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.

The knowledge of data quality and the quality of the associated information, including metadata, is critical for data use and reuse. Assessment of data and metadata quality is key for ensuring credible available information, establishing a foundation of trust between the data provider and various downstream users, and demonstrating compliance with requirements established by funders and federal policies. Data quality information should be consistently curated, traceable, and adequately documented to provide sufficient evidence to guide users to address their specific needs. The quality information is especially important for data used to support decisions and policies, and for enabling data to be truly findable, accessible, interoperable, and reusable (FAIR). Clear documentation of the quality assessment protocols used can promote the reuse of quality assurance practices and thus support the generation of more easily-comparable datasets and quality metrics. To enable interoperability across systems and tools, the data quality information should be machine-actionable. Guidance on the curation of dataset quality information can help to improve the practices of various stakeholders who contribute to the collection, curation, and dissemination of data. This presentation introduces international community guidelines to curate data quality information that is consistent with the FAIR principles throughout the entire data life cycle and inheritable by any derivative product. Supportive case studies demonstrate the applicability of the proposed guidelines.