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The University of Iowa Data Services Manager and the Engineering and Informatics Librarian are developing a set of Research Data Management (RDM) instructional components that can be used within and across multi-disciplinary research teams. Beginning this spring and through the coming year, we will be developing instructional materials for the Iowa Superfund Research Program, which currently has six research project teams, supported by several central support cores (e.g., training core, administrative core, analytical core). The program is funded by the National Institute of Environmental Health Sciences (NIEHS), which is part of the National Institutes of Health (US).
FAIR data principles are expressly noted in the NIEHS funding opportunity announcement for the next round of Superfund grants. The connections to data science, guiding principles for data sharing, and local research support infrastructure have also been noted by NIH. These factors have raised the awareness among researchers of FAIR guiding principles. This, in turn, contributed significantly to the opportunities for collaboration with the Libraries on the research proposal, development of RDM training, and plans for exploration of infrastructure in the next funding cycle.
Our poster will highlight some of the implications of FAIR data on instruction and infrastructure decisions, both for the current program, and the next iteration of the center, if funded. We will summarize the results of a literature review and environmental scan of resources for instructional materials on FAIR data. The opportunities for internal and external collaborations afforded by the emphasis on FAIR data principles by NIH and other organizations will also be described.