Implementing a Graduate Class in Research Data Management for Science and Engineering Students

Abstract Practical knowledge and training in Research Data Management (RDM) is critical for today’s researchers. Effective practices in RDM result in thoughtful and thorough use of data for publication and sharing. Additionally RDM plans through a Data Management Plan (DMP) have become required for federal research funding proposals. Literature studies report that graduate students are not prepared to effectively manage their research data. While faculty understand the need for RDM education for graduate students, they also acknowledge that they cannot individually provide the necessary guidance or instruction. The National Science Foundation also encourages “full engagement” of graduate students in the RDM process. To fulfill this need, libraries have developed seminars/workshops and standalone courses. However, the seminars do not offer the necessary in-depth material coverage, and in some cases, standalone courses have difficulty attracting students since the librarians do not have basic research experience to provide working examples. Thus, there is a need for both RDM education for graduate students and an effective model for delivering the interdisciplinary material needed for this topic. A RDM course offered in a specific discipline was developed and co-taught by a librarian and a research active faculty member. The course was not necessarily discipline specific and designed to provide the knowledge encouraged by NSF and which faculty members note they cannot provide. The goal of the combined teaching approach was to allow the librarian to provide broad knowledge on RDM tools and standards while also allowing the faculty researcher to provide focused examples and experience. Specific goals of the course were: 1) Expose the students to broad concepts and best practices of research data management 2) Bring in outside experts to demonstrate specific areas of RDM, and 3) Provide a focused application of RDM to active research projects. Topics under broad concepts and best practices included: Data and Data Lifecycles, Describing Your Data, and Planning Your Research Topic. Guest expert lectures were focused on specific applications of RDM: metadata, data management on an interdisciplinary project, RDM tools available within the university, and the primary investigator for a multi-university data intensive research project. Focused application of RDM to research projects included DMPtool and development of a Data Curation Profile (DCP) for an ongoing research project. Pre-course and post-course assessment was performed to determine the students’ knowledge about their current laboratory RDM practices and eight specific areas of RDM. In this paper, details on the structure and content of this RDM course will be presented. Textbook and other materials, student assignments, and lecture topics will be presented. The benefits from the combined librarian/research faculty approach to co-teaching the course will be discussed. The course will be compared/contrasted with previous approaches to teaching RDM. Assessment results will be presented along feedback from the faculty researchers participating in the DCP project.