The FAIR principles were created with the goal of enhancing the reusability of research data, and to give guidance to data creators, curators and publishers on how to make data Findable, Accessible, Interoperable and Reusable. In addition to being good for science, implementing FAIR, along with other principles such as CARE, can lead to making data access more equitable and ethical. In order to implement these aspirational principles, incorporating FAIR into research data policies at all levels (funder, publisher, and institutional), is vital. In this talk we will explore the role of institutional research data policies in enabling and encouraging researchers at their institutions to generate FAIR data. We identified the research data policies in place for “very high research activity” institutions (as defined by Carnegie classification) in the United States. We created a list of 31 criteria, based on the FAIRsFAIR project criteria for policies and on the previous work of Briney et al (2015) and evaluated the 42 policies. We will discuss how different policies have elements that support the FAIR principles; for example, we will examine the existing models regarding data sharing, and how data sharing is allowed, encouraged or required in these policies. Other criteria that we analyzed include the research infrastructures and repositories mentioned in the policies, references to data protection and to data management plans, and support for compliance. We will present specific examples that will inform participants about the options that other universities have chosen for their policies. These examples will provide a framework for those working with their institutions to create or update an institutional research data management policy. At this time, institutional policies are not being used as a tool to facilitate the adoption of FAIR. This presentation will show some first steps that can be taken in that direction.