Modern research and enterprise environments require collaboration across teams, departments, and even institutions. At the same time, data often carries sensitive context, proprietary identifiers, or compliance requirements. Delfini addresses this challenge with federation: an approach that allows distributed data sharing without requiring centralizing control.

The Problem with Centralization

Traditional approaches often force organizations to copy data into a single repository:

• Teams struggle with self-determination over their own datasets
• Metadata and context can be lost or corrupted
• Efficiencies of scale quickly give way to conflicts and shifting priorities

Centralized repositories can create bottlenecks and risk breaking provenance chains.

How Delfini Federation Works

Delfini was designed from the outset with data linking and federation in mind. Federation in Delfini allows multiple independent instances to interoperate securely, so each team retains autonomy while sharing data selectively:

• Instance-to-instance sharing: Teams expose datasets or dataviews to specific collaborators without needing users to manage the movement of the underlying data.
• Preserved context: Metadata, data elements, and provenance travel with shared data, so recipients immediately understand column meaning and constraints.
• Controlled access: Permissions are granular, ensuring sensitive columns or datasets remain protected.
• Harmonization: Shared datasets can be mapped to organization-wide standards without requiring local teams to change their internal naming conventions or metadata structures.

This approach ensures that collaboration enhances productivity rather than introducing risk or overhead.

Use Cases

• Cross-institutional research: Federated access to patient or experimental datasets while maintaining local regulatory compliance.
• Multi-team projects: Teams can share intermediate results or aggregated data without exposing raw sensitive data.
• Enterprise data operations: Independent business units maintain their own systems but can harmonize key datasets for corporate analytics.

Summary

Federation turns the traditional trade-off between control and collaboration into a strength. Teams can work independently while contributing to a larger, organization-wide ecosystem of trusted, AI-ready datasets. Delfini ensures that context, validation, and provenance remain intact, no matter where data travels.

For early access or to discuss how federation can help your organization collaborate securely and efficiently, contact delfini@bioteam.net.