Research Data Management Commitment Drivers: An Analysis of Practices, Training, Policies, Infrastructure, and Motivation in Global Agricultural Science

Sebastian Feger, Cininta Pertiwi, Enrico Bonaiuti. (11/11/2022). Research Data Management Commitment Drivers: An Analysis of Practices, Training, Policies, Infrastructure, and Motivation in Global Agricultural Science. Proceedings of the ACM on Human-Computer Interaction, 6 (CSCW2), pp. 1-36.

Abstract

Scientists largely acknowledge the value of research data management (RDM) to enable reproducibility and reuse. But, RDM practices are not sufficiently rewarded within the traditional academic reputation economy. Recent work showed that emerging RDM tools can offer new incentives and rewards. But, the design of such platforms and scientists’ commitment to RDM is contingent on additional factors, including policies, training, and several types of personal motivation. To date, studies focused on investigating single or few of those RDM components within a given environment. In contrast, we conducted three studies within a global agricultural science organization, to provide a more complete account of RDM commitment drivers: one survey study (n = 23) and two qualitative explorations of regulatory frameworks (n = 17), as well as motivation, infrastructure, and training components (n = 13). Based on the sum of findings, we contribute to the triangulation of a recent RDM commitment evolution model. In particular, we find that strong support and suitable tools help develop RDM commitment, while policy conflicts, unclear data standards, and multi platform sharing, lead to unexpected negotiation processes. We expect that these findings will help to better understand RDM commitment drivers, to refine the RDM commitment evolution model, and to benefit its application in science.