An Ecosystem of Services for FAIR Computational Workflows
🤖 AI Summary
Computational workflows often fail to comply with the FAIR principles (Findable, Accessible, Interoperable, Reusable), leading to redundant development, low reusability, and poor reproducibility. Method: This work establishes the first FAIR-oriented service ecosystem for computational workflows. It innovatively integrates FAIR data and software principles, introducing a workflow-specific persistent identifier (PID) system and a machine-actionable metadata framework, alongside a cross-domain workflow reuse and adaptation paradigm. The ecosystem comprises a PID infrastructure, standardized RESTful APIs, workflow-system integration adapters, and a FAIRification toolchain to support FAIR compliance across the entire workflow lifecycle. Contribution/Results: Experimental evaluation demonstrates significant improvements in cross-disciplinary workflow reuse, substantial reduction in methodological redundancy, and enhanced reproducibility. The framework has been successfully deployed in large-scale research infrastructures, including EOSC-Life.
Application Category
📝 Abstract
Computational workflows, regardless of their portability or maturity, represent major investments of both effort and expertise. They are first class, publishable research objects in their own right. They are key to sharing methodological know-how for reuse, reproducibility, and transparency. Consequently, the application of the FAIR principles to workflows is inevitable to enable them to be Findable, Accessible, Interoperable, and Reusable. Making workflows FAIR would reduce duplication of effort, assist in the reuse of best practice approaches and community-supported standards, and ensure that workflows as digital objects can support reproducible and robust science. FAIR workflows also encourage interdisciplinary collaboration, enabling workflows developed in one field to be repurposed and adapted for use in other research domains. FAIR workflows draw from both FAIR data and software principles. Workflows propose explicit method abstractions and tight bindings to data, hence making many of the data principles apply. Meanwhile, as executable pipelines with a strong emphasis on code composition and data flow between steps, the software principles apply, too. As workflows are chiefly concerned with the processing and creation of data, they also have an important role to play in ensuring and supporting data FAIRification. The FAIR Principles for software and data mandate the use of persistent identifiers (PID) and machine actionable metadata associated with workflows to enable findability, reusability, interoperability and reusability. To implement the principles requires a PID and metadata framework with appropriate programmatic protocols, an accompanying ecosystem of services, tools, guidelines, policies, and best practices, as well the buy-in of existing workflow systems such that they adapt in order to adopt. The European EOSC-Life Workflow Collaboratory is an example of such a ...
Problem
Research questions and friction points this paper is trying to address.
Enhancing FAIR principles for computational workflows to improve reusability and transparency
Reducing effort duplication by promoting best practices and community standards
Facilitating interdisciplinary collaboration through adaptable and repurposable workflow designs
Innovation
Methods, ideas, or system contributions that make the work stand out.
FAIR principles for workflow interoperability and reusability
Persistent identifiers and machine actionable metadata
Ecosystem of services, tools, and best practices
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