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RASPNet is a large-scale dataset for radar adaptive signal processing (RASP) applications, aimed at supporting the development of data-driven models within the radar community. It consists of 100 realistic radar scenarios compiled over a variety of topographies and land types from across the contiguous United States, each comprising 10,000 realizations of clutter returns from an airborne radar setting. RASPNet intends to fill a prominent gap in the availability of a large-scale, realistic dataset that standardizes the evaluation of adaptive radar processing techniques. The dataset is publicly available.