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dc.contributor.authorHaustein, Stefanie
dc.date.accessioned2020-04-20T19:02:09Z
dc.date.availableNO_RESTRICTIONfr
dc.date.available2020-04-20T19:02:09Z
dc.date.issued2016-03-14
dc.identifier.urihttp://hdl.handle.net/1866/23286
dc.publisherSpringerfr
dc.subjectBig datafr
dc.subjectData integrationfr
dc.subjectResearch and innovation policyfr
dc.subjectData qualityfr
dc.subjectComparabilityfr
dc.subjectStandardizationfr
dc.subjectConcordance tablesfr
dc.subjectModularizationfr
dc.subjectInteroperabilityfr
dc.subjectResearch assessmentfr
dc.titleGrand challenges in altmetrics : heterogeneity, data quality and dependenciesfr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. Faculté des arts et des sciences. École de bibliothéconomie et des sciences de l'informationfr
dc.identifier.doi10.1007/s11192-016-1910-9
dcterms.abstractWith increasing uptake among researchers, social media are finding their way into scholarly communication and, under the umbrella term altmetrics, are starting to be utilized in research evaluation. Fueled by technological possibilities and an increasing demand to demonstrate impact beyond the scientific community, altmetrics have received great attention as potential democratizers of the scientific reward system and indicators of societal impact. This paper focuses on the current challenges for altmetrics. Heterogeneity, data quality and particular dependencies are identified as the three major issues and discussed in detail with an emphasis on past developments in bibliometrics. The heterogeneity of altmetrics reflects the diversity of the acts and online events, most of which take place on social media platforms. This heterogeneity has made it difficult to establish a common definition or conceptual framework. Data quality issues become apparent in the lack of accuracy, consistency and replicability of various altmetrics, which is largely affected by the dynamic nature of social media events. Furthermore altmetrics are shaped by technical possibilities and are particularly dependent on the availability of APIs and DOIs, strongly dependent on data providers and aggregators, and potentially influenced by the technical affordances of underlying platforms.fr
dcterms.isPartOfurn:ISSN:0138-9130fr
dcterms.isPartOfurn:ISSN:1588-2861fr
dcterms.languageengfr
UdeM.ReferenceFournieParDeposantGrand challenges in altmetrics: heterogeneity, data quality and dependencies Haustein, S. (2016). Grand challenges in altmetrics: heterogeneity, data quality and dependencies. Scientometrics.fr
UdeM.VersionRioxxVersion acceptée / Accepted Manuscriptfr
oaire.citationTitleScientometrics
oaire.citationVolume108
oaire.citationStartPage413
oaire.citationEndPage423


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