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A Reputation Economy: Results from an Empirical Survey on Academic Data Sharing

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  • Benedikt Fecher
  • Sascha Friesike
  • Marcel Hebing
  • Stephanie Linek
  • Armin Sauermann

Abstract

Academic data sharing is a way for researchers to collaborate and thereby meet the needs of an increasingly complex research landscape. It enables researchers to verify results and to pursuit new research questions with “old” data. It is therefore not surprising that data sharing is advocated by funding agencies, journals, and researchers alike. We surveyed 2661 individual academic researchers across all disciplines on their dealings with data, their publication practices, and motives for sharing or withholding research data. The results for 1564 valid responses show that researchers across disciplines recognise the benefit of secondary research data for their own work and for scientific progress as a whole—still they only practice it in moderation. An explanation for this evidence could be an academic system that is not driven by monetary incentives, nor the desire for scientific progress, but by individual reputation—expressed in (high ranked journal) publications. We label this system a Reputation Economy. This special economy explains our findings that show that researchers have a nuanced idea how to provide adequate formal recognition for making data available to others—namely data citations. We conclude that data sharing will only be widely adopted among research professionals if sharing pays in form of reputation. Thus, policy measures that intend to foster research collaboration need to understand academia as a reputation economy. Successful measures must value intermediate products, such as research data, more highly than it is the case now.

Suggested Citation

  • Benedikt Fecher & Sascha Friesike & Marcel Hebing & Stephanie Linek & Armin Sauermann, 2015. "A Reputation Economy: Results from an Empirical Survey on Academic Data Sharing," Discussion Papers of DIW Berlin 1454, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1454
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    References listed on IDEAS

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    1. Heather A Piwowar & Michael J Becich & Howard Bilofsky & Rebecca S Crowley & on behalf of the caBIG Data Sharing and Intellectual Capital Workspace, 2008. "Towards a Data Sharing Culture: Recommendations for Leadership from Academic Health Centers," PLOS Medicine, Public Library of Science, vol. 5(9), pages 1-5, September.
    2. Andreoli-Versbach, Patrick & Mueller-Langer, Frank, 2014. "Open access to data: An ideal professed but not practised," Research Policy, Elsevier, vol. 43(9), pages 1621-1633.
    3. Gert G. Wagner & Joachim R. Frick & Jürgen Schupp, 2007. "The German Socio-Economic Panel Study (SOEP) – Scope, Evolution and Enhancements," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 127(1), pages 139-169.
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    Cited by:

    1. Linek, Stephanie B. & Fecher, Benedikt & Friesike, Sascha & Hebing, Marcel, 2017. "Data sharing as social dilemma: Influence of the researcher’s personality," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(8), pages 1-24.
    2. Isabella Peters & Peter Kraker & Elisabeth Lex & Christian Gumpenberger & Juan Gorraiz, 2016. "Research data explored: an extended analysis of citations and altmetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 723-744, May.
    3. Stephanie B Linek & Benedikt Fecher & Sascha Friesike & Marcel Hebing, 2017. "Data sharing as social dilemma: Influence of the researcher’s personality," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-24, August.
    4. Claire M Mason & Paul J Box & Shanae M Burns, 2020. "Research data sharing in the Australian national science agency: Understanding the relative importance of organisational, disciplinary and domain-specific influences," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.

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    More about this item

    Keywords

    Data sharing; research policy; reputation; science; survey research;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • L89 - Industrial Organization - - Industry Studies: Services - - - Other

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