Expanding the Citation Graph for Data Citations

Peter Buneman*, Dennis Dosso*, Matteo Lissandrini*, Gianmaria Silvello*

*Corresponding author for this work

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The Citation Graph (CG) is a computational artifact widely used to represent the domain of published literature. There is an increasing demand to treat the publication of data in the same way that we treat conventional publications. It should be possible to cite data for the same reasons that is is necessary to cite other publications. In this paper we see some of the limitations of the citation graph, and we discuss how some implementation-agnostic extensions may solve them, thus also allowing the introduction of data and the management of data citations within the CG.

Original languageEnglish
JournalCEUR Workshop Proceedings
Pages (from-to)276-283
Number of pages8
Publication statusPublished - 2022
Event30th Italian Symposium on Advanced Database Systems, SEBD 2022 - Tirrenia, Italy
Duration: 19 Jun 202220 Jun 2022


Conference30th Italian Symposium on Advanced Database Systems, SEBD 2022

Bibliographical note

Funding Information:
The work was partially supported by the ExaMode project, as part of the European Union H2020 program under Grant Agreement no. 825292. Matteo Lissandrini is supported by the European Union H2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 838216. Peter Buneman was partly supported by the Huawei Edinburgh Research Laboratory.

Publisher Copyright:
© 2022 CEUR-WS. All rights reserved.


  • Bibliometrics
  • Citation Graph
  • Data Citation


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  • Data citation and the citation graph

    Buneman, P., Dosso, D., Lissandrini, M. & Silvello, G., 4 Feb 2022, In: Quantitative Science Studies. 2, 4, p. 1399-1422 24 p.

    Research output: Contribution to journalJournal articleResearchpeer-review

    Open Access
    12 Citations (Scopus)
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