Research data management work and digital collections work follow similar workflows, principles, and practices, but are often situated outside of each other within cultural heritage organizations. And while “Collections as Data” and related initiatives have fostered opening digital collections data for computational analysis, data management standards and practices are not always considered in these settings. What if we were intentional about connecting digital collections work and data management practices? In this session, we’ll consider why this union makes sense, how to apply emerging metadata standards like the Schema.org Dataset type<https://schema.org/Dataset> and the RO-Crate (Research Object Crate) initiative<https://researchobject.github.io/ro-crate/> to digital collections, and the benefits to organizations in finding ways to connect this work. We’ll focus the discussion by grounding our work in two implementations: An API for a digital collection encoding cultural heritage objects as components in a dataset and a Progressive Web App using a structured datastore based on dataset encoding principles to power the search and browse of items. With these case studies as a guide, our goal will be to demonstrate why data management principles and practices complement digital library work and how digital library principles can inform research data management.