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 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 research explores many facets of data reusers' trust in data generated by other researchers focusing on the trust judgment process with influential factors that determine reusers' trust. The author took an interpretive qualitative approach by using in‐depth semistructured interviews as the primary research method. 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.

This study explores the factors that influence the data reuse behaviors of scientists and identifies the generalized patterns that occur in data reuse across various disciplines. This research employed an integrated theoretical framework combining institutional theory and the theory of planned behavior. The combined theoretical framework can apply the institutional theory at the individual level and extend the theory of planned behavior by including relevant contexts. This study utilized a survey method to test the proposed research model and hypotheses. Study participants were recruited from the Community of Science's (CoS) Scholar Database, and a total of 1,528 scientists responded to the survey. A multilevel analysis method was used to analyze the 1,237 qualified responses. This research showed that scientists' data reuse intentions are influenced by both disciplinary level factors (availability of data repositories) and individual level factors (perceived usefulness, perceived concern, and the availability of internal resources). This study has practical implications for promoting data reuse practices. Three main areas that need to be improved are identified: Educating scientists, providing internal supports, and providing external resources and supports such as data repositories.

United States through a content analysis of 185 library websites, with four main areas of focus: service, information, education, and network. The results from the content analysis of these webpages reveals that libraries need to advance and engage more actively to provide services, supply information online, and develop educational services. There is also a wide variation among library data management services and programs according to their web presence. IntroductionThe importance of research data management has been emphasized over the past few decades. Tenopir et al. argue that, as science grows and moves toward more collaborative, data-intensive, and computational research, researchers are faced with various data management needs.1 Research data management is also mandated for scholarly researchers.2 Despite this, many researchers are unprepared for or lack sufficient time to handle the requirements of data management.3 Researchers also have a number of concerns about data management issues, such as data storage, integrity, and backup options. 4 Keil thus argues the researchers will need the help of a team of experts. 5As a response to researchers' need and request for help with data management, academic libraries have been actively involved in research data services: that is, "services that address the full data lifecycle, including the data management plan, digital Although libraries are aware of their potential role in and impact on research data management and curation and have started providing (or planning) data management services, not all libraries are in the same phase due to the different perceptions and needs related to data management at the institutional level, which vary with institutional capacity (and/or boundaries) and policies. Previous research reports that libraries face many challenges in data management program development (such as funding for personnel and equipment and lack of broader institutional support). Tools and recommendations have been developed by leading libraries to overcome these challenges, but other areas of support are needed, such as professional training and collaboration with other institutions to develop more skills in identifying appropriate materials.This study examined the research data (management) services in academic libraries in the United States through a content analysis of 185 library websites. Examining the current landscape of research data management services is timely and significant for both libraries that are currently planning to improve their data management services and those libraries that are already ahead in providing services. This research provides an overall understanding of where research data management programs are and where they are going; it also facilitates the understanding of current practices and data management recommendations and/or tool adoptions and reveals areas that need improvement and support. Literature ReviewMany researchers attest to the role of libraries in data management. Flore et al. argue that data management involv...

Although researchers have shown their skepticism of qualitative data reuse due to the epistemological issues, interest in qualitative data reuse has continuously grown. Discussions regarding qualitative data sharing and reuse have also been very active, especially in Europe and Australia. Compared with efforts in other countries, the discussions do not seem to be as prominent in the United States, in spite of a long history of data‐depositing and ‐curating practices and researchers’ reusing qualitative data in some disciplines. This research aims to explore qualitative researchers’ experiences reusing data in the field of social science in US, which have not been empirically addressed yet. The preliminary results from the in‐depth interviews with qualitative researchers who have used secondary data are presented, along with the barriers or hindrances to reusing qualitative data and the keys to successful data reuse.

A B S T R A C TMany disciplines within the social sciences have a dynamic culture of sharing and reusing data. Because social science data differ from data in the hard sciences, it is necessary to explicitly examine social science data reuse. This study explores the data reuse behaviors of social scientists in order to better understand both the factors that influence those social scientists' intentions to reuse data and the extent to which those factors influence actual data reuse. Using an integrated theoretical model developed from the theory of planned behavior (TPB) and the technology acceptance model (TAM), this study provides a broad explanation of the relationships among factors influencing social scientists' data reuse. A total of 292 survey responses were analyzed using structural equation modeling. Findings suggest that social scientists' data reuse intentions are directly influenced by the subjective norm of data reuse, attitudes toward data reuse, and perceived effort involved in data reuse. Attitude toward data reuse mediated social scientists' intentions to reuse data, leading to the indirect influence of the perceived usefulness and perceived concern of data reuse, as well as the indirect influence of the subjective norm of data reuse. Finally, the availability of a data repository indirectly influenced social scientists' intentions to reuse data by reducing the perceived effort involved.