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Open AccessArticle

Text and Data Quality Mining in CRIS

German Center for Higher Education Research and Science Studies (DZHW), Schützenstraße 6a, 10117 Berlin, Germany
Information 2019, 10(12), 374; https://doi.org/10.3390/info10120374
Received: 23 September 2019 / Revised: 7 November 2019 / Accepted: 25 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Quality of Open Data)
To provide scientific institutions with comprehensive and well-maintained documentation of their research information in a current research information system (CRIS), they have the best prerequisites for the implementation of text and data mining (TDM) methods. Using TDM helps to better identify and eliminate errors, improve the process, develop the business, and make informed decisions. In addition, TDM increases understanding of the data and its context. This not only improves the quality of the data itself, but also the institution’s handling of the data and consequently the analyses. This present paper deploys TDM in CRIS to analyze, quantify, and correct the unstructured data and its quality issues. Bad data leads to increased costs or wrong decisions. Ensuring high data quality is an essential requirement when creating a CRIS project. User acceptance in a CRIS depends, among other things, on data quality. Not only is the objective data quality the decisive criterion, but also the subjective quality that the individual user assigns to the data. View Full-Text
Keywords: current research information systems (CRIS); research information; text and data mining (TDM); data quality; knowledge exploration; knowledge transfer; decision making; user acceptance current research information systems (CRIS); research information; text and data mining (TDM); data quality; knowledge exploration; knowledge transfer; decision making; user acceptance
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Azeroual, O. Text and Data Quality Mining in CRIS. Information 2019, 10, 374.

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