Signed Citations: Making Persistent and Verifiable Citations of Digital Scientific Content

Abstract

Digital data are a foundation of 21st century science. However, commonly used data citation practices rely on unverifiable retrieval methods which are susceptible to “content drift”, which occurs when the data associated with an identifier have been allowed to change. Based on our earlier work on reliable dataset identifiers, we propose signed citations, i.e., customary data citations extended to also include a standards-based, verifiable, unique, and fixed-length digital content signature. For example, the signature of a dataset could represent the SHA-256 hash of its content. We show that the inclusion of content signatures in citations not only enables independent verification of the cited content, but can also improve the persistence of the citation. Furthermore, if a content signature registry is available which links content signatures to known locations of their associated content, then content signatures can be used as resolvable data identifiers. Because signed citations are location- and storage-medium-agnostic, as many copies of cited data as necessary can be created to ensure content 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 repositories, search engines, and registries without requiring any time-sensitive information to be baked into citations. 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 real-world applications of content signatures, including compiling a distributed collection of digitized images of bee specimens from natural history collections, versioning biodiversity datasets containing over a billion records available through biodiversity data aggregators, and stabilizing URLs used as identifiers for taxonomic name resources.