Investments in cyberinfrastructure and e-Science initiatives are motivated by the desire to accelerate scientific discovery. Always viewed as a foundation of science, data sharing is appropriately seen as critical to the success of such initiatives, but new technologies supporting increasingly data-intensive and collaborative science raise significant challenges and opportunities.

Much of the recent research on digital data repositories has focused on assessing either the trustworthiness of the repository or quantifying the frequency of data reuse. Satisfaction with the data reuse experience, however, has not been widely studied. Drawing from the information systems and information science literature, we developed a model to examine the relationship between data quality and data reusers' satisfaction. Based on a survey of 1,480 journal article authors who cited Inter-University Consortium for Political and Social Research (ICPSR) data in published papers from 2008-2012, we found several data quality attributescompleteness, accessibility, ease of operation, and credibility-had significant positive associations with data reusers' satisfaction. There was also a significant positive relationship between documentation quality and data reusers' satisfaction.

There is almost universal agreement that scientific data should be shared for use beyond the purposes for which they were initially collected. Access to data enables system-level science, expands the instruments and products of research to new communities, and advances solutions to complex human problems. While demands for data are not new, the vision of open access to data is increasingly ambitious. The aim is to make data accessible and usable to anyone, anytime, anywhere, and for any purpose. Until recently, scholarly investigations related to data sharing and reuse were sparse. They have become more common as technology and instrumentation have advanced, policies that mandate sharing have been implemented, and research has become more interdisciplinary. Each of these factors has contributed to what is commonly referred to as the "data deluge". Most discussions about increases in the scale of sharing and reuse have focused on growing amounts of data. There are other issues related to open access to data that also concern scale which have not been as widely discussed: broader participation in data sharing and reuse, increases in the number and types of intermediaries, and more digital data products. The purpose of this paper is to develop a research agenda for scientific data sharing and reuse that considers these three areas.

Purpose Taking the researchers’ perspective, the purpose of this paper is to examine the types of context information needed to preserve data’s meaning in ways that support data reuse. Design/methodology/approach This paper is based on a qualitative study of 105 researchers from three disciplinary communities: quantitative social science, archaeology and zoology. The study focused on researchers’ most recent data reuse experience, particularly what they needed when deciding whether to reuse data. Findings Findings show that researchers mentioned 12 types of context information across three broad categories: data production information (data collection, specimen and artifact, data producer, data analysis, missing data, and research objectives); repository information (provenance, reputation and history, curation and digitization); and data reuse information (prior reuse, advice on reuse and terms of use). Originality/value This paper extends digital curation conversations to include the preservation of context as well as content to facilitate data reuse. When compared to prior research, findings show that there is some generalizability with respect to the types of context needed across different disciplines and data sharing and reuse environments. It also introduces several new context types. Relying on the perspective of researchers offers a more nuanced view that shows the importance of the different context types for each discipline and the ways disciplinary members thought about them. Both data producers and curators can benefit from knowing what to capture and manage during data collection and deposit into a repository.

We know little about the data reuse practices of novice data users. Yet large scale data reuse over the long term depends in part on uptake from early career researchers. This paper examines 22 novice social science researchers and how they make sense of social science data. Novices are particularly interested in understanding how data: 1) are transformed from qualitative to quantitative data, 2) capture concepts not well-established in the literature, and 3) can be matched and merged across multiple datasets. We discuss how novice data users make sense of data in these three circumstances. We find that novices seek to understand the data producer's rationale for methodological procedures and measurement choices, which is broadly similar to researchers in other scientific communities. However we also find that they not only reflect on whether they can trust the data producers' decisions, but also seek guidance from members of their disciplinary community. Specifically, novice social science researchers are heavily influenced by more experienced social science researchers when it comes to discovering, evaluating, and justifying their reuse of other's data. Keywords Communities of practice, Data repositories, Data reuse LITERATURE REVIEWMuch of the data reuse literature has drawn from the concept of communities of practice to explain reuse behavior within and across disciplinary communities. The research suggests that data reuse is easier when data circulate within as opposed to outside of a disciplinary community, because members of a disciplinary community This is the space reserved for copyright notices.