Snowball Metrics – providing a robust methodology to inform research strategy – but do they help?
Author(s)
Darroch, Peter
Green, John
Issue Date
June 10, 2016
Publisher
euroCRIS
Source
"Communicating and Measuring Research Responsibly: Profiling, Metrics, Impact, Interoperability": Proceedings of the 13th International Conference on Current Research Information Systems (2016)
Procedia Computer Science 106: 11-18 (2017)
Type
Conference Paper
Abstract
Universities and funders need robust metrics to help them develop and monitor evidence-based strategies. Metrics are a part, albeit an important part, of the evaluation landscape, and no single metric can paint a holistic picture or inform strategy. A “basket of metrics” alongside other evaluation methods such as peer review are needed. Snowball Metrics offer a robust framework for measuring research performance and related data exchange and analysis, providing a consistent approach to information and measurement between institutions, funders and government bodies. The output of Snowball Metrics is a set of mutually agreed and tested methodologies: “recipes”. These recipes are available free-of-charge and can be used by anyone for their own purposes. A freely available API: the Snowball Metrics Exchange service (SMX), acts as a free “broker service” for the exchange of Snowball Metrics between peer institutions who agree that they would like to share information with each other and any institution can become a member of the SMX. In this paper, we present a use case where the University of St Andrews reviewed its institutional level KPIs referring to the Snowball Metrics recipes. In conclusion, quantitative data inform, but do not and should not ever replace, peer review judgments of research quality – whether in a national assessment exercise, or for any other purpose. Metrics can support human judgment and direct further investigation to pertinent areas, thus contributing to a fully rounded view on the research question being asked. We suggest using a “basket of metrics” approach measuring multiple qualities and applied to multiple entities.
Description
Delivered at the CRIS2016 Conference in St Andrews; published in Procedia Computer Science 106 (Mar 2017).-- Contains conference paper (8 pages) and presentation (16 slides).
URI
https://dspacecris.eurocris.org/handle/11366/516
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