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How research data deliver non-academic impacts: A secondary analysis of UK Research Excellence Framework impact case studies

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  • Eric A Jensen
  • Paul Wong
  • Mark S Reed

Abstract

This study investigates how research data contributes to non-academic impacts using a secondary analysis of high-scoring impact case studies from the UK’s Research Excellence Framework (REF). A content analysis was conducted to identify patterns, linking research data and impact. The most prevalent type of research data-driven impact related to “practice” (45%), which included changing how professionals operate, changing organizational culture and improving workplace productivity or outcomes. The second most common category was “government impacts”, including reducing government service costs and enhancing government effectiveness or efficiency. Impacts from research data were developed most frequently through “improved institutional processes or methods” (40%) and developing impact via pre-analyzed or curated information in reports (32%), followed by “analytic software or methods” (26%). The analysis found that research data on their own rarely generate impacts. Instead they require analysis, curation, product development or other forms of significant intervention to leverage broader non-academic impacts.

Suggested Citation

  • Eric A Jensen & Paul Wong & Mark S Reed, 2022. "How research data deliver non-academic impacts: A secondary analysis of UK Research Excellence Framework impact case studies," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-12, March.
    • Handle: RePEc:plo:pone00:0264914
    DOI: 10.1371/journal.pone.0264914
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    References listed on IDEAS

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    1. Grün, Bettina & Hornik, Kurt, 2011. "topicmodels: An R Package for Fitting Topic Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i13).
    2. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    3. Bella Reichard & Mark S Reed & Jenn Chubb & Ged Hall & Lucy Jowett & Alisha Peart & Andrea Whittle, 2020. "Writing impact case studies: a comparative study of high-scoring and low-scoring case studies from REF2014," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-17, December.
    4. Oecd, 2019. "Reference framework for assessing the scientific and socio-economic impact of research infrastructures," OECD Science, Technology and Industry Policy Papers 65, OECD Publishing.
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