I think the biggest issue is us, people. We are capable of building sophisticated tools and infrastructures, but because data curation is a collective enterprise and a long-term dynamic system, we constantly need to negotiate our goals, needs, values, and attitudes. There are many social, political, and historical reasons for data curation to fail or succeed. We need to be aware of that.

My view on the biggest issue facing data curation is aligned with insights from the history and sociology of science. Many publications have expressed this view, but Edwards, Jackson, Bowker, and Knobel’s report on challenges regarding infrastructures is probably most relevant to the issue of data stewardship. ((Paul N. Edwards, Steven J Jackson, Geoffrey C Bowker, and Cory P Knobel, “Report of a Workshop on ‘History &amp; Theory of Infrastructure : Lessons for New Scientific Cyberinfrastructures’ January 2007,” <i>Design</i> 20 (January 2007): 1-50, <a href="http://hdl.handle.net/2027.42/49353">http://hdl.handle.net/2027.42/49353</a>.)) It is also more useful and interesting to consider not just data, but a broader range of products, or what Bechhover et al. call “research objects,” such as publications, data, code and algorithms, workflows, and so on. ((Sean Bechhofer, David De Roure, Matthew Gamble, Carole Goble, and Iain Buchan, “Research Objects: Towards Exchange and Reuse of Digital Knowledge,” <i>Nature Proceedings</i> (2010), <a href="http://eprints.soton.ac.uk/268555/">http://eprints.soton.ac.uk/268555/</a>.))

As a complex system, research-object curation involves tensions, such as those between immediate and long-term goals of development, between global and local implementations, and between planned versus emergent changes in the system. To negotiate these, the system needs actors in different roles, including technical experts, managers, and “champions” who stimulate interest and promote adoption. During systemic growth, users and user communities can become critical to success or failure.

Data curation is embedded in social and institutional practices and norms, none of which change more slowly than communication and data-processing technologies. These discontinuities may be part of the reason why culture of sharing and making data public has not been widely adopted, and they may also become obstacles in creating such a culture in the future. As people, practices, and norms play a formative role in data curation, it is important to remember that there are always “winners” and “losers.” Some people initiate and adopt innovation and change, while others resist it and find it hard to fit in. To quote Edwards et al., “It is also possible that a tech-centered approach to the challenge of data sharing inclines us toward failure from the beginning, because it leaves untouched underlying questions of incentives, organization, and culture that have in fact <i>always </i>structured the nature and viability of distributed scientific work." ((Edwards et al, “Report of a Workshop,” 32.))

Paths to solutions that Edwards et al. hint at include historical and sociological awareness; the consolidation of local systems and tools; the development of bridges and gateways, as well as boundary workers who can act as “bridge-builders” and “translators”; and more flexibility and collaboration. The question that data curators need to ask themselves is, “Are we playing a role in any of these?”