{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T11:06:42Z","timestamp":1789729602873,"version":"4.0.1"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2019s t-test. FAIR maturity was scored against the 41 indicators of the Research Data Alliance (RDA) FAIR Data Maturity Model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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 \u00b1 4.1 s versus 30.0 \u00b1 2.2 s for federated, 1.49 times slower (\n                      <jats:italic>p<\/jats:italic>\n                      = 0.003). Vertical and horizontal federation performed comparably (\n                      <jats:italic>p<\/jats:italic>\n                      = 0.060) and returned identical results (1,378 records). FAIR maturity of the published data was 6%, 60%, and 83% for direct, centralized, and federated access, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fvets.2026.1962800","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T10:56:25Z","timestamp":1789728985000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From data silos to actionable insights: a comparative evaluation of data access models and practical guidelines for animal health surveillance"],"prefix":"10.3389","volume":"13","author":[{"given":"Saba","family":"Noor","sequence":"first","affiliation":[{"name":"Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University","place":["Ghent, Belgium"]},{"name":"Department of Internal Medicine, Reproduction, and Population Medicine, Faculty of Veterinary Medicine, Ghent University","place":["Ghent, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruben","family":"Taelman","sequence":"additional","affiliation":[{"name":"IDLab, Department of Electronics and Information Systems, Ghent University-IMEC","place":["Ghent, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeroen","family":"Degroote","sequence":"additional","affiliation":[{"name":"Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University","place":["Ghent, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bart","family":"Pardon","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Reproduction, and Population Medicine, Faculty of Veterinary Medicine, Ghent University","place":["Ghent, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerdien","family":"van Schaik","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, Unit Farm Animal Health, Utrecht University","place":["Utrecht, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Rafiq","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Technology, KU Leuven","place":["Brugge, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camille","family":"Delavenne","sequence":"additional","affiliation":[{"name":"EpiMundi","place":["Lyon, France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Animal Science, Cornell University","place":["Ithaca, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanlu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Animal Science, Cornell University","place":["Ithaca, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puchun","family":"Niu","sequence":"additional","affiliation":[{"name":"Department of Animal Science, Cornell University","place":["Ithaca, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miel","family":"Hostens","sequence":"additional","affiliation":[{"name":"Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University","place":["Ghent, Belgium"]},{"name":"Department of Animal Science, Cornell University","place":["Ithaca, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"100704","DOI":"10.1016\/j.onehlt.2024.100704","article-title":"Implementation of one health surveillance systems: opportunities and challenges-lessons learned from the OH-EpiCap application","volume":"18","author":"Tegegne","year":"2024","journal-title":"One Health"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1384988","DOI":"10.3389\/fpubh.2024.1384988","article-title":"One health surveillance in practice: experiences of integration among human health, animal health, environmental health, and food safety sectors","volume":"12","author":"Benedetti","year":"2024","journal-title":"Front Public Health"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"2362","DOI":"10.3390\/s25082362","article-title":"Challenges and solution directions for the integration of smart information systems in the agri-food sector","volume":"25","author":"Ahoa","year":"2025","journal-title":"Sensors"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"109855","DOI":"10.1016\/j.compag.2024.109855","article-title":"Implementing FAIR principles in data management systems: a multi-case study in precision farming","volume":"230","author":"Krisnawijaya","year":"2025","journal-title":"Comput Electron Agric"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"111388","DOI":"10.1016\/j.dib.2025.111388","article-title":"Agri-food data spaces: highlighting the need for a farm-centered strategy","volume":"59","author":"Brunori","year":"2025","journal-title":"Data Brief"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1038\/s41597-025-04785-4","article-title":"Reusability challenges of livestock production data to improve animal health","volume":"12","author":"Delavenne","year":"2025","journal-title":"Sci Data"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"101977","DOI":"10.1016\/j.atech.2026.101977","article-title":"A systematic review on the role of livestock ontologies in animal health management and disease surveillance: a PRISMA 2020 and AI-assisted screening approach","volume":"14","author":"Noor","year":"2026","journal-title":"Smart Agric Technol"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"216","DOI":"10.2460\/javma.23.07.0426","article-title":"Assessing stakeholders\u2019 opinions on sharing antimicrobial susceptibility testing data from animals is key to developing a centralized database and dashboard tool","volume":"262","author":"Raineri","year":"2024","journal-title":"J Am Vet Med Assoc"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"635","DOI":"10.3390\/pathogens11060635","article-title":"Monitoring and surveillance of small ruminant health in the Netherlands","volume":"11","author":"Dijkstra","year":"2022","journal-title":"Pathogens"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.prevetmed.2016.10.002","article-title":"Surveillance of cattle health in the Netherlands: monitoring trends and developments using routinely collected cattle census data","volume":"134","author":"Santman-Berends","year":"2016","journal-title":"Prev Vet Med"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1249925","DOI":"10.3389\/fvets.2024.1249925","article-title":"The French national animal health surveillance platform: an innovative, cross-sector collaboration to improve surveillance system efficiency in France and a tangible example of the one health approach","volume":"11","author":"Dupuy","year":"2024","journal-title":"Front Vet Sci"},{"key":"ref12","author":"Bokma","year":"2023"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.13354666","article-title":"Cattle barometer","author":"Bokma","year":"2023","journal-title":"DECIDE Case Stud"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1186\/s12917-021-02971-1","article-title":"Systematic review of the status of veterinary epidemiological research in two species regarding the FAIR guiding principles","volume":"17","author":"Meyer","year":"2021","journal-title":"BMC Vet Res"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"301","DOI":"10.3389\/fvets.2019.00301","article-title":"Opportunities for improved disease surveillance and control by use of integrated data on animal and human health","volume":"6","author":"Houe","year":"2019","journal-title":"Front Vet Sci"},{"key":"ref16","author":"Dedecker","year":"2022"},{"key":"ref17","author":"Sambra","year":"2016"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"4336","DOI":"10.3390\/app12094336","article-title":"Multi-institutional breast cancer detection using a secure on-boarding service for distributed analytics","volume":"12","author":"Welten","year":"2022","journal-title":"Appl Sci"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"bbae262","DOI":"10.1093\/bib\/bbae262","article-title":"Sharing sensitive data in life sciences: an overview of centralized and federated approaches","volume":"25","author":"Rujano","year":"2024","journal-title":"Brief Bioinform"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.csbj.2024.02.014","article-title":"Privacy-preserving federated machine learning on FAIR health data: a real-world application","volume":"24","author":"Sinaci","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/cryptography9030052","article-title":"Towards empowering stakeholders through decentralized trust and secure livestock data sharing","volume":"9","author":"Ghafoor","year":"2025","journal-title":"Cryptography"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3390\/informatics12030057","article-title":"Blockchain-enabled, nature-inspired federated learning for cattle health monitoring","volume":"12","author":"Ganesan","year":"2025","journal-title":"Informatics"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/sdata.2016.18","article-title":"The FAIR guiding principles for scientific data management and stewardship","volume":"3","author":"Wilkinson","year":"2016","journal-title":"Sci Data"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1038\/s41597-019-0184-5","article-title":"Evaluating FAIR maturity through a scalable, automated, community-governed framework","volume":"6","author":"Wilkinson","year":"2019","journal-title":"Sci Data"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"41","DOI":"10.5334\/dsj-2020-041","article-title":"The FAIR data maturity model: an approach to harmonise FAIR assessments","volume":"19","author":"Bahim","year":"2020","journal-title":"Data Sci J"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"82","DOI":"10.12688\/openreseurope.15988.2","article-title":"The DECIDE project: from surveillance data to decision-support for farmers and veterinarians","volume":"3","author":"van Schaik","year":"2023","journal-title":"Open Res Eur"},{"key":"ref27","author":"Garcia-Morante","year":"2025"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1186\/s13620-026-00356-0","article-title":"The DECIDE cattle barometer: a cross-country monitoring and decision support tool for respiratory pathogens","author":"Bokma","year":"2026","journal-title":"Ir Vet J"},{"key":"ref29","volume-title":"decide-project-eu\/LiveStockHealthOntologyWp1: Initial Release","author":"Noor","year":"2023"},{"key":"ref30","first-page":"75","volume-title":"Smart Farms: Improving Data-Driven Decision Making in Agriculture","author":"Noor","year":"2024"},{"key":"ref31","author":"Noor","year":"2024"},{"key":"ref32","author":"Taelman","year":"2023"},{"key":"ref33","author":"Taelman","year":"2018"},{"key":"ref34","author":"Choudhury","year":"2020"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-026-08217-9","article-title":"Enhancing reusability of veterinary epidemiological data by creating contextual metadata","author":"Faverjon","year":"2026","journal-title":"Res Sq"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"33","DOI":"10.5334\/dsj-2024-033","article-title":"The FAIR assessment conundrum: reflections on tools and metrics","volume":"23","author":"Candela","year":"2024","journal-title":"Data Sci J"},{"key":"ref37","author":"Ereth","year":"2018"},{"key":"ref38","author":"Berends","year":"2025"},{"key":"ref39","author":"Aimonier-Davat","year":"2024"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s00778-015-0415-0","article-title":"Processing SPARQL queries over distributed RDF graphs","volume":"25","author":"Peng","year":"2016","journal-title":"VLDB J"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"33","DOI":"10.5334\/dsj-2024-018","article-title":"Advancing FAIR agricultural data: the AgReFed FAIR assessment tool","volume":"23","author":"Bahlo","year":"2024","journal-title":"Data Sci J"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"102126","DOI":"10.1016\/j.ecoinf.2023.102126","article-title":"FAIR degree assessment in agriculture datasets using the F-UJI tool","volume":"76","author":"Petrosyan","year":"2023","journal-title":"Ecol Inform"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"100402","DOI":"10.1016\/j.impact.2022.100402","article-title":"FAIR assessment tools: evaluating use and performance","volume":"27","author":"Krans","year":"2022","journal-title":"NanoImpact"}],"container-title":["Frontiers in Veterinary Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fvets.2026.1962800\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T10:56:27Z","timestamp":1789728987000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fvets.2026.1962800\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,17]]},"references-count":43,"alternative-id":["10.3389\/fvets.2026.1962800"],"URL":"https:\/\/doi.org\/10.3389\/fvets.2026.1962800","relation":{},"ISSN":["2297-1769"],"issn-type":[{"value":"2297-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,17]]},"article-number":"1962800"}}