Perhaps We Have Framed the Research Data Management Challenge Incorrectly?
Over the past year I’ve been doing a good deal of thinking about the broad way we’ve framed the work of research data management and preservation, and the roles of various parties (researchers, data curators, repositories, etc.) in this effort. The dominant model to date has been one of describing datasets, archiving them into repositories, and assuming that they will be discovered and reused by other scholars.
I’ll critically examine this model and some of the ideas — for example, the FAIR principles, privacy challenges, and widespread use of machine learning — place great stress on this model, along with the proliferation of what I’ll call “scholarly information aggregation and management environments”. I’ll speculate about what these developments may imply for how to reformulate the research data management enterprise, including some discussion about implications for funding, roles, and resource allocation and prioritization.
Clifford Lynch is the director of the Coalition for Networked Information (CNI) and an adjunct professor at the School of Information. Prior to joining CNI in 1997, Lynch spent eighteen years at the University of California Office of the President, the last ten as director of Library Automation. Lynch is a past president of ASIS&T and a fellow of the American Association for the Advancement of Science and the National Information Standards Organization.