Saake, Gunter

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Saake, Gunter

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    Data Quality Measures and Data Cleansing for Research Information Systems
    (Digital Information Research Foundation, 2018-02-01)
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    The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased.
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    Solving problems of research information heterogeneity during integration – using the European CERIF and German RCD standards as examples
    (IOS Press, 2019-09-18)
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    Integrating data from a variety of heterogeneous internal and external data sources (e.g. CERIF and RCD data models with different modeling languages) in a federated database system such as “Research Information Management System (RIMS)” is becoming more challenging for (inter-)national universities and research institutions. Data quality is an important factor for successful integration and interpretation of research information and interoperability of various independent information systems. Before the data is loaded into RIMS, they should be reviewed during data integration process to resolve conflicts between the different data sources and clean the data quality issues. Poor data quality leads to distortion in data presentation, and thus to erroneous basis for decisions. It is ultimately a cost for scientific institutions and it starts with integrating research information into the RIMS. Therefore, the investment in the topic of information integration makes sense insofar, the achievement of a high data quality is of primary importance. This paper presents methods, processes and techniques of information integration in the context of research information management systems. In order to ensure the quality of research information in an institutions data sources during its integration into the RIMS. Numerous attempts have already been done by universities and research institutions to create techniques and solutions for this need.
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    Text data mining and data quality management for research information systems in the context of open data and open science
    (École des Sciences de l’Information (ESI, Rabat, Maroc), 2018-11-28)
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    In the implementation and use of research information systems (RIS) in scientific institutions, text data mining and semantic technologies are a key technology for the meaningful use of large amounts of data. It is not the collection of data that is difficult, but the further processing and integration of the data in RIS. Data is usually not uniformly formatted and structured, such as texts and tables that cannot be linked. These include various source systems with their different data formats such as project and publication databases, CERIF and RCD data model, etc.
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    Item type:Publication,
    Quality of Research Information in RIS Databases: A Multidimensional Approach
    (Springer Nature, 2019-05-18)
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    For the permanent establishment and use of a RIS in universities and academic institutions, it is absolutely necessary to ensure the quality of the research information, so that the stakeholders of the science system can make an adequate and reliable basis for decision-making. However, to assess and improve data quality in RIS, it must be possible to measure them and effectively distinguish between valid and invalid research information. Because research information is very diverse and occurs in a variety of formats and contexts, it is often difficult to define what data quality is. In the context of this present paper, the data quality of RIS or rather their influence on user acceptance will be examined as well as objective quality dimensions (correctness, completeness, consistency and timeliness) to identify possible data quality deficits in RIS. Based on a quantitative survey of RIS users, a reliable and valid framework for the four relevant quality dimensions will be developed in the context of RIS to allow for the enhancement of research information driven decision support.