Abstract: |
When the term „e-Science“ became popular, it frequently was referred to as
“enhanced science” or “electronic science”. More telling is the definition
‘e-Science is about global collaboration in key areas of science and the next
generation of infrastructure that will enable it’ (Taylor, 2001). The question
arises to what extent can the social sciences profit from recent developments
in e- Science infrastructure? While computing, storage and network capacities
so far were sufficient to accommodate and access social science data bases,
new capacities and technologies support new types of research, e.g. linking
and analysing transactional or audio-visual data. Increasingly collaborative
working by researchers in distributed networks is efficiently supported and
new resources are available for e-learning. Whether these new developments
become transformative or just helpful will very much depend on whether their
full potential is recognized and creatively integrated into new research
designs by theoretically innovative scientists. Progress in e-Science was very
much linked to the vision of the Grid as “a software infrastructure that
enables flexible, secure, coordinated resource sharing among dynamic
collections of individuals, institutions and resources’ and virtually
unlimited computing capacities (Foster et al. 2000). In the Social Sciences
there has been considerable progress in using modern IT- technologies for
multilingual access to virtual distributed research databases across Europe
and beyond (e.g. NESSTAR, CESSDA – Portal), data portals for access to
statistical offices and for linking access to data, literature, project,
expert and other data bases (e.g. Digital Libraries, VASCODA/SOWIPORT).
Whether future developments will need GRID enabling of social science
databases or can be further developed using WEB 2.0 support is currently an
open question. The challenges here are seamless integration and
interoperability of data bases, a requirement that is also stipulated by
internationalisation and trans-disciplinary research. This goes along with the
need for standards and harmonisation of data and metadata. Progress powered by
e- infrastructure is, among others, dependent on regulatory frameworks and
human capital well trained in both, data science and research methods. It is
also dependent on sufficient critical mass of the institutional infrastructure
to efficiently support a dynamic research community that wants to “take the
lead without catching up”. |