Paper on the Incentives for Academic Data Sharing published: „What Drives Academic Sharing?“

Benedikt Fecher, Sascha Friesike, and Marcel Hebing have published another paper presenting further results of their study concerning academic data sharing. Since data sharing enables researchers to verify results and to pursuit new research questions with “old” data, it is of particular importance for scientific progress.

Fecher, Friesike, and Hebing conducted a systematic review of 98 scholarly papers as well as an empirical survey among 603 secondary data users. In order to explain the data sharing process from the primary researcher’s point of view, the authors introduce a conceptual framework based on the analyses. They divide the data sharing process into six descriptive categories: data donor, research organization, research community, norms, data infrastructure, and data recipients.

Fecher, Friesike, and Hebing conclude that data is not a common good, because most authors consider themselves to be the owner of data they collected and at the same time want to keep control of that data. This is reflected in the perceived right to publish first and in the fear of data misuse. Another reason is the lack of sufficient formal recognition for data publications. All in all, authors are rather incentivised not to share their data than incentivised to share.

That is why Fecher, Friesike, and Hebing claim that research policies are needed in order to incentivise data sharing and at the same time increase the quality of data. They give concrete recommendations concerning possible incentives for sharing data as well as possibilities to impede researchers not to share: an adequate formal recognition, forms of financial reimbursement, journal policies including mandatory data sharing policies, easy-to-use data management systems, an understandable and clear legal basis concerning the rights of use, clear guidelines regarding the receipt of consent and the possibilities of anonymizing data, as well as educational efforts within data-driven research fields.

The paper is available here.

Image: “Shags Excepted” by Michael Coghlan / flickr.com. License: CC BY-NC-SA 2.0