FAIR digital twins for biodiversity: enabling data, model, and workflow integration.

Area of Science:

• Environmental Science
• Computational Biology
• Data Science

Background:

• The biodiversity crisis necessitates advanced computational tools for data integration and analysis.
• Existing biodiversity data and models are often complex and disparate, hindering research progress.

Purpose of the Study:

• To introduce the concept of FAIR Digital Twins (FDTs) for biodiversity research.
• To demonstrate how combining Digital Twins with FAIR principles can transform biodiversity research and decision-making.
• To present strategies for integrating heterogeneous data, models, and workflows within a FAIR framework.

Main Methods:

• Leveraging the Biodiversity Digital Twin (BioDT) project (2022-2025) to develop prototype digital twins.
• Implementing FAIR principles (Findable, Accessible, Interoperable, Reusable) for data and model integration.
• Utilizing tools like Research Object Crate (RO-Crate) for metadata packaging and standardization.

Main Results:

• Development of ten prototype digital twins addressing diverse biodiversity challenges (e.g., grassland dynamics, bird monitoring, ecosystem services).
• Demonstration of strategies for integrating heterogeneous data, models, and computational workflows.
• Identification of implementation challenges including data fragmentation and semantic interoperability.

Conclusions:

• FAIR Digital Twins offer a scalable and reusable approach to biodiversity modeling and simulation.
• This convergence advances evidence-based policy decisions through robust data integration and analysis.
• Opportunities exist for dynamic adaptation, modular workflows, and cross-domain collaboration in biodiversity research.