Embedded systems for global e-Social Science: Moving computation rather than data

There is a wealth of digital data currently being gathered by commercial and private concerns that could supplement academic research. To unlock this data it is important to gain the trust of the companies that hold the data as well as showing them how they may benefit from this research. Part of this trust is gained through established reputation and the other through the technology used to safeguard the data. This paper discusses how different technology frameworks have been applied to safeguard the data and facilitate collaborative work between commercial concerns and academic institutions. The paper focuses on the distinctive requirements of e-Social Science: access to large-scale data on behaviour in society in environments that impose confidentiality constraints on access. These constraints arise from both privacy concerns and the commercial sensitivities of that data. In particular, the paper draws on the experiences of building an intercontinental Grid–INWA–from its first operation connecting Australia and Scotland to its subsequent extension to China across the Trans-Eurasia Information Network–the first large-scale research and education network for the Asia-Pacific region. This allowed commercial data to be analysed by experts that were geographically distributed across the globe. It also provided an entry point for a major Chinese commercial organization to approve use of a Grid solution in a new collaboration provided the centre of gravity of the data is retained within the jurisdiction of the data owner. We describe why, despite this approval, an embedded solution was eventually adopted. We find that ‘data sovereignty’ dominates any decision on whether and how to participate in e-Social Science collaborations and how this might impact on a Cloud based solution to this type of collaboration.