certain social sciences (sociology, geography and psychology) rather than others (anthropology, economics
and political science).
So, for example, in the UK, the e-Social Science initiative is thus focused on some types of social sciences
rather than others. There are also national variations. For example, in the UK, there has been a major focus
on e-Social Science, though with little emphasis on business and economics, while in the German D-Grid
initiative, e-Research focuses almost exclusively on business applications without any other social sciences.
In other areas of social science, despite an ambitious e-Research programme that includes many natural
science and humanities projects, the uptake in the social sciences has so far been conspicuous by its absence
in Germany (Schroeder, Den Besten, & Fry, 2007). In Sweden, there was initially a plan to develop e-
Infrastructure to support a wide range of social science research, but subsequently there has come to be a
narrower focus on creating a facility for sharing micro-data (Axelsson & Schroeder, 2007). In the EU, within
the European Strategy Forum on Research Infrastructures (ESFRI), the main effort has been to develop
infrastructures around existing large-scale quantitative datasets such as the European Social Science Data
Archives and the European Social Survey. These kinds of efforts create commonalities across national
programmes, such as when national social science datasets that are being federated via CESSDA (Council of
European Social Science Data Archives) and similar organizations around the world.
In the US, two flagship projects have been funded by the Office of the Cyberinfrastructure of the National
Science Foundation, one focusing on social network structure of the Web and the other collecting real-time
multimodal behavioural data. Finally, in the UK, the ‘nodes’ of the National Centre for e-Social Science
programme have concentrated on certain areas within the social sciences (such as qualitative and quantitative
sociology) and not others (such as anthropology and political science). These national research programmes
and funding initiatives will, at a minimum, shape the kinds of infrastructures and communities of researchers
that develop in e-Research.
There is also the question of what types of data or other kinds of phenomena lend themselves to
transformation into - or representation in - digital form. Moreover, the phenomena that can be captured or
represented in this way must be transferable, manipulable, and shareable via networks. This raised questions
of security, privacy and trust when sensitive quantitative (Axelsson & Schroeder, 2007) and qualitative (see,
for example, Jirotka et al., 2005) data about human subjects is made accessible for distributed collaboration.
2.2 Research Technologies
One impact of e-Research on the social sciences is to make them increasingly reliant on research
technologies. It is important to spell out what this impact means. First, it has been argued that research
technologies have been crucial to the rise of modern science. Thus Collins argues that research technologies
led to the rise of ‘high-consensus rapid-discovery science’ (1998, pp. 532-538) from about 1600 onwards in
Europe. This is an important argument inasmuch as it stands the conventional wisdom – that progress in
science leads to more powerful technologies – on its head. Instead, new technologies for research lead, in
Collins’ view, to progress in scientific knowledge. It is easy to think of examples: new and improved
telescopes, microscopes, galvanometers – or today, computers – lead to scientific discoveries because they
allow more powerful representations and manipulations of the physical world.
The reason why research technologies make for ‘high-consensus rapid-discovery science’ is that they make it
possible for scientists to use equipment to manipulate phenomena and improve the research equipment to do
this – and move on to new phenomena: ‘What was discovered was a method of discovery; confidence was
soon built up that techniques could be modified and recombined endlessly, with new discoveries guaranteed
continually along the way. And the research technologies gave a strong sense of the objectivity of the
phenomena, since they were physically demonstrable. The practical activity of perfecting each technique
consisted in modifying it until it would reliably repeat the phenomena at will’ (Collins, 1994, p. 163).
Does the same apply to social science? Collins argues that it does, but to a far lesser extent. This is because
the social sciences have been slow to adopt the use of research technologies. Here we can think, for example,
of the use of recording technologies in sociology, which has become commonplace quite recently, partly
because of the expense and quality and bulkiness of recording equipment (R. M. Lee, 2004). Similarly if we
consider the uses of computers for large population datasets: among the earliest users of commercial
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