The amount of data, defined as a "reinterpretable representation of information in a formalized manner, suitable for communication, interpretation, or processing" [1] is constantly increasing in varied institutions. Particularly affected is the amount of research information (such as publication data, personal data, project data, third-party funded data, etc.) in universities and research institutions. This means that research results can not only be verified and interpreted, but it must be understood how these results came about and how they can be used. As the preparation, utilization and preservation of a wide variety of research information has always been an important core task for these institutions and their libraries, as they can take over the organization of all information about the data stocks and their secure longterm archiving. The usefulness of useful research information depends very much on the quality of the data, recorded there. Nowadays, the topic of data quality (DQ) is becoming therefore more and more important both in theory and practice. This is not surprising, since securing and improving it is playing an increasingly important role, especially in the course of rapidly growing data stocks and the increasing use of RIM. Data quality is defined as properties of data in relation to their ability to meet specified requirements [2,3]. To ensure a high level of DQ, scientifically proven methods and procedures are required.

Different research institutions use research information for different purposes. Data analyses and reports based on current research information systems (CRIS) provide information about the research activities and their results. As a rule, management and controlling utilize the research information from the CRIS for reporting. For example, trend analysis helps with business strategy decisions or rapid ad-hoc analysis to respond effectively to short-term moves. Ultimately, the analysis results and the resulting interpretations and decisions depend directly on the quality of the data.