Making and keeping data FAIR requires extensive and complex technical infrastructure, but it also requires systematic and sustained improvements in the human practices of managing research data. These improvements can be brought about through training, mentoring and recognition measures, which naturally also have implications for policymaking at the supra-national, sectoral, national, institutional, departmental and project levels. ACME-FAIR is a 7-part guide developed in the FAIRsFAIR project, whose main purpose is to help managers of Research Data Management and related professional services to self-assess how they are enabling researchers, and the professional staff who support them, to put the FAIR data principles into practice (for short we refer to this as ���FAIR-enabling practice���). This part addresses the key issue of Professionalising Roles through Training, Mentoring, and Recognition. We welcome your feedback.