Data cleaning is a process used to determine inaccurate, incomplete, or unreasonable data and then improving the quality through correction of detected errors and omissions. The process may include format checks, completeness checks, reasonableness checks, limit checks, review of the data to identify outliers (geographic, statistical, temporal or environmental) or other errors, and assessment of data by subject area experts (e.g. taxonomic specialists). These processes usually result in flagging, documenting and subsequent checking and correction of suspect records. Validation checks may also involve checking for compliance against applicable standards, rules, and conventions.