Understanding Researchers’ Data-Centric Tasks: A Classification of Goals, Gaps, and Resources

In an era where data reuse is increasingly central to research efficacy, this study delves into the granularity of data-centric work tasks and addresses task goals, the challenges researchers encounter (i.e., the gaps), and the essential resources for these tasks. Utilizing a systematic literature review, we articulate a classification framework that identifies four distinct goal families and twelve goal categories. Within the goal families of “Research” and “Data”, goals are further characterized as either exploratory, confirmatory, or balanced. Our results demonstrate that the nature of goals has implications for how researchers anticipate gaps and resources. Specifically, those with more defined (confirmatory and balanced) goals predict the hurdles they will face and are proactive in identifying resources, whereas those with exploratory goals show less foresight in challenges but seek a wider range of potential resources. This study enhances our understanding of the complex interplay among goals, gaps, and resources in data-centric research tasks, offering avenues for more targeted research support services.