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In 2018, the Data Curation Network (DCN) developed the CURATE(D) model, a standardized set of steps for curating research data with an eye toward the FAIR and CARE principles. The CURATE(D) model has proven to be a useful teaching tool for demonstrating data curation best practices; while practical and structured enough to provide a foundation for learners, it also provides enough flexibility to be adaptive for different disciplines and data format needs. The CURATE(D) model has been revised over the years to integrate feedback and keep pace with the evolving data curation profession. In the past year, the DCN has undertaken efforts to rework this model to be responsive to ethical and power considerations highlighted by data sovereignty and data justice movements. This has meant revising the guidance to make explicit the tacit, power-laden assumptions regarding data appraisal and selection criteria, sharing decisions, and the iterative nature of curation. In this presentation, attendees will be invited to compare the previous and current versions of this model, will learn about the revision process, and have the opportunity to provide feedback on the model.

Incomplete and inconsistent connections between institutional repository holdings and the global data infrastructure inhibit research data discovery and reusability. Preventing metadata loss on the path from institutional repositories to the global research infrastructure can substantially improve research data reusability. The Realities of Academic Data Sharing (RADS) Initiative, funded by the National Science Foundation, is investigating institutional processes for improving research data FAIRness. Focal points of the RADS inquiry are to understand where researchers are sharing their data and to assess metadata quality, i.e., completeness, at six Data Curation Network (DCN) academic institutions: Cornell University, Duke University, University of Michigan, University of Minnesota, Washington University in St. Louis, and Virginia Tech. RADS is examining where researchers are storing their data, considering local institutional repositories and other popular repositories, and analyzing the completeness of the research data metadata stored in these institutional and other repositories. Metadata FAIRness (Findable, Accessible, Interoperable, Reusable) is used as the metric to assess metadata quality as FAIR complete. Research findings show significant content loss when metadata from local institutional repositories are compared to metadata found in DataCite.After examining the factors contributing to this metadata loss, RADS investigators are developing a set of recommended best practices for institutions to increase the quality of their scholarly metadata. Further, documentation such as README files are of particular importance not only for data reuse, but as sources containing valuable metadata such as Persistent Identifiers (PIDs). DOIs and related PIDs such as ORCID and ROR are still rarely used in institutional repositories. More frequent use would have a positive effect on discoverability, interoperability and reusability, especially when transferring to global infrastructure.

ARL works with partners in the research library community to understand the implications of policy and determine the direction of our advocacy. The RADS Initiative provides analysis of policy implementation, specifically around research data sharing, which further informs our advocacy work. Collaborative practices contribute to a collective articulation of relevant issues in the field.