Lessons learned and way forward: leveraging PIDs and metadata for FAIR research workflows

Persistent identifiers (PIDs) and their associated metadata are uniquely positioned to play an instrumental role in capturing, preserving, and providing access to provenance information that is key to facilitating findability, accessibility, interoperability, and reusability (FAIR) of scholarly resources. Over the last years, we carried out a FAIR workflows project in close collaboration with a Neuroscience research group, in which we conducted an exemplar neuroscience research project entirely following the principles of FAIR research practices - with an openly shared DMP, pre-registered experiments, published dataset and preprint, and numerous other outputs throughout the project lifecycle, using research tools and platforms integrated with open infrastructure. The project resulted in a comprehensive portfolio within which all people, organizations, and outputs are identified and interconnected through PIDs. In this presentation, I will walk through the work done and share lessons learned in the first three years of the FAIR workflows project: 1. It is key to ensure the infrastructure and tools are ready to support the researchers when they are ready to embrace open practices like preregistration, data sharing, and preprint publishing; 2. Funders and publishers are essential in providing guidelines to support and streamline the integration of open and FAIR sharing practices; and 3. The researcher community works around creating and maintaining data standards, data analysis tools/platforms to enable sharing and reuse can benefit greatly from the integration of PIDs. I will also touch on the ongoing and future work in the project, including the validation of FAIR practices in a collaboration context and the examination of the reuse of these FAIR outputs. This proposal is intended to contribute to the discourse on “data provenance, CARE / FAIR data principles” at the conference; we hope to provide inspiration and invite feedback and engagement from the community for our ongoing work.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-06
DOI
https://doi.org/10.5281/zenodo.15728081
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
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article

Lessons learned and way forward: leveraging PIDs and metadata for FAIR research workflows

Zenodo (CERN European Organization for Nuclear Research)
Research Data Management Practices
article

Lessons learned and way forward: leveraging PIDs and metadata for FAIR research workflows

article en

Abstract

Persistent identifiers (PIDs) and their associated metadata are uniquely positioned to play an instrumental role in capturing, preserving, and providing access to provenance information that is key to facilitating findability, accessibility, interoperability, and reusability (FAIR) of scholarly resources. Over the last years, we carried out a FAIR workflows project in close collaboration with a Neuroscience research group, in which we conducted an exemplar neuroscience research project entirely following the principles of FAIR research practices - with an openly shared DMP, pre-registered experiments, published dataset and preprint, and numerous other outputs throughout the project lifecycle, using research tools and platforms integrated with open infrastructure. The project resulted in a comprehensive portfolio within which all people, organizations, and outputs are identified and interconnected through PIDs. In this presentation, I will walk through the work done and share lessons learned in the first three years of the FAIR workflows project: 1. It is key to ensure the infrastructure and tools are ready to support the researchers when they are ready to embrace open practices like preregistration, data sharing, and preprint publishing; 2. Funders and publishers are essential in providing guidelines to support and streamline the integration of open and FAIR sharing practices; and 3. The researcher community works around creating and maintaining data standards, data analysis tools/platforms to enable sharing and reuse can benefit greatly from the integration of PIDs. I will also touch on the ongoing and future work in the project, including the validation of FAIR practices in a collaboration context and the examination of the reuse of these FAIR outputs. This proposal is intended to contribute to the discourse on “data provenance, CARE / FAIR data principles” at the conference; we hope to provide inspiration and invite feedback and engagement from the community for our ongoing work.

Zenodo (CERN European Organization for Nuclear Research)
DataCite (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 37%
Research Data Management Practices
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