Last week at a social event I met
two interesting academics, working in apparently diverse fields –chemistry and
social science. It became clear in conversation that academic distinctions mean
much less than they used to, and rather than becoming more specialised, many
research disciplines are expanding to such an extent that boundaries are
blurred and perhaps even irrelevant. Both these academics, for example, use
computational methods not just to aid their research, but at its core.
Working in Cambridge exposes one
to all sorts of academic research and thought – it is ceaselessly fascinating
to meet real experts in their fields from all over the world and to hear
first-hand about the early Christian temples of the Middle East, developments
in cancer prevention or the history of concentration camps. In today’s
cross-disciplinary environment one can risk overload when considering the
multitudinous connections between research disciplines – particularly when
working, as I do, in a field which facilitates collaboration. For decades,
‘hard sciences’ such as particle physics have generated massive amounts of data,
and developed advanced statistical analysis tools to cope. It didn’t take long
for financial institutions to grasp the power of these mathematical techniques and
computational approaches (and to hoover-up PhD graduates), but it has taken
longer for other academic disciplines to catch on. In the last ten years or so,
there has been a significant change: highly data-centric scientific disciplines
boomed – computational biology being an excellent example, and specialist hubs
are no longer solely at traditional centres of excellence like Cambridge but
are truly global. Furthermore, and perhaps less expectedly, big data has been
embraced by social sciences, humanities and even the arts.
The cynic might see some of this
activity as a reflection of the poor state of research (and particularly arts) funding
causing academics to jump on the latest ‘data’ bandwagon, regardless of its
relevance to their work. This is undoubtedly true in some cases, but there is
real academic rigour in much of this work; surely it is not so surprising that
statistical analysis on a massive scale should return to its home territory of monitoring
and analysing human society – just as in the Doomsday Book. What is more, new swathes
of cross-disciplinary collaboration are opened up: the cross referencing of
data sets and scientific models from disparate sources can lead to radical and
sometimes counter-intuitive findings. This trend is taking hold across academic
study encouraged by forward-thinking organisations such as GEO in earth-observation,
mapping land and sea measurements against atmospheric and satellite data, and
Cambridge’s own CRASSH in the social
sciences, whose cross-disciplinary seminar series can be amazing to attend.
The scope and form of the
research groupings that will emerge from this collapsing of boundaries is
difficult to predict. What is already clear, however, is that these new fields
have specific requirements which go beyond traditional data handling tools.
Technologies which allow metadata to be cross-referenced across academic
boundaries, methods of uniquely identifying research findings, and secure
data-sharing technologies will all become key to the next generation of
scientist-cum-data-beachcomber. What isn’t clear yet, is who will establish the
universal standards in this space.