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What Drives Academic Data Sharing?

Author

Listed:
  • Benedikt Fecher
  • Sascha Friesike
  • Marcel Hebing

Abstract

Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded a knowledge commons, but research policies that better incentivize data sharing are needed to improve the quality of research results and foster scientific progress.

Suggested Citation

  • Benedikt Fecher & Sascha Friesike & Marcel Hebing, 2014. "What Drives Academic Data Sharing?," RatSWD Working Papers 236, German Data Forum (RatSWD).
  • Handle: RePEc:rsw:rswwps:rswwps236
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    References listed on IDEAS

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    More about this item

    Keywords

    ratswd; ratswd working paper; Data Sharing; Academia; Systematic Review; Research Policy; Knowledge Commons; Crowd Science; Commons-based Peer Production;
    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
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • L17 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Open Source Products and Markets
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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