Scientists are increasingly required by funding agencies, publishers and their institutions to produce and publish data that are Findable, Accessible, Interoperable and Reusable (FAIR). This requires curatorial activities, which are expensive in terms of both time and effort. Based on our experience of supporting a multidisciplinary research team, we provide recommendations to direct the efforts of researchers towards affordable ways to achieve a reasonable degree of "FAIRness" for their data to become reusable upon its publication. The recommendations are accompanied by concrete insights on the challenges faced when trying to implement them in an actual data-intensive reference project.