Turn Open Data Requirements into Credited Research Outputs
Close the credit gap. Reduce burden. Make data truly reusable.
The Publisher's Challenge
Publishers face a fundamental tension: funders require open data, researchers need career credit for sharing it, and institutions demand responsible stewardship. Yet most data publishing solutions add operational burden without solving the credit gap that keeps researchers from investing in reusability.
Why Repositories Alone Aren't Enough
Repositories excel at preservation and access, but they don't establish credit, enable exploration, or provide reuse guidance. Data deposited without editorial oversight, peer review, or clear attribution remains invisible to researchers and risky for AI systems. Publishers orchestrate the full lifecycle—from curation through publication to responsible reuse.
The FAIR² Solution: Close the Credit Gap
Senscience turns open data requirements into credited research outputs. The FAIR² Data Platform integrates data curation, FAIR² certification, and peer-reviewed publication into a single workflow that closes the credit gap while reducing operational burden.
Publishers maintain editorial control and standards. Researchers get career credit and powerful tools for exploration. Machines access data responsibly with clear reuse guidance. Institutions ensure preservation and accountability. Senscience orchestrates across the ecosystem without replacing repositories or existing infrastructure.
Proven at Scale: FAIR² in Production
FAIR² is already in production at Frontiers as FAIR² Data Management, demonstrating how data publishing can be operationalized at scale while closing the credit gap and enabling responsible reuse. This isn't theoretical—it's working today.
How It Works
- •Submission: Researchers submit raw data and documentation
- •AI-Assisted Curation: Clara, our intelligent data steward, handles validation and standardization under human oversight
- •FAIR² Certification: Comprehensive metadata and compliance verification
- •Peer Review: Editorial oversight and quality assurance
- •Publication: Citable data article with DOI
- •Repository Deposition: Automatic archival for long-term preservation
- •Responsible Reuse: Machine-actionable format with ethical guidance enables trustworthy AI/ML integration
Implementation Strategy
Adoption can begin with a scoped rollout and expand over time, reducing risk while delivering immediate value. FAIR² is designed to integrate seamlessly with existing publishing workflows, not replace them.
