Making Data Count: Developing a Data Metrics Pilot

University Of California, Office Of The President, Oakland, Oakland CA

Investigators

Abstract

The research community has been calling for solutions to data discovery and to more broadly capture the value of the work that is at the core of the researcher's scholarly pursuit. The University of California Curation Center at the California Digital Library will work with PLOS and DataONE to address what metrics are needed to capture the activity surrounding research data in a valid and credible way. This collaborative team will prototype a suite of metrics that track and measure data use, "data-level metrics" (DLM), which will measure the broad range of activity surrounding the reach and use of data as a research output. The project will augment the existing scholarly cyberinfrastructure, which currently is focused on journal articles, and introduce data as a valued scholarly output into the framework. Data metrics will create incentives that support data sharing and usage to increase the velocity of information dissemination across a wide range of disciplines, once the impact of the research is exposed. The team will build a reference model for data metrics based on automatic tracking based on in-depth field analyses of data use and engagement practices. They will test mechanisms of automatic tracking of data activity and explore ways in which the dynamic data harvested can be delivered to drive data discovery as well as support reporting needs for funders and institutions. DLM data will provide a clear and growing picture of the activity around the dissemination and reach of research data. As this activity is linked to other research entities and objects in the research information ecosystem, an expansive portrait of the dynamics of data use and reuse will emerge, which can inform the community's understanding of what impact means for research data as a scholarly output. In the comprehensive ecosystem of identifiable, trackable research data, these metrics tools will become essential to data-rich science.