This study demonstrates how applying the FAIR Signposting standard can significantly enhance the discoverability, accessibility, and, therefore, the reusability of research datasets. As researchers increasingly share data, ensuring it meets FAIR (Findable, Accessible, Interoperable, Reusable) criteria is essential to boost their data visibility. However, current assessment tools (like F-UJI, FAIR-Checker, and FAIR Evaluator) often interpret these principles differently. FAIR Signposting addresses this by embedding standardized, machine-readable metadata directly into dataset landing pages via HTML or HTTP headers, thus eliminating inconsistencies and ensuring a more consistent interpretation of the FAIR Principles across different assessment platforms. For individual researchers, persistent identifiers (such as DOIs), licensing terms, access conditions, and citation information are easily accessible to search engines, data harvesters, and other automated agents. This leads to higher FAIRness scores and significantly greater visibility for their datasets. 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. 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). The study showed that applying FAIR Signposting can significantly improve FAIRness scores. The approach has great potential to become a key benchmark in standardizing FAIRness assessments, as it directly improved FAIR assessment scores by up to 40 percentage points. 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. The approach is technically lightweight, scalable, and does not require complex infrastructure, maximizing research data's impact, transparency, and potential reuse.