Abstract
The SHARC Interest Group of the Research Data Alliance was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data (and material resources) for community sharing. This requires that data are findable and accessible on the Web, and comply with shared standards making them interoperable and reusable in alignment with the FAIR principles. It takes considerable time, energy, expertise and motivation. It is imperative to facilitate the processes to encourage scientists to share their data. To that aim, supporting FAIR principles compliance processes and increasing the human understanding of FAIRness criteria-i.e., promoting FAIRness literacy-and not only the machine-readability of the criteria, are critical steps in the data sharing process. Appropriate human-understandable criteria must be the first identified in the FAIRness assessment processes and roadmap. This paper reports on the lessons learned from the RDA SHARC Interest Group on identifying the processes required to prepare FAIR implementation in various communities not specifically data skilled, and on the procedures and training that must be deployed and adapted to each practice and level of understanding. These are essential milestones in developing adapted support and credit back mechanisms not yet in place.