Data that are FAIR demonstrate specific characteristics including: ease of discovery, ability to access, community acceptable formats allowing interoperability, and information that supports the decision for reuse. The process used to determine data reuse is commonly called “fit for purpose” or “fit for use”. These criteria are defined using relevant factors established by the community for which the data was originally created, and also a “best effort” for criteria needed by other research communities. The FAIR Data Principles support robust documentation of datasets to include the necessary information for reuse. An important part of that documentation, or metadata, is clear documentation of the quality and uncertainty related to the data being considered. When this information is not complete, data has a higher tendency of being used incorrectly leading to inaccurate research, rejected papers, or even retracted papers. The importance of data creators to make their data FAIR – including uncertainty information – directly improves the transparency and integrity of our science today and into the future.