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What drives academic data sharing?

Fecher B, Friesike S, Hebing M - PLoS ONE (2015)

Bottom Line: It allows the reproducibility of study results and the reuse of old data for new research questions.We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients.We conclude that research data cannot be regarded as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.

View Article: PubMed Central - PubMed

Affiliation: Internet-enabled Innovation, Alexander von Humboldt Institute for Internet and Society, Berlin, Germany; Research Infrastructure, German Institute for Economic Research, Berlin, Germany.

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 as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.

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Research Methodology.
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pone.0118053.g001: Research Methodology.

Mentions: In order to arrive at a framework for data sharing in academia, we used a systematic review of scholarly articles and an empirical survey among secondary data users (SOEP User survey). The first served to design a preliminary category system, the second to empirically revise it. In this section, we delineate our methodological approach as well as its limitations. Fig. 1 illustrates the research methodology.


What drives academic data sharing?

Fecher B, Friesike S, Hebing M - PLoS ONE (2015)

Research Methodology.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4340811&req=5

pone.0118053.g001: Research Methodology.
Mentions: In order to arrive at a framework for data sharing in academia, we used a systematic review of scholarly articles and an empirical survey among secondary data users (SOEP User survey). The first served to design a preliminary category system, the second to empirically revise it. In this section, we delineate our methodological approach as well as its limitations. Fig. 1 illustrates the research methodology.

Bottom Line: It allows the reproducibility of study results and the reuse of old data for new research questions.We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients.We conclude that research data cannot be regarded as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.

View Article: PubMed Central - PubMed

Affiliation: Internet-enabled Innovation, Alexander von Humboldt Institute for Internet and Society, Berlin, Germany; Research Infrastructure, German Institute for Economic Research, Berlin, Germany.

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 as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.

Show MeSH