ArticleFrontiers in veterinary science2026
From data silos to actionable insights: a comparative evaluation of data access models and practical guidelines for animal health surveillance.
Article in Frontiers in veterinary science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Effective livestock disease surveillance depends on scalable, privacy-conscious access to distributed diagnostic data, yet current systems are constrained by fragmented datasets, inconsistent formats, and limited interoperability. Objective: This paper compares three approaches for accessing distributed animal health data: direct data sharing, centralized access, and federated access using Solid Pods. The comparison covers architectural properties, query performance benchmarks, and the FAIR (Findable, Accessible, Interoperable, Reusable) maturity of the data artifacts produced by each approach, with the aim of providing practical guidance for choosing the right strategy in livestock health surveillance. Methods: The three approaches were characterized along seven operational criteria: data access mechanism, privacy and governance, integration workflow, consistency, workflow scalability, ease of use, and total cost of ownership, using the European Cattle Barometer as a testbed. Two experiments measured query performance: centralized versus federated SPARQL on 76,295 cattle records (740,056 RDF triples) across five Solid Pods, and vertical versus horizontal federation across 12 Pods. Queries were mapped to the Livestock Health Ontology (LHO), run five times, with differences tested using Welch's t-test. FAIR maturity was scored against the 41 indicators of the Research Data Alliance (RDA) FAIR Data Maturity Model. Results: Across the seven operational criteria, direct sharing scored high only on ease of use but low on scalability, privacy, integration, and consistency. Centralized access scored highest on data consistency and ease of automation, while federated access scored highest on workflow scalability, privacy and governance, and data integration. Centralized queries finished in 20.2 ± 4.1 s versus 30.0 ± 2.2 s for federated, 1.49 times slower ( Conclusion: No single approach suits all situations: direct sharing for short-term simplicity, centralized access for fast dashboards, and federated access where data ownership and FAIR-aligned outputs matter. In particular, the choice between centralized and federated access is a trade-off between query speed and data sovereignty. These findings inform practical, scalable, and privacy-conscious data-sharing guidelines for livestock disease surveillance.
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