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Open Government Data for Non-expert Citizens: Understanding Content and Visualizations’ Expectations

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Research Challenges in Information Science (RCIS 2021)

Abstract

Open government data (OGD) refers to data made available online by governments for anyone to freely reuse. Non-expert users, however, lack the necessary technical skills and therefore face challenges when trying to exploit it. Amongst these challenges, finding useful datasets for citizens is very difficult as their expectations are not always identified. Furthermore, findings the appropriate visualization that is more understandable by citizens is also a barrier. The goal of this paper is to decrease those two entry barriers by better understanding the expectations of non-expert citizens. In order to reach that goal, we first seek to understand their content expectations through the usage statistics analysis of the OGD portal of Namur and through a complementary online survey of 43 participants. Second, we conduct interviews with 10 citizens to obtain their opinion on the appropriate and well-designed visualizations of the content they seek. The findings of this multi-method approach allow us to issue 5 recommendations for OGD portal publishers and developers to foster non-expert use of OGD.

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Notes

  1. 1.

    https://rb.gy/7sgpqo.

  2. 2.

    https://data.namur.be/pages/accueil/.

  3. 3.

    https://data.overheid.nl/community/maatschappij/high-value/gemeenten.

  4. 4.

    https://rb.gy/2wjtgd.

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Correspondence to Abiola Paterne Chokki .

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Chokki, A.P., Simonofski, A., Frénay, B., Vanderose, B. (2021). Open Government Data for Non-expert Citizens: Understanding Content and Visualizations’ Expectations. In: Cherfi, S., Perini, A., Nurcan, S. (eds) Research Challenges in Information Science. RCIS 2021. Lecture Notes in Business Information Processing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-030-75018-3_42

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  • DOI: https://doi.org/10.1007/978-3-030-75018-3_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75017-6

  • Online ISBN: 978-3-030-75018-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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