Snowball Metrics – providing a robust methodology to inform research strategy – but do they help?
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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.
Clements , A , Darroch , P I & Green , J 2017 , ' Snowball Metrics – providing a robust methodology to inform research strategy – but do they help? ' Procedia Computer Science , vol 106 , pp. 11-18 . DOI: 10.1016/j.procs.2017.03.003
Procedia Computer Science
© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. https://creativecommons.org/licenses/by-nc-nd/4.0/
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