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The Road Towards Reproducibility in Science: The Case of Data Citation

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Digital Libraries and Archives (IRCDL 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 733))

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Abstract

Data citation has a profound impact on the reproducibility of science, a hot topic in many disciplines such as as astronomy, biology, physics, computer science and more. Lately, several authoritative journals have been requesting the sharing of data and the provision of validation methodologies for experiments (e.g., Nature Scientific Data and Nature Physics); these publications and the publishing industry in general see data citation as the means to provide new, reliable and usable means for sharing and referring to scientific data. In this paper, we present the state of the art of data citation and we discuss open issues and research directions with a specific focus on reproducibility. Furthermore, we investigate reproducibility issues by using experimental evaluation in Information Retrieval (IR) as a test case. (This paper is a revised and extended version of [33, 35, 57]).

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Notes

  1. 1.

    https://www.acm.org/publications/policies/artifact-review-badging.

  2. 2.

    http://db-reproducibility.seas.harvard.edu/.

  3. 3.

    http://www.tira.io/.

  4. 4.

    Actually, this would be difficult to achieve.

  5. 5.

    http://direct.dei.unipd.it/.

  6. 6.

    http://lod-direct.dei.unipd.it/.

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Authors and Affiliations

  1. Department of Information Engineering, University of Padua, Padua, Italy

    Nicola Ferro & Gianmaria Silvello

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  1. Nicola Ferro
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  2. Gianmaria Silvello
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Correspondence to Gianmaria Silvello .

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  1. University of Modena and Reggio Emilia, Modena, Italy

    Costantino Grana

  2. University of Modena and Reggio Emilia, Modena, Italy

    Lorenzo Baraldi

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Ferro, N., Silvello, G. (2017). The Road Towards Reproducibility in Science: The Case of Data Citation. In: Grana, C., Baraldi, L. (eds) Digital Libraries and Archives. IRCDL 2017. Communications in Computer and Information Science, vol 733. Springer, Cham. https://doi.org/10.1007/978-3-319-68130-6_2

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