This special issue is on Canonical Workflow Frameworks for Research (CWFR). A workflow refers to a sequence of activities, which may be more or less computer-based, used with regularity in the research process. CWFR aim to identify common patterns in such scientifically motivated workflows and to offer libraries of components based on FAIR Digital Objects as the integrative standard. Such CWFR components can be reusable independent of particular technologies, benefitting researchers in their daily work by making recurring activities more efficient, using automated workflow methods that would immediately create FAIR compliant data without adding burden.

It is the goal of this special issue to provide readers with a deep exploration of CWFR and how it relates to research driven workflows, to existing workflow technologies, and to the use of FAIR Digital Objects. This issue covers articles examining core research activities including experimentation, data processing and analysis, data management, reproducibility, and publication. The articles comment on CWFR and its relation to these workflows, either conceptually in view of the current research ecosystem and infrastructure or more practically, focusing on a specific implementation, design, tool, or context relating to CWFR.

The contributing authors are experts in their area. They include researchers, data professionals, data managers and curators, IT specialists and others who are using, developing, or experimenting with the effective use of canonical workflows and workflow patterns for data intensive research. As guest editors, it has been a privilege to work with such accomplished authors.

It is our hope that this issue will stimulate further exploration of this subject. The papers in this issue address timely questions such as, what are the recurring patterns of work within or across institutions and research communities? What are the core elements of workflow technologies and how can they relate to the core ideas of CWFR? How well do existing integration standards and best practices address this? What is the potential of FDOs to support the goals of CWFR? How can research be protected against the ever-changing technological fashions?

Finally, we are grateful to the journal for the opportunity to publish this special issue and to Dr. Fenghong Liu, Managing Editor-in-Chief, for her skilled guidance and support.

Yann Le Franc, PhD is the CEO and Scientific Director of e-Science Data Factory S.A.S.U., a French R&D company aiming at proposing innovative solutions for FAIR data management to accelerate growth and progress. Yann Le Franc has a PhD in Neurosciences and Pharmacology (2004). After a postdoctoral experience in the US, he worked on data management projects in Neurosciences at the University of Antwerp (Belgium) and in the context of the International Neuroinformatics Coordinating Facility (INCF) where he developed a strong expertise in ontology design and semantic web technologies. He then contributed to several Horizon 2020 Research Infrastructure projects (EUDAT, EOSC-Hub, …) as an expert on Semantic Web and ontology design. He is co-chairing the Research Data Alliance Vocabulary and Semantic Service Interest Group and the FDO Semantic Group. He is also a member of the EOSC Semantic Interoperability Task Force. He is actively involved in the FAIRification and standardization of semantic artefacts in the context of FAIRsFAIR and OntoCommons projects. In parallel, he is the technical manager of the EOSC-Pillar project for the French National Computing Center for Higher Education (CINES).

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit