In this paper, we survey literature on grounding in Answer Set Programming (ASP) and related fields, analyse the common benchmarks used for this purpose, and introduce a new grounding benchmark called DIRT. In ASP, reasoning engines typically rely on a “ground-and-solve” approach, in which a high-level description of a problem domain (e.g., an Answer Set Program) is first transformed into a low-level description (e.g., aspif) in order to solve. This process, better known as grounding, has a significant effect on the overall speed of the reasoning engine. For this reason, literature contains numerous works dedicated to optimizing various aspects of the grounding process. However, each paper tends to measure their improvements on distinct benchmarks, making a direct comparison between works often difficult. We argue that this is caused by a lack of standardized benchmarks for grounding, and substantiate this claim through a survey of grounding literature. Based on this survey, we have distilled the Dataset for Instantiating in Reasoning Tools (DIRT) as a specialized grounding benchmark. We provide encodings for ASP and ASP-like formats, and present their baseline performance on this problem set. In this way, our benchmark suite can help identify bottlenecks in state-of-the-art grounders, and can serve as a standardized dataset for future works on grounding.