The MegaVeridicality Dataset is a collection of ordinal veridicality judgments as well as ordinal acceptability judgments for 773 clause-embedding verbs of English. It was created by Aaron Steven White and Kyle Rawlins. The dataset is used to study the complex array of inferences that different open-class lexical items trigger. For example, it examines why certain sentences give rise to specific inferences while structurally identical sentences trigger different inferences. The dataset also investigates how lexically triggered inferences are conditioned by surprising aspects of the syntactic context in which a word occurs. It provides a detailed description of item construction, and collection methods, and discusses how to use a dataset on this scale to address questions in linguistic theory.