Since the publication of the “FAIR Guiding Principles for scientific data management and stewardship” (Wilkinson et al., 2016), a wide-variety of sciences, mathematics, and other data-intensive fields have utilized the principles to promote reusability of data and ensure the long-term preservation of valuable outputs. In particular, domain experts and leading researchers have organized hackathons and sprints to define FAIR for their specific domains. Similarly, these domain experts have created tools to evaluate their data and research outputs against FAIR metrics. This has been driven in part by fragile digital objects and perpetually changing hardware and software environments necessary to create, edit, manipulate, and visualize the data. DH initiatives share many of these concerns; rapidly changing technical ecosystems, fragile digital objects and data, as well as funding concerns from grant or temporary funding, all have significant impacts on the long-term sustainability and reusability of DH projects and outputs. Overcoming these challenges necessitates data that is easily understandable, reusable, and preservable into the long-term. In this presentation, attendees will learn the FAIR data principles, with specific examples of current tools, metrics, indicators of success, and automated evaluators. This presentation will end with a call to action to digital humanists, data librarians, and other collaborators to engage in the FAIR practice, defining standards for success, and creating domain-specific tools. In short, what does FAIR DH look like, and how do we get there?
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