Making Research FAIR With a PID-centric Workflow

Abstract

Persistent identifiers (PIDs) are unique, machine-readable codes assigned to research entities that allow them to be easily discoverable. PIDs, along with their accompanying metadata, are crucial enablers of the FAIR principles (Findable, Accessible, Interoperable, and Reusable). PIDs ensure that digital objects can be located, accessed, and reused by humans and machines alike, while metadata provides essential information about research objects, including their origin, content, and format.

In the research ecosystem, each stakeholder has a role to play in integrating PIDs into their workflows. Publishers, for example, can assign DOIs (Digital Object Identifiers) to articles, books, and other publications, making them easily findable and citable. Repositories can assign PIDs to datasets, making them discoverable and accessible. Researchers can use PIDs to link their data to their publications, ensuring that their data is discoverable and can be reused in future research.

Despite the importance of PIDs and metadata, it's not always clear to researchers how to take advantage of the existing infrastructure and make their outputs FAIR. Being aware of the available PIDs, such as DOIs, ORCIDs, and RORs, and how they can be used to identify, connect, and cite various types of outputs and resources can help researchers plan and execute sensible data management, sharing, and publishing decisions that are efficient and beneficial in the long term.

In the Implementing FAIR Workflows Project, DataCite works with a team of researchers at the Max Planck Institute for Empirical Aesthetics to follow along a neuroscience PhD project from the beginning, to design and plan for a series of workflows that put the FAIR principles into practice, so that they become an inherent part of the research process, instead of an afterthought.  The FAIR workflows researcher is undertaking in the project include data management planning, experiment preregistration, domain-specific metadata capturing and archiving, data and code sharing, preprinting, and open access publishing. We have also been tracking the time spent on various types of FAIR and Open activities, hoping to shed a light on the actual time commitment expected for a FAIRly conducted research project.

We share our experience so far implementing these workflows with the Edinburgh Open Science community - the approach we used, the steps we’ve taken, and the outcomes and challenges that surfaced during the process. We are also preparing a guide for researchers to take on FAIR research workflows in their day-to-day work based on the lessons learned in the project, we look forward to taking the opportunity to hear from the community whether it resonates, and how can we format it in a way that’s most useful.