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dc.contributor.authorClements, Anna
dc.contributor.authorDarroch, Peter I.
dc.contributor.authorGreen, John
dc.date.accessioned2017-03-27T14:30:16Z
dc.date.available2017-03-27T14:30:16Z
dc.date.issued2017-03-21
dc.identifier.citationClements , 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 . https://doi.org/10.1016/j.procs.2017.03.003en
dc.identifier.issn1877-0509
dc.identifier.otherPURE: 249439349
dc.identifier.otherPURE UUID: ca222c70-dc09-43a6-b267-2a0ccd87e55b
dc.identifier.otherScopus: 85020735235
dc.identifier.otherORCID: /0000-0003-2895-1310/work/31348265
dc.identifier.otherWOS: 000398828200003
dc.identifier.urihttp://hdl.handle.net/10023/10533
dc.description.abstractUniversities 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.
dc.format.extent8
dc.language.isoeng
dc.relation.ispartofProcedia Computer Scienceen
dc.rights© 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/en
dc.subjectSnowball Metricsen
dc.subjectMetricsen
dc.subjectResearch metricsen
dc.subjectBasket of metricsen
dc.subjectSnowball Metrics Exchangeen
dc.subjectMetric recipeen
dc.subjectResearch evaluationen
dc.subjectMetric methodologyen
dc.subjectResearch impacten
dc.subjectResearch qualityen
dc.subjectZ665 Library Science. Information Scienceen
dc.subjectZA4050 Electronic information resourcesen
dc.subjectQA76 Computer softwareen
dc.subject3rd-DASen
dc.subject.lccZ665en
dc.subject.lccZA4050en
dc.subject.lccQA76en
dc.titleSnowball Metrics – providing a robust methodology to inform research strategy – but do they help?en
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.University of St Andrewsen
dc.identifier.doihttps://doi.org/10.1016/j.procs.2017.03.003
dc.description.statusPeer revieweden


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