Working with datasets
NiMARE stores meta-analytic data in its
Dataset objects may contain a range of elements, including coordinates (for coordinate-based meta-analysis),
links to statistical maps (for image-based meta-analysis), article text, label weights, and other metadata.
Additionally, NiMARE contains fetching and conversion tools for a number of meta-analytic resources, including Neurosynth, NeuroQuery, NeuroVault, and, to a limited extent, BrainMap. In the examples below, we show what a Dataset can do and exhibit tools for working with data from external meta-analytic resources.
NiMARE implements a number of coordinate- and image-based meta-analysis algorithms in its
In the examples below, we exhibit a range of meta-analyses that can be done with coordinates and/or images in NiMARE.
Annotation tools within NiMARE (
annotate) refer to methods which
assign labels to studies in a Dataset, generally based on study text.
Decoding ROIs and images
Functional characterization analysis refers to methods which use meta-analytic databases to characterize, or “decode”, brain regions or statistical maps in terms of tasks and/or mental processes. For more information about functional characterization analysis, see Meta-analytic functional decoding.