We all know that data are important for research. So how can we quantify that? How can you get credit for the data you produce? What do you want to know about how your data is used? If you are a researcher or data manager, we want to hear from you. Take this 5-10 minute survey and help us craft data-level metrics:
Please share widely! The survey closes December 1st.
The responses will directly be fed into a broader project to design and develop metrics that track and measure data use, i.e. “data-level metrics” (DLM). See an earlier blog post for more detail on the NSF-funded project, Making Data Count, that is a partnership between PLOS, CDL, and DataONE.
DLM are a multi-dimensional suite of indicators, measuring the broad range of activity surrounding the reach and use of data as a research output. They will provide a clear and growing picture of the activity around, direct, first-hand views of the dissemination of, and reach of research data. These indicators capture the footprint of the data from the moment of deposition in a repository through to its dynamic evolution over time. DLM are automatically tracked, thus reducing the burden of reporting and potentially increasing consistency. Our aims in measuring the level and type of data usage across many channels are plain:
- make it possible for data producers to get credit for their work
- prototype a platform so that these footprints can be automatically harvested
- make all DLM data freely available to all (open metrics!)
At the moment, we are canvassing researchers and data managers to better understand data sharing attitudes and perceptions to identify core values for data use and reuse to describe existing norms surrounding the use of and sharing of data. The survey answers combined with the previous and ongoing research (ex: Tenopir et al., Callaghan et al.) will serve as the basis for this work ahead. They will be converted into requirements for an industry-wide data metrics platform. We will explore metrics that can be generalized across broad research areas and communities of practice, including life sciences, physical sciences, and social sciences. The resulting framework and prototype will represent the connections between data and the various channels in which engagement with the data is occurring. We will then test the validity of the pilot DLMs with real data in the wild and explore the extent to which automatic tracking is a viable approach for implementation.