Data quality is commonly defined as fitness for use. The problem of identifying the quality of data is faced by many data consumers. To make the task of finding good quality datasets more efficient, we introduce the Dataset Quality Ontology (daQ). The daQ is a lightweight, extensible vocabulary for attaching the results of quality benchmarking of a linked open dataset to that dataset. We discuss the design considerations, give examples for extending daQ by custom quality metrics, and present use cases such as browsing data sets by quality. We also discuss how tools can use the daQ to enable consumers find the right dataset for use.