Introduction

We live in an information age characterized by a deluge of digital data (Hey and Trefethen, 2003a; Hey, Tansley and Tolle, 2009). The potential benefits to researchers are enormous, offering opportunities to mount multidisciplinary investigations into humankind's major social and scientific challenges on a hitherto unrealizable scale by marshalling artificially produced and naturally occurring data of multiple kinds from multiple sources. However, this newfound wealth of research data will be without value unless it can be managed in ways that will ensure that it is discoverable, accessible and (re)usable.

Over the past ten years, the efforts of national e-research programmes to innovate research methods, tools and infrastructure – as in the UK e-Science Programme (Hey and Trefethen, 2003b), the Australian e-Research Programme (Treloar, 2007) and the NSF Cyberinfrastructure Program (National Science Foundation, 2003, 2006) – have raised awareness among stakeholders that research data is a vital resource whose value needs to be preserved for future research by the data generators and by others. Achieving this requires that the data be systematically organized, securely stored, fully described, easily locatable, accessible on appropriate authority, shareable, archived and curated. Fulfilling all of these research data management tasks is a complex sociotechnical challenge that all stakeholders, whether they are research funders, higher education institutions (HEIs), publishers, researchers or regulators, are currently ill prepared to meet. There are, as yet, no widely agreed, mature solutions. Moreover, given the combination of the data deluge and a world recession, the scale of the tasks is increasing while the financial and therefore human resources to undertake the tasks are shrinking.

In this chapter we review the drivers for research data management services, consider the challenges their provision poses for HEIs and explore how these might be met, including possible pathways to sustainability. To do this, we begin by drawing on the findings from several UK studies of researchers’ everyday data management practices, which help us to understand the reasons why these practices exist and why they may not be easily changed.