughts:

1. What is meta-analysis?
2. How is meta-analysis in neuroimaging different from your standard, effect-size meta-analysis?
3. Why would you want to do a meta-analysis? 🤔
4. But where do the data come from? 😕
5. Which package should you use for your meta-analyses and why is it definitely NiMARE?
6. The timeline of a meta-analysis project, from design to execution.
7. Follow-up analyses after you've done the meta-analysis.
   • Decoding.
   • MACMs.
8. A few fancy analyses with meta-analytic data (e.g., MACMs, metaICA, CBP).

For the nimare-paper book, we stored a bunch of data in a zipped folder, uploaded to Google Drive. It contains copies of the Neurosynth and NeuroQuery Dataset objects, because (1) the FIU HPC doesn't have internet access on its processing nodes and (2) the conversion takes a lot of time and memory. The data file also contains a bunch of MA maps and images for decoding and IBMA.

Do we want to limit this book to small meta-analyses (i.e., the type of meta-analysis folks manually compile), or do we also want to show Neurosynth/NeuroQuery meta-analyses and/or decoding?