A visual analytics approach for the assessment of information quality of performance models—a software review

Marco Angelini (), Cinzia Daraio () and Luca Urban ()
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Marco Angelini: Sapienza University of Rome
Cinzia Daraio: Sapienza University of Rome
Luca Urban: Sapienza University of Rome

Scientometrics, 2022, vol. 127, issue 12, No 3, 6827-6853

Abstract: Abstract In this paper we provide a review of the main functionalities of a Visual Analytics Environment (VAE) developed for the assessment of data and information quality in the context of performance evaluation of research organizations. Performing data and information quality tests are necessary procedures to ensure the bibliometric and research performance evaluation analysis of organizations have the necessary robustness. The proposed environment is helpful to guide the user to an Information Quality-aware development of Performance models. This interactive visual analytics environment offers to the user the possibility to produce and compare information quality-aware indicators, exploring and defining correct behavior, identifying anomalous cases from both data quality and information quality perspectives, and supporting the user in forming hypotheses on possible causes for those anomalies. The proposed approach, exploiting visual interactive exploration results in a more efficient process, minimizing the number of cases for which a manual investigation is needed. The illustration on European higher education institutions data demonstrates the use of the presented functionalities and their benefits.

Keywords: Visual analytics; Data quality; Information quality; Research and innovation data; Higher education data (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11192-022-04399-2

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