Making Data Count: A Practical Framework for Engaging Researchers in Open Science

While sharing research data openly benefits the broader scientific community, it requires significant time and effort and is often perceived to have little benefit to the individual researcher. Contributing to this perception is the fact that data sharing is not formally recognized in tenure or career advancement decisions at most academic institutions. To make open science more appealing and sustainable, it is important to address both the perceived benefit of and effort required for data sharing. Therefore, this framework incorporates data sharing outputs like publications and citations, which ‘count’ in academic structures, in addition to focusing on practical research data management (RDM) skills. This session presents a framework for engaging researchers in open science and data sharing by showing them how to get credit for sharing their data. Central to this approach is the focus on practical research outputs that help to build a researcher’s CV or citation count. One example of this is data papers, which are peer-review publications that accompany open data. Data papers provide an additional peer-reviewed publication and a direct way to cite the use of shared data in the future. To prepare data for publication, researchers also need training in practical, ‘good enough’ RDM practices. These ‘good enough’ practices are relatively low effort, have a shallow learning curve, and increase data reusability. This framework will help librarians develop programing that improves RDM, encourages data sharing, and helps research see strategic benefits of data sharing Learning Objectives: · Identify ‘good enough’ data practices that can support data sharing · Understand how data papers can incentivize researchers to engage in RDM training and data sharing