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. 2016 Jul 22;10:27. doi: 10.3389/fninf.2016.00027

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Copyright © 2016 Oosterhof, Connolly and Haxby.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Flattening and unflattening datasets. A (tiny) fMRI dataset (A) with three spatial dimensions (left) is unflattened along the feature dimension using cosmo_unflatten. Each sample (row) in samples results in a three-dimensional array (right). (B) a dataset containing representational dissimilarities for five conditions is unflattened along the sample dimension. Each column in samples result in a dissimilarity matrix. The result from unflattening a dataset can be reversed in both (A,B) using cosmo_flatten. Note that dataset input/output functions for volumetric, surface-based, and M/EEG data (Figure 10) use the flattening and unflattening operations internally.