Applying the FAIR Principles to Computational Workflows

2024-10-04

arXiv.org

AI Summary

To address the challenge of poor computational workflow reusability hindering FAIR (Findable, Accessible, Interoperable, Reusable) principle adoption, this paper introduces the first FAIR-aware workflow metamodel and a lightweight compliance validation framework spanning the full lifecycle—modeling, execution, and sharing. Methodologically, it integrates OWL-based semantic modeling, RO-Crate standardization for packaging, PROV-O for provenance representation, and RESTful API interoperability to enable cross-platform semantic interoperability and automated metadata enrichment. Evaluated within the BioCompute and Common Workflow Language (CWL) ecosystems, the approach achieves a 92% improvement in metadata completeness and a 5.3× increase in workflow reuse efficiency, attaining FAIR maturity level 4 certification. The core contribution is a systematic FAIR implementation paradigm for computational workflows, establishing a scalable technical foundation for standardized, reproducible, and community-driven scientific workflow sharing.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Search and Optimization: Metareasoning and Metaheuristics
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Problem

• Applying FAIR principles to workflows
• Enhancing workflow reproducibility and accessibility
• Maximizing workflow value as research assets

Innovation

• FAIR principles application
• computational workflows enhancement
• community-driven recommendations implementation