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We present Scalable Insets, a technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visual spaces such as gigapixel images, matrices, or maps. Exploration of many but sparsely-distributed patterns in multiscale visual spaces is challenging as visual representations change across zoom levels, context and navigational cues get lost upon zooming, and navigation is time consuming. Our technique visualizes annotated patterns too small to bedoi:10.1101/301036 fatcat:rlrbc2m6tnecjjszhltoawa7gq