{"version":"1.0","type":"rich","title":"Gist 0c28ed2b6d7235146517","description":"This example visualizes intraclass correlations for within- and between-site reliability of brain volume estimates from Freesurfer and SIENAX across multicenter MS study scans. It presents a pandas DataFrame table of scanning parameters and reliability metrics for nine scanners at six sites. The code uses IPython’s `%pylab inline` for interactive plotting, pandas for tabular data handling, and nipype’s `loadpkl` to load the analysis results. The visualization is built in a Jupyter notebook with Vue integration, showing how heterogeneous acquisition protocols affect measurement consistency.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-0c28ed2b6d7235146517?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}