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Grand challenges in altmetrics: heterogeneity, data quality and dependencies


With 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.

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Fig. 1




  3. The loss might be avoided or at least mitigated by maintaining a dark archive, which was mentioned by Altmetric founder Euan Adie in a tweet:

  4. In the short history of altmetrics, such loss can already be observed for Connotea.


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The author acknowledges funding from the Alfred P. Sloan Foundation Grant # 2014-3-25. She would also like to thank Vincent Larivière for stimulating discussions and helpful suggestions on the manuscript, as well as Sam Work for proofreading.

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Correspondence to Stefanie Haustein.

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Haustein, S. Grand challenges in altmetrics: heterogeneity, data quality and dependencies. Scientometrics 108, 413–423 (2016).

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  • Big data
  • Data integration
  • Research and innovation policy
  • Data quality
  • Comparability
  • Standardization
  • Concordance tables
  • Modularization
  • Interoperability
  • Research assessment