What is the matter with e-research?
E-Science is generally defined as the combination of three different developments: the sharing of computational resources, distributed access to massive datasets, and the use of digital platforms for collaboration and communication (Hey and Trefethen 2002; Nentwich 2003). The precise definition varies between the UK, the US and the Netherlands, which inter alia illustrates the local nature of this type of global developments. Nevertheless, these three elements are generally recurring in e-science projects and programmes. In the Dutch initiatives of e-science, the e stands not in the first place for "electronic" but for "enhancement".
The core idea of the e-science movement (most of it still promise rather than practice) is that knowledge production will be enhanced by the combination of pooled human expertise, data and sources, and computational and visualisation tools. e-Science has become a buzzword for funding large-scale facilities, especially in those research fields in which research is driven by huge high-technology research groups. The confrontation of this ideal of enhanced knowledge creation with scholars in the humanities and social sciences has mostly yet to begin.
So far, the meeting is partly metaphorical, with the exception of facilities for computationally or experimentally oriented social sciences and humanities (eg. in economics and linguistics).
In this proposal we use the notion of e-research rather than e-science to indicate that it is not a matter of importing e-science ways of working into the social sciences and humanities. The humanities and social sciences will develop their own specific ways of integrating the use of networked information and communication technologies (Bijker and Peperkamp 2002; Bijker, Schurer et al. 2003; Boonstra, Breure et al. 2004; Kircz 2004). This does not have to mean that the difference with natural sciences will become less important. Hence, the generic term e-research is preferable over the notion of e-science. The humanities and social sciences are no backwater with respect to e-research.
For example, archaeology has developed e-science ways of working in its combination of natural science and humanities expertise, its use of sophisticated Geographical Information Systems (GIS) software packages, and its use of expert systems in parts of its research and training. In the field of linguistics, both corpus-based and experimental approaches have led to a transformation of the study of language and the creation of sophisticated research infrastructures. The cognitive sciences are an example of the confluence of natural sciences, social sciences and humanities which drives them into a new experimental direction that relies heavily on computer-based imaging techniques.
Economists are interested in modelling and simulation and develop fields like neuro-economics. In sociology, computational research seems to catch on again in the form of new research programmes aimed at, among others, micro-simulations of households and agent-based modelling (Ahrweiler and Gilbert 1998). Moreover, computerised social network analysis is a well-established tradition in sociology (Wasserman and Faust 1994). Even in the more traditional fields, many researchers in the humanities and social sciences are adept users of the most advanced tools they can get, as long as the learning curve is not perceived as too steep.