Working with datasets

NiMARE stores meta-analytic data in its Dataset class. 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.

Performing meta-analyses

NiMARE implements a number of coordinate- and image-based meta-analysis algorithms in its meta module. In the examples below, we exhibit a range of meta-analyses that can be done with coordinates and/or images in NiMARE.

For more information about the components that go into coordinate-based meta-analyses in NiMARE, see Coordinate-based meta-analysis in NiMARE, as well as Outputs of NiMARE.

Annotating Datasets

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.

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