Data Citation Impediments: Human and Institutional Inertia

Data citations are growing in visibility in scientific and public policy circles. Data citations directly link scholarship and data, and as such provide a mechanism through which data can be discovered and accessed, scholarly use of data can be tracked, and the impact of data facilities can be identified. The interest in data citations is coming from many research stakeholders, including funders, policy makers, professional societies and their publication entities, research organizations, and individual researchers. Most of the efforts to date around data citations have focused on the challenges of assigning unique identifiers to digital data sets. While these challenges are significant, an additional challenge has gone relatively unaddressed, namely, the fact that data citation is not a common practice within scientific communities. This presentation will present findings from an interview study within the University Corporation for Atmospheric Research / National Center for Atmospheric Research (UCAR/NCAR). Through interviews with 14 scientists and engineers, we have found that there is little evidence that data citations have gained momentum as a common practice. Currently, data users acknowledge their use of particular data sets in either the research methods or acknowledgements sections of their papers, not as formal citations in a paper's bibliography. Data users are often 1) unaware that they can and should cite data sets, 2) unsure of how to cite data sets, and 3) lacking career motivations to forward data citations as a common activity. Data citation initiatives will have minimal impact on the scientific community if they do not address this practical inertia. Data users are a critical stakeholder in the data citation process. Their voice needs to be central to the data citation discussion. We will discuss how outreach efforts need to focus on raising the profile of data citations by informing scientists and administrators, being proactive in providing data users with recommended citations, and embedding data citations within larger scientific research institutions like academic tenure and scholarly peer review.