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Quality Issues of CRIS Data: An Exploratory Investigation with Universities from Twelve Countries

Author

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  • Otmane Azeroual

    (German Center for Higher Education Research and Science Studies (DZHW), 10117 Berlin, Germany
    Institute for Technical and Business Information Systems—Database Research Group, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
    Department of Computer Science and Engineering, University of Applied Science—HTW Berlin, 12459 Berlin, Germany)

  • Joachim Schöpfel

    (GERiiCO Laboratory, University of Lille, 59653 Villeneuve-d’Ascq, France)

Abstract

Collecting, integrating, storing and analyzing data in a database system is nothing new in itself. To introduce a current research information system (CRIS) means that scientific institutions must provide the required information on their research activities and research results at a high quality. A one-time cleanup is not sufficient; data must be continuously curated and maintained. Some data errors (such as missing values, spelling errors, inaccurate data, incorrect formatting, inconsistencies, etc.) can be traced across different data sources and are difficult to find. Small mistakes can make data unusable, and corrupted data can have serious consequences. The sooner quality issues are identified and remedied, the better. For this reason, new techniques and methods of data cleansing and data monitoring are required to ensure data quality and its measurability in the long term. This paper examines data quality issues in current research information systems and introduces new techniques and methods of data cleansing and data monitoring with which organizations can guarantee the quality of their data.

Suggested Citation

  • Otmane Azeroual & Joachim Schöpfel, 2019. "Quality Issues of CRIS Data: An Exploratory Investigation with Universities from Twelve Countries," Publications, MDPI, vol. 7(1), pages 1-18, February.
    • Handle: RePEc:gam:jpubli:v:7:y:2019:i:1:p:14-:d:208141
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    References listed on IDEAS

    as
    1. Sophie Biesenbender & Stefan Hornbostel, 2016. "The Research Core Dataset for the German science system: challenges, processes and principles of a contested standardization project," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 837-847, February.
    2. Otmane Azeroual & Gunter Saake & Jürgen Wastl, 2018. "Data measurement in research information systems: metrics for the evaluation of data quality," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1271-1290, June.
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    Citations

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    Cited by:

    1. Otmane Azeroual & Joachim Schöpfel & Dragan Ivanovic, 2020. "Influence of Information Quality via Implemented German RCD Standard in Research Information Systems," Data, MDPI, vol. 5(2), pages 1-10, March.
    2. Otmane Azeroual & Gunter Saake & Mohammad Abuosba & Joachim Schöpfel, 2020. "Data Quality as a Critical Success Factor for User Acceptance of Research Information Systems," Data, MDPI, vol. 5(2), pages 1-13, April.
    3. Janne Pölönen & Otto Auranen, 2022. "Research performance and scholarly communication profile of competitive research funding: the case of Academy of Finland," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7415-7433, December.
    4. Yrjö Lappalainen & Matti Lassila & Tanja Heikkilä & Jani Nieminen & Tapani Lehtilä, 2023. "Migrating 120,000 Legacy Publications from Several Systems into a Current Research Information System Using Advanced Data Wrangling Techniques," Publications, MDPI, vol. 11(4), pages 1-16, November.
    5. Joachim Schöpfel & Stéphane Chaudiron & Bernard Jacquemin & Eric Kergosien & Hélène Prost & Florence Thiault, 2023. "The Transformation of the Green Road to Open Access," Publications, MDPI, vol. 11(2), pages 1-12, May.
    6. Sandra Rousseau & Ronald Rousseau, 2021. "Bibliometric Techniques And Their Use In Business And Economics Research," Journal of Economic Surveys, Wiley Blackwell, vol. 35(5), pages 1428-1451, December.

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