<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/c269b015379f4a5a9cf9b053045d4291&quot; frameborder=&quot;0&quot; width=&quot;1908&quot; height=&quot;1431&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1431</height><width>1908</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1431</thumbnail_height><thumbnail_width>1908</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/c269b015379f4a5a9cf9b053045d4291-89afb1da514d4772.gif</thumbnail_url><duration>222.93</duration><title>Interpreting Heatmaps in Rankings Datasets</title><description>In this video, I explain how to interpret the heat map from our rankings datasets. The heat map uses a gradient where brighter yellow indicates top performance and darker brown shows lower performance. I walk you through the various controls available, such as selecting rank, percentile, or score, and how to customize the view by choosing benchmark peers and periods. Additionally, I highlight the option to copy data to clipboard or download it as a CSV, even without full dataset access. Please explore these features to enhance your analysis of university performance over time.</description></oembed>