The Human Disease Ontology (DO) is a FAIR genomic resource fostering cross-domain disease data integration and analysis, at model organism databases (e.g., FlyBase, MGD, RGD, SGD, WormBase, Xenbase, ZFIN) and by the broader biomedical community. It provides expertly curated, human-readable, and machine-actionable disease data with a comprehensive etiological disease classification spanning genetic, infectious, cancer, environmental, complex, rare, and common diseases. It links across authoritative biomedical resources with over 37,000 clinical vocabulary cross mappings and semantically represents diseases with mechanistic drivers and characteristics like mode of inheritance, phenotypes, chemical and environmental exposure, anatomy, and infectious agents.The DO is a collaborative project that assists with disease data standardization and supports a growing disease data ecosystem, having been incorporated into over 390 biomedical resources and used in thousands of individual research projects since its inception in 2003. To expand this ecosystem and help researchers actionably leverage the DO in their research, genomic and machine learning applications and tools at disease-ontology.org that aid in exploration, access, connection, and search of disease data are highlighted. These tools include the Disease Ontology browser and new Disease Ontology Knowledgebase (DO-KB) SPARQL service and faceted search interface. The DO-KB SPARQL service empowers data discovery, extraction, and connection to diverse data across the web through the addition of DO-specific and federated query options, while the DO-KB faceted search interface simplifies and enhances accessibility to the disease-defining semantic data in the DO, eliminating the need for users to have knowledge of programming and semantic logic to explore disease relationships.Disease knowledge within the Disease Ontology and the disease data ecosystem it supports is continually updated and expanding. Greater collaboration will foster FAIR-er disease data access and open new avenues in biomedical research.