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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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Flowchart selection process.
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pone.0118053.g002: Flowchart selection process.

Mentions: In order to find the relevant papers for our research intent, we defined a research question (Which factors influence data sharing in academia?) as well as explicit selection criteria for the inclusion of papers [12]. According to the criteria, the papers needed to address the perspective of the primary researcher, focus on academia and stem from defined evaluation period. To ensure an as exhaustive first sample of papers as possible, we used a broad basis of multidisciplinary data banks (see Table 1) and a search term (“data sharing”) that generated a high number of search results. We did not limit our sample to research papers but also included for example discussion papers. In the first sample we included every paper that has the search term in either title or abstract. Fig. 2 summarizes the selection process of the papers.


What drives academic data sharing?

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

Flowchart selection process.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0118053.g002: Flowchart selection process.
Mentions: In order to find the relevant papers for our research intent, we defined a research question (Which factors influence data sharing in academia?) as well as explicit selection criteria for the inclusion of papers [12]. According to the criteria, the papers needed to address the perspective of the primary researcher, focus on academia and stem from defined evaluation period. To ensure an as exhaustive first sample of papers as possible, we used a broad basis of multidisciplinary data banks (see Table 1) and a search term (“data sharing”) that generated a high number of search results. We did not limit our sample to research papers but also included for example discussion papers. In the first sample we included every paper that has the search term in either title or abstract. Fig. 2 summarizes the selection process of the papers.

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