Research information in the light of artificial intelligence: quality and data ecologies

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.