Research data management (RDM) is as old as research itself. Scholars across the centuries have developed ways to organize and preserve the materials they work with. In today’s age of seemingly boundless technological possibilities, tool options, and ideas, more and more funders and institutions are outlining formal requirements around RDM to help overwhelmed researchers find their way to the latest best practices in handling data.
These requirements aren’t always experienced as helpful, of course. The lingo of RDM can sound alien; data management plan (DMP) templates don’t always align with recognizable research practices in different disciplines; tools for proper RDM are sometimes missing; and so on. These problems are (almost) as annoying for research support librarians like myself as they are for researchers themselves.
In moments of frustration, I like to go back to this 2015 video by data librarian Kristin Briney about the possibilities of RDM. Enjoy fifteen minutes of data inspiration:
The United States spends billions of dollars every year to publicly support research that has resulted in critical innovations and new technologies. Unfortunately, the outcome of this work, published articles, only provides the story of the research and not the actual research itself. This often results in the publication of irreproducible studies or even falsified findings, and it requires significant resources to discern the good research from the bad. There is way to improve this process, however, and that is to publish both the article and the data supporting the research. Shared data helps researchers identify irreproducible results. Additionally, shared data can be reused in new ways to generate new innovations and technologies. We need researchers to “React Differently” with respect to their data to make the research process more efficient, transparent, and accountable to the public that funds them.
Find more articles, resources, and general interesting stuff in the Artes Digital Scholarship Community on Zotero.