publication: Sign data citations to enable data verification and improve citation persistence
30 Jun 2023
Reading time ~1 minute
With support from the National Science Foundation EAGER Grant 1839201, we are happy to announce the publication of:
Elliott, M.J., Poelen, J.H. & Fortes, J.A.B. 2023. Signing data citations enables data verification and citation persistence. Sci Data. doi:10.1038/s41597-023-02230-y hash://sha256/f849c870565f608899f183ca261365dce9c9f1c5441b1c779e0db49df9c2a19d
Commonly used data citation practices rely on unverifable retrieval methods which are susceptible to content drift, which occurs when the data associated with an identifer have been allowed to change. Based on our earlier work on reliable dataset identifers, we propose signed citations, i.e., customary data citations extended to also include a standards-based, verifable, unique, and fxed-length digital content signature. We show that content signatures enable independent verifcation of the cited content and can improve the persistence of the citation. Because content signatures are location- and storage-medium-agnostic, cited data can be copied to new locations to ensure their persistence across current and future storage media and data networks. As a result, content signatures can be leveraged to help scalably store, locate, access, and independently verify content across new and existing data infrastructures. Content signatures can also be embedded inside content to create robust, distributed knowledge graphs that can be cited using a single signed citation. We describe applications of signed citations to solve real-world data collection, identifcation, and citation challenges.