Connected Research: The Potential of the PID Graph

Helena Cousijn; Ricarda Braukmann; Martin Fenner; Christine Ferguson; René van Horik; Rachael Lammey; Alice Meadows; Simon Lambert

doi:10.1016/j.patter.2020.100180

Connected Research: The Potential of the PID Graph

Patterns (N Y). 2021 Jan 8;2(1):100180. doi: 10.1016/j.patter.2020.100180.

Authors

Helena Cousijn¹, Ricarda Braukmann², Martin Fenner¹, Christine Ferguson³, René van Horik², Rachael Lammey⁴, Alice Meadows⁵, Simon Lambert⁶

Affiliations

¹ DataCite, Welfengarten 1B, 30167 Hannover, Germany.
² Data Archiving and Networked Services, Anna van Saksenlaan 51, 2593 HW Den Haag, the Netherlands.
³ The European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
⁴ Crossref, New Road, Oxford OX1 1BY, UK.
⁵ National Information Standards Organization (NISO), 3600 Clipper Mill Road Suite 302, Baltimore, MD 21211-1948, USA.
⁶ UKRI-STFC, Scientific Computing Department, Rutherford Appleton Laboratory, Harwell Campus, Didcot OX11 0QX, UK.

Abstract

Persistent identifiers (PIDs) provide unique and long-lasting references to entities. They enable unique identification persistently over time and hence play a crucial role in supporting the FAIR (Findable, Accessible, Interoperable, Reusable) principles. In this paper, we describe how the benefits of PIDs can be amplified by connecting them via their metadata. We are introducing the next step in PID infrastructure: the PID Graph. The PID Graph establishes connections between different entities within the research landscape, thereby enabling both researchers and institutions to access new information. The paper closes with three recommendations, which will help to optimize the use and value of PIDs within the research ecosystem.

Keywords: DSML 4: Production: Data science output is validated, understood, and regularly used for multiple domains/platforms.

Publication types

Review