Worries connected to Open Science
Fear-related barriers for Open Science.
In their publication, Houtkoop et al. (2018) gathered results from multiple former surveys as well as their own (N=600) that tried to identify barriers for researchers not to share their research materials. As rather objective barriers researchers often claim a lack of appropriate infrastructure, lack of data management skills and training (or: too time consuming), and worries about licensing or sensitive data restrictions. Since those survey results were published, great developments on appropriate data management infrastructure happened, a vast variety of free resources and trainings are made available, and data specific user and sharing agreements as well as access policies have been developed. Yet, researchers still carry some more concerns about data sharing, which Houtkoop et al. (2018) call the “fear-related barriers”. These barriers mainly include the fear of
a) being scooped (i.e., other researchers publishing results from the data before the primary researchers can)
b) rejection of conclusions as a result of alternative analyses of the shared data
c) loss of control over intellectual property
d) detection of errors in the data
e) misinterpretation of the data by secondary users
f) use of shared data for unintended purposes.
(Houtkoop et al., 2018, p. 77)
Interestingly, researchers agreed more with the fear-statements when they were asked to indicate to what extent they agree with the statements about other researchers’ fear-related barriers compared to what extent they agree about their own fear-related barriers (see figure below, ). The authors explain this result by the effect of socially desirable responding.
As for reasons a), c), e), and f) the developments in Open Science since publication of this paper should have reduced this fear by now: Appropriate licensing, dataset documentation, and data user agreements can be easily set up by now. All of this ensures that you will be rightfully acknowledged for your hard work and data is only used for the purposes you intended. However, reasons b) and d) cannot be controlled by laws or contracts. In fact, it is very likely that errors in your data or code will be detected and hence your conclusion will be challenged. Yet, there is a way to get (at least some) control over this fear anyway: As scientists we’re all part of a community and as a community it is our responsibility to form the values of this community. After all, we’re all human and humans make mistakes. Instead of pretending that this is not the case, we should acknowledge the transparency and work as a community on an informed critical discourse: foster real collaboration for the purpose of finding true (i.e., replicable) answers.
Isn’t this what science is all about?