SSBD Ontology: A Two-Tier Approach for Interoperable Bioimaging Metadata

• URL: http://arxiv.org/abs/2508.02084v1
• Date: Mon, 04 Aug 2025 05:51:55 GMT
• Title: SSBD Ontology: A Two-Tier Approach for Interoperable Bioimaging Metadata
• Abstract summary: Bioimaging technologies have enabled the large-scale acquisition of multidimensional data, yet effective metadata management and interoperability remain significant challenges.<n>We propose a new framework for the Systems Science of Biological Dynamics Database that adopts a two-tier architecture.<n>Our approach contributes to establishing a Findable, Accessible, Interoperable, and Reusable data ecosystem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advanced bioimaging technologies have enabled the large-scale acquisition of multidimensional data, yet effective metadata management and interoperability remain significant challenges. To address these issues, we propose a new ontology-driven framework for the Systems Science of Biological Dynamics Database (SSBD) that adopts a two-tier architecture. The core layer provides a class-centric structure referencing existing biomedical ontologies, supporting both SSBD:repository -- which focuses on rapid dataset publication with minimal metadata -- and SSBD:database, which is enhanced with biological and imaging-related annotations. Meanwhile, the instance layer represents actual imaging dataset information as Resource Description Framework individuals that are explicitly linked to the core classes. This layered approach aligns flexible instance data with robust ontological classes, enabling seamless integration and advanced semantic queries. By coupling flexibility with rigor, the SSBD Ontology promotes interoperability, data reuse, and the discovery of novel biological mechanisms. Moreover, our solution aligns with the Recommended Metadata for Biological Images guidelines and fosters compatibility. Ultimately, our approach contributes to establishing a Findable, Accessible, Interoperable, and Reusable data ecosystem within the bioimaging community.

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