Programmatic Access to FAIRified Digital Plant Genetic Resources.

Area of Science:

• Genomics and Bioinformatics
• Plant Science
• Data Science

Background:

• High-throughput genotype technologies are established, with increasing focus on phenotype data acquisition from automated platforms.
• Managing large, diverse experimental datasets requires robust data stewardship for interoperability and reusability.
• FAIR data principles (Findable, Accessible, Interoperable, Reusable) are essential for machine-readable and scalable scientific data sharing.

Purpose of the Study:

• To present concepts, best practices, and implementations for managing and sharing plant genetics resources (PGR) data.
• To demonstrate the transformation of legacy data infrastructures into a bio-digital resource center.
• To highlight the benefits of integrated databases, data stewardship, and FAIR data exposition.

Main Methods:

• Utilizing FAIR data principles for machine-readability and scalability.
• Adopting BrAPI (Breeding Application Programming Interface) standards for FAIR data guidelines.
• Implementing integrated database back-ends and data stewardship processes.

Main Results:

• Established a FAIR data framework for plant genetics resources (PGR).
• Successfully transformed legacy data infrastructures into a bio-digital resource center.
• Demonstrated machine-readable, highly scalable programmatic interfaces for data exposition.

Conclusions:

• FAIR data principles and BrAPI standards are critical for effective management of PGR data.
• Integrated data stewardship and FAIR data exposition enhance data interoperability and reusability.
• The developed bio-digital resource center improves access to and utilization of plant genetics information.