A data quality checklist is a list of possible issues with a dataset. This list can be created before you start exploring your data to help streamline your data cleaning. If there are physical or logical boundaries that your data should conform to, such as humidity not being above 100%, or age always being 0 or greater, these can form part of your checklist. As such, there is often a strong relationship between a data quality checklist and the data dictionary for your dataset.