The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications.
In this paper we discuss what is needed for the citation graph to represent data citation. We identify two challenges: (i) to model the evolution of credit appropriately (through references) over time and (ii) to model data citation not only to a dataset treated as a single object but also to parts of it. We describe an extension of the current citation graph model that addresses these challenges. It is built on two central concepts: citable units and reference subsumption. We discuss how this extension would enable data citation to be represented within the citation graph and how it allows for improvements in current practices for bibliometric computations both for scientific publications and for data.
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 t he European Union H2020 research and i nnovation program under the Mari e Sk?odowska-Curie grant agreement No. 838216.
© 2021 Peter Buneman, Dennis Dosso, Matteo Lissandrini, and Gianmaria Silvello.
- Citation graph
- Data citation