Fusion of computational and experimental provenance in RO-Crate.

Area of Science:

• Data Science
• Computational Biology
• Bioinformatics

Background:

• Modern research generates complex, multimodal data from diverse experimental and computational analyses.
• The FAIR Principles guide data management, but integrating experimental and computational provenance remains challenging.
• Existing frameworks like ISA and Workflow RO-Crate capture different aspects of research processes but are largely disconnected.

Purpose of the Study:

• To present the ARC Workflow Run RO-Crate profile, a novel standard for unifying experimental and computational provenance.
• To enable the creation of FAIR Digital Objects that capture the full research process.
• To enhance interoperability between experimental assays and computational workflow runs.

Main Methods:

• Development of the ARC Workflow Run RO-Crate profile, aligning ISA-based experimental provenance with workflow execution metadata.
• Creation of a standards-compliant container for integrated research data objects.
• Ensuring compatibility with existing RO-Crate and ISA tooling ecosystems.

Main Results:

• The ARC Workflow Run RO-Crate profile successfully integrates experimental and computational provenance into a single, queryable provenance graph.
• Shared semantics are established, making experimental assays and workflow runs interoperable.
• The profile harmonizes diverse data types and remains compatible with established tools.

Conclusions:

• The ARC Workflow Run RO-Crate profile provides a unified approach to managing complex research data provenance.
• This standard facilitates the creation of more meaningful and FAIR Digital Objects.
• The profile enhances data discoverability, accessibility, and reusability in multimodal research projects.