Please use this identifier to cite or link to this item: http://hdl.handle.net/11366/516
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dc.contributor.authorClements, Annaen_US
dc.contributor.authorDarroch, Peteren_US
dc.contributor.authorGreen, Johnen_US
dc.date.accessioned2016-05-30T17:59:34Z-
dc.date.available2016-05-30T17:59:34Z-
dc.date.issued2016-06-10-
dc.identifier.citation"Communicating and Measuring Research Responsibly: Profiling, Metrics, Impact, Interoperability": Proceedings of the 13th International Conference on Current Research Information Systems (2016)en_US
dc.identifier.citationProcedia Computer Science 106: 11-18 (2017)-
dc.identifier.urihttp://hdl.handle.net/11366/516-
dc.descriptionDelivered at the CRIS2016 Conference in St Andrews; published in Procedia Computer Science 106 (Mar 2017).-- Contains conference paper (8 pages) and presentation (16 slides).en_US
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.en_US
dc.language.isoenen_US
dc.publishereuroCRISen_US
dc.relation.ispartofseriesCRIS2016: 13th International Conference on Current Research Information Systems (St Andrews, June 9-11, 2016)-
dc.subjectSnowball Metricsen_US
dc.subjectmetricsen_US
dc.subjectresearch metricsen_US
dc.subjectbasket of metricsen_US
dc.subjectSnowball Metrics Exchangeen_US
dc.subjectmetric recipeen_US
dc.subjectresearch evaluationen_US
dc.subjectmetric methodologyen_US
dc.subjectresearch impacten_US
dc.subjectresearch qualityen_US
dc.titleSnowball Metrics – providing a robust methodology to inform research strategy – but do they help?en_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.procs.2017.03.003-
dc.relation.conferenceCRIS2016 – St Andrewsen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.openairetypeConference Paper-
item.fulltextWith Fulltext-
item.languageiso639-1en-
crisitem.author.orcid0000-0003-4399-9881-
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CRIS2016_paper_26_Clements.pdfpost-print version160.21 kBAdobe PDF
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Snowball metrics CRIS 2016 presentation v4.pptxPPT presentation1.33 MBMicrosoft Powerpoint XML
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