diff --git a/README.md b/README.md index cdabffa..3d95a1c 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,7 @@ Illustrative movie-viewing analysis: an HTFA template (K = 50) fitted to 12 adults watching Pixar's Partly Cloudy, from Nilearn's development_fmri dataset, viewed from the left, above and right. Inter-subject functional correlation (ISFC) measures correlations between participants' factor time courses at matching moments of the film. Edges show the strongest 5% of factor pairs (|ISFC| ≥ 0.137; red positive, blue negative). Node size represents factor width; node colour identifies communities in the positive-ISFC graph. See the ISFC tutorial for the analysis workflow. [![PyPI version](https://img.shields.io/pypi/v/htfa.svg)](https://pypi.org/project/htfa/) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.23772196.svg)](https://doi.org/10.5281/zenodo.23772196) [![tests](https://github.com/ContextLab/htfa/actions/workflows/tests.yml/badge.svg)](https://github.com/ContextLab/htfa/actions/workflows/tests.yml) [![Documentation Status](https://readthedocs.org/projects/htfa/badge/?version=latest)](https://htfa.readthedocs.io/en/latest/?badge=latest) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/ContextLab/htfa/blob/main/LICENSE)