neuroimaging

In augmentation, elastic_transform, it only applies a random transform on one input image array. I would think to be used for training, the image and mask pair should be transform in the same way. However, this single-input-image, single-output-image method makes it very inconvenient. Could we deform a list of images (np.arrays) using the same transformation in this method ? Thanks!

Convert references in nilearn/regions/region_extractor.py to use footcite / footbibliography.
See #2759

The date and time on the output of the running docker container isn't correct, adding a volume "-v /etc/localtime:/etc/localtime:ro" might fix this?

This would help me know how long a process

Is your feature request related to a problem? Please describe.

When a release is being created, the checklist associated with the issue documenting progress is manually crafted. For example, see Slicer/Slicer#5217 and Slicer/Slicer#5489.

While the steps are already documented on the wiki (see [here](https://www.slicer.org/wiki/Documentat

Some is already in .gitignore, but some is not. We should check all examples.

This was originally part of #772, but @effigies pointed out that slipping in content changes with a formatting change is not a great idea, so we should do this separately. However, it should be done after #772 is merged.

Stems from https://github.com/bids-standard/bids-specification/pull/772/files#r669693695.

Following our work on migrating interactive prompts to Click, some shortcomings regarding the current user experience wrt to interactivity surfaced.

Some pipelines provide the caller with the ability to set some parameters in an interactive fashion, which does not play nice with cluster usage where prompts may hang execution indefinitely. One pi

Currently the help on commandline is printed correctly if -h is added at the end of the command or not (depending on the command).

The main function could be improved to the and subparsers.required = True removed to homogeneize everything.

It seems likely that NIMADS datasets will be stored in compressed json files, so we should ensure that NiMARE Datasets can load from those in addition to standard jsons.