Science has become a data-intensive enterprise. Data sets are commonly being stored in public data repositories and are thus available for others to use in new, often unexpected ways. Such re-use of data sets can take the form of reproducing the original analysis, analyzing the data in new ways, or combining multiple data sets into new data sets that are analyzed still further. A scientist who re-uses a data set collected by another must be able to assess its trustworthiness. This chapter reviews the types of errors that are found in metadata referring to data collected manually, data collected by instruments (sensors), and data recovered from specimens in museum collections. It also summarizes methods used to screen these types of data for errors. It stresses the importance of ensuring that metadata associated with a data set thoroughly document the error prevention, detection, and correction methods applied to the data set prior to publication.