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When developing university‐based research data curation services, libraries face critical decisions around organization and sustainability that can affect dataset producers' satisfaction with these services. We present a study, involving interviews (n = 43) and ethnographic observation, of how two libraries partnered with the Sloan Digital Sky Survey (SDSS) to curate a significant astronomy dataset. Each library took different decisions: one library assigned activities to a unit specializing in digital curation, while the other distributed activities across its existing units. Neither approach proved a silver bullet. While library staff members felt the outcomes largely met their expectations, SDSS leaders expressed mixed opinions. We identify three factors that contributed to these differences in perspective: differing strategic motivations for undertaking this Data Transfer Process, SDSS leaders' misperceptions about libraries, and organizational mismatches. These factors contributed to four differences in perspective between SDSS leaders and library staff: provenance as technical information or as information about social context, dataset as a live research object or as a static object to be preserved, systems and services tailored to the dataset or easily adaptable to other datasets, and obstacles as setbacks or as opportunities. Only those differences that emerged when SDSS collaboration members and library staff communicated frequently were resolved. [ABSTRACT FROM AUTHOR]

University libraries are partnering with disciplinary data producers to provide long‐term digital curation of research data sets. Managing data set producer expectations and guiding future development of library services requires understanding the decisions libraries make about curatorial activities, why they make these decisions, and the effects on future data reuse. We present a study, comprising interviews (n = 43) and ethnographic observation, of two university libraries who partnered with the Sloan Digital Sky Survey (SDSS) collaboration to curate a significant astronomy data set. The two libraries made different choices of the materials to curate and associated services, which resulted in different reuse possibilities. Each of the libraries offered partial solutions to the SDSS leaders' objectives. The libraries' approaches to curation diverged due to contextual factors, notably the extant infrastructure at their disposal (including technical infrastructure, staff expertise, values and internal culture, and organizational structure). The Data Transfer Process case offers lessons in understanding how libraries choose curation paths and how these choices influence possibilities for data reuse. Outcomes may not match data producers' initial expectations but may create opportunities for reusing data in unexpected and beneficial ways. [ABSTRACT FROM AUTHOR]

Data and software are critical components of scientific work. Increased data and software sharing promises many benefits for science. Many stakeholders are building infrastructure and implementing policies to promote sharing. However, sharing remains rare in practice. Attention must be paid to researchers' ethical perspectives on sharing to fully realize the promise of sharing and promote greater circulation of data and software and better uptake of infrastructure for data and software curation. This research presents an agenda for researching these perspectives, including characterizing and accounting for researchers' perspectives; examining how these perspectives shape decisions related to data and software sharing; and understanding how and why differences in perspectives arise and are contested, negotiated, and resolved in multidisciplinary scientific collaboration. This agenda will enable stakeholders to identify and resolve differences in ethical perspectives, and develop policies, infrastructures, and education that support existing ethical perspectives, and cultivate better ethical practices. [ABSTRACT FROM AUTHOR]

A 5‐year project to study scientific data uses in geography, starting in 1999, evolved into 20 years of research on data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the "team science" approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards's model of "making global data"—collecting signals via consistent methods, technologies, and policies—to "make data global"—comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations of students and staff, transitions between grants, transfer of legacy data between software tools, research methods, and the role of professional data managers in the social sciences. [ABSTRACT FROM AUTHOR]