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In the implementation and use of research information systems (RIS) inscientific institutions, text data mining and semantic technologies are a keytechnology for the meaningful use of large amounts of data. It is not thecollection of data that is difficult, but the further processing andintegration of the data in RIS. Data is usually not uniformly formatted andstructured, such as texts and tables that cannot be linked. These includevarious source systems with their different data formats such as project andpublication databases, CERIF and RCD data model, etc. Internal and externaldata sources continue to develop. On the one hand, they must be constantlysynchronized and the results of the data links checked. On the other hand, thetexts must be processed in natural language and certain information extracted.Using text data mining, the quality of the metadata is analyzed and thisidentifies the entities and general keywords. So that the user is supported inthe search for interesting research information. The information age makes iteasier to store huge amounts of data and increase the number of documents onthe internet, in institutions' intranets, in newswires and blogs isoverwhelming. Search engines should help to specifically open up these sourcesof information and make them usable for administrative and research purposes.Against this backdrop, the aim of this paper is to provide an overview of textdata mining techniques and the management of successful data quality for RIS inthe context of open data and open science in scientific institutions andlibraries, as well as to provide ideas for their application. In particular,solutions for the RIS will be presented.

Text data mining and data quality management for research information systems in the context of open data and open science