What Drives Academic Data Sharing?
AbstractDespite 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by German Council for Social and Economic Data (RatSWD) in its series Working Paper Series of the German Council for Social and Economic Data with number 236.
Date of creation: 2014
Date of revision:
ratswd; ratswd working paper; Data Sharing; Academia; Systematic Review; Research Policy; Knowledge Commons; Crowd Science; Commons-based Peer Production;
Find related papers by 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, and Operations
- 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; Social and Economic Stratification
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-05-09 (All new papers)
- NEP-INO-2014-05-09 (Innovation)
- NEP-SOG-2014-05-09 (Sociology of Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- B.D. McCullough, 2009. "Open Access Economics Journals and the Market for Reproducible Economic Research," Economic Analysis and Policy (EAP), Queensland University of Technology (QUT), School of Economics and Finance, vol. 39(1), pages 117-126, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (RatSWD) The email address of this maintainer does not seem to be valid anymore. Please ask RatSWD to update the entry or send us the correct address.
If references are entirely missing, you can add them using this form.