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 the current limitations of the citation graph to represent data citation. We identify two critical challenges: to model the evolution of credit appropriately (through references) over time and the ability to model data citation not only for whole datasets (as single objects) but also for parts of them. 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.
Cite:
Buneman, Peter;
Dosso, Dennis;
Lissandrini, Matteo;
and
Silvello, Gianmaria;
“Data Citation and the Citation Graph.”
Quantitative Science Studies (1399-1422).
@article{10.1162/qss_a_00166,
author = {Buneman, Peter and Dosso, Dennis and Lissandrini, Matteo and Silvello, Gianmaria},
title = "{Data citation and the citation graph}",
journal = {Quantitative Science Studies},
volume = {2},
number = {4},
pages = {1399-1422},
year = {2022},
month = {02},
issn = {2641-3337},
doi = {10.1162/qss_a_00166},
url = {https://doi.org/10.1162/qss\_a\_00166},
eprint = {https://direct.mit.edu/qss/article-pdf/2/4/1399/1986111/qss\_a\_00166.pdf},
}