Objective: To demonstrate how to incorporate a data services librarian role into another role for a library, often where staff or workload limitations are prohibitive for hiring a specialist. Method: An R2 university which until recently was not a DRU, attempted to combine a traditional research and instruction liaison role to a subject specialty and a new data services role. The university, needing a subject specialist for a prominent set of disciplines, but also needing a librarian to handle the new demand (or expectation) for data services, combined the two roles into one position. Changes were made to the role of the outgoing librarian to accommodate the new data services role, while an essential body for reference and instruction was not lost to the department. Results: The initial structuring of duties has been managed without much issue. Selecting facets of data services to provide to the campus, rather than trying to contribute the entire corpus of data services, has effectively managed the workload of one staff member who also works normal reference, instruction, and liaison shifts within the library. Conclusions: Libraries with limited staffing abilities need not fear the inability to hire a full time data services librarian, so long as someone on the staff is willing to take the responsibility. The distribution of labor, while perhaps an addition to the workload of a staff member, need not be arduous or difficult provided that the individual(s) have a clear plan for how to provide the needed services for a campus.

Objective: One of the two major observed gaps in data management plan services at an aspirant Research University is the selection and implementation of a metadata schema. Previous instructional sessions with metadata focused on theory, but may not have reached learners effectively. To address these needs, three librarians have come together to plan a hands-on course to instruct researchers about the fundamentals of metadata. Methods: The instruction for faculty, staff, and graduate students will be taught in a single 90-minute workshop. Learners will be given an introduction to metadata before experimenting with a sample data set using OpenRefine and Dublin Core. Following this introduction, attendees will be given the opportunity to use their own dataset to experiment with Dublin Core conventions. Results (Expected): The course, expected to be taught in mid-April, is expected to provide learners with an easy-to-understand method of implementing metadata for their research projects. In-class hands-on practice should give attendees enough confidence to use Dublin Core or other metadata schema in their work. Conclusions (Expected): We expect that the course will have a higher attendance than previous sections of our Data Management Workshop Series, and we expect that a wider variety of attendees will be present. Without the pressure to commit to a full series, and the promotion of an active learning strategy, we would expect attendees to feel more confident that they have learned and will be able to implement valuable information.