Data reuse refers to the secondary use of data—not for its original purpose but for studying new problems. Although reusing data might not yet be the norm in every discipline, the benefits of reusing shared data have been asserted by a number of researchers, and data reuse has been a major concern in many disciplines. Assessing data for its trustworthiness becomes important in data reuse with the growth in data creation because of the lack of standards for ensuring data quality and potential harm from using poor-quality data. This dissertation aims to explore many facets of data reusers’ trust in data generated by other researchers, focusing on user-defined trust attributes and the judgment process with influential factors that determine these attributes. Because trust is a complex concept that is explored in multiple disciplines, this study developed a theoretical framework from an extensive literature review in the areas of sociology, social psychology, information, and information systems. This study takes an interpretive qualitative approach by using in-depth semi-structured interviews as the primary research method. The study population comprises reusers of quantitative social science data from public health and social work—the primary disciplines with data reuse cultures. By employing purposive sampling, a total of 38 participants were recruited. The study results suggest different stages of trust development associated with the process of data reuse. Data reusers’ trust may remain the same throughout their experiences, but it can also be formed, lost, declined, and recovered during their data reuse experiences. These various stages reflect the dynamic nature of trust. The user-defined trust attributes that influenced the formation of trust also suggested various implications for data curation. The outcomes of this study will contribute to the current research on data reuse and data curation. Integrating theories and concepts of trust can provide a new theoretical lens to understand reusers’ behaviors and perceptions. Understanding how data reusers trust data will also provide insights on how to improve current data curation activities in a user-trusted way, such as methods that ensure users’ trustworthiness during data curation and develop user evaluation criteria for the trustworthiness of data.