**1. Introduction**

Over the past decades, research organizations, administrations and researchers have been collecting data that describe both the input as well as the output side of research. This has resulted in an enormous pile of data on publications, projects, patents, … researchers and their organizations that are collected within database systems or current research information systems (CRIS). Such data systems are created according to specific goals and use purposes of individual organizations, which reflects their specific nature and the surrounding context in which they operate. However, over time these data systems, institutions as well as the research ecosystem at large have evolved, thereby potentially threatening the quality of the collected data and the resulting data analyses, particularly if no formal data quality management policy is being implemented. This chapter introduces the readers into the concept of data quality and provides methods to assess and improve data quality, in order to obtain data that can be used as a reliable source for quantitative and qualitative measurements of research.
