Enhancing Data Findability through FAIR Signpost-ing: experiences and best practices from KonsortSWD

The FAIR Principles are inherently open to interpretation, leading to different assessments of compliance with the principles by different instruments (such as F-UJI, FAIR-Checker, or FAIR Evaluator), as all of them apply slightly different approaches to measure FAIRness. FAIR Signposting plays a critical role in standardizing FAIRness assessments by eliminating inconsistencies and ensuring a more consistent interpretation of the FAIR Principles across different assessment platforms. In the context of a pilot project of KonsortSWD, the FAIR Signposting standard was tested in some associated research data centers to improve their FAIRness scores by standardizing URIs. The study showed that the application of FAIR Signposting can significantly improve FAIRness scores. It moreover turned out that the approach has a great potential to become a key benchmark in standardizing FAIRness assessments. FAIR Signposting is a lightweight yet powerful standard for improving research datasets' discoverability, findability, and machine-actionability via machine-readable typed links. These links expose key metadata elements, such as persistent identifiers, access conditions, and licensing information, directly in the HTML or HTTP headers of dataset landing pages. This method embeds standard relation types (such as cite-as, describedby, license, author, item, and collection) into HTML headers, HTTP responses, or standalone linkset documents, guiding automated agents—such as search engines, data harvesters, and FAIR assessment tools - to the metadata, persistent identifiers (PIDs), and related resources as-sociated with digital objects. Doing so directly improves FAIRness evaluations while simplifying the navigation and retrieval of research data. The FAIR Signposting project of KonsortSWD aimed to adopt and implement the FAIR Signposting standard at research data centers associated with KonsortSWD, to improve their FAIRness scores by standardized URIs. The project was executed in 2024 through a dual approach: • Technical implementation – A prototype was deployed at GESIS - Leibniz Institute for the Social Sciences , followed by collaborative implementations at several partner institutions, including The Leibniz Institute for Educational Trajectories (LIfBi) , The Leibniz-Institute for Research and Information in Education (DIPF) , The German Institute for Economic Research (DIW/SOEP) , and The German Centre for Higher Education Research and Science Studies (DZHW) . • Knowledge sharing and best practices – A comprehensive best practice and guidance document was published, summarizing lessons learned, technical templates, validation methods, and strategic recommendations for scaling FAIR Signposting across RDCs and other research infrastructures. Each implementation story reflects unique challenges and solutions, providing valuable guidance for broader adoption. However, in all cases, measurable improvements in dataset FAIRness have been achieved, with F-UJI assessment scores rising significantly after implementation. For example, GESIS improved from 43% to 79%, and LIfBi from 41% to 83% of F-UJI FAIR score, despite varying technical infrastructures, content management systems, and metadata quality levels across partners. Common challenges included limited CMS flexibility, lack of server-side metadata generation, and absence of OAI-PMH end-points. These were addressed through tailored support, iterative testing, and community engagement. The project demonstrated that applying FAIR Signposting significantly improves the findability and FAIRness of research datasets. It enables machines to interpret and access metadata consistently, boosting data usability within KonsortSWD data centers. The approach is technically lightweight, cost-effective, and scalable, making it a valuable tool for repositories of all sizes. Furthermore, the project aligned with the broader goals of NFDI and the European Open Science Cloud (EOSC) by promoting metadata standardization, persistent identifiers, and automated FAIRness assessment.