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Cluster thresholding in multi-voxel pattern analysis (MVPA) is an open problem, as many figures of merit are possible, few
(if any) of which have been sufficiently analyzed to permit a parametric solution or a guarantee of compatibility with
pre-computed simulations. Stelzer, et al. 2012 represents probably the
most conservative approach, constructing voxel-wise and then cluster-wise null distributions at the group level, based on
permuting the training labels at the individual level.
OpenNeuro has implemented a data retention policy, stating that datasets that have been in draft state for greater than 28 days may be reverted to the latest snapshot. Unfortunately, we don't currently have an interface for viewing what changes have been made since the last snapshot, so users may not know whether they want to create a new snapshot or not.
This gist shows two ways to view the changes using the OpenNeuro CLI. We will use ds000001 as an example.
Download and diff
The easy but slow approach would be to use the CLI to download two copies of your dataset, the most recent tag and the draft, and run diff -r on the pair:
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Comparing sizes of identical .nii.gz data saved as int16, float32, float64
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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters