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Ten challenges in modeling bibliographic data for bibliometric analysis

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Abstract

The complexity and variety of bibliographic data is growing, and efforts to define new methodologies and techniques for bibliometric analysis are intensifying. In this complex scenario, one of the most crucial issues is the quality of data and the capability of bibliometric analysis to cope with multiple data dimensions. Although the problem of enforcing a multidimensional approach to the analysis and management of bibliographic data is not new, a reference design pattern and a specific conceptual model for multidimensional analysis of bibliographic data are still missing. In this paper, we discuss ten of the most relevant challenges for bibliometric analysis when dealing with multidimensional data, and we propose a reference data model that, according to different goals, can help analysis designers and bibliographic experts in working with large collections of bibliographic data.

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Notes

  1. A detailed description of each fact schema according to DFM is given in the following sections.

  2. The model has also been tested on a collection of about 8,000 publications in the research area of databases and data modeling.

  3. A very important contribution about the statistical issues in comparing institutional performance is [22].

  4. For a detailed overview of the international rankings, see [19]; for the quality assessment of composite indicators, see [3].

  5. http://bulletin.imstat.org/2011/09/presidential-address-peter-hall/.

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Acknowledgments

We would like to thank the UNIMIVAL group of the University of Milan (http://www.unimi.it/cataloghi/nucelo_valutazione/ricercatori_in_breve.pdf). In the last year, they worked with us on the subject of bibliometrics; many of our ideas come from our common work and from our many fruitful discussions.

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  1. Dipartimento di Informatica e Comunicazione, Università degli Studi di Milano, Milan, Italy

    Alfio Ferrara

  2. Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Milano, Milan, Italy

    Silvia Salini

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  1. Alfio Ferrara
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  2. Silvia Salini
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Correspondence to Silvia Salini.

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Ferrara, A., Salini, S. Ten challenges in modeling bibliographic data for bibliometric analysis. Scientometrics 93, 765–785 (2012). https://doi.org/10.1007/s11192-012-0810-x

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