Today – May 7, 2009 – is the 50th Anniversary of C.P. Snow’s famous Rede Lecture published as The Two Cultures. In this, he lamented the essential lack of even a rudimentary knowledge of the natural sciences (and technology) among those trained in the arts and humanities – but the expectation by the latter that scientists should themselves be ‘cultured’ by having a rather detailed knowledge of artistic matters sensu lato. He further considered that many of the failures he then perceived in political and public life as the UK developed technologically post-war were due to exactly this kind of ignorance. His ‘test’ for scientific knowledge was whether a person might know about the Second Law of Thermodynamics, a topic also treated more lightheartedly by Flanders and Swann, but later (see e.g. the edition reprinted in 1998) he modified this (in the light of experience) to state that most non-scientists could not even describe properly the meaning of acceleration.

In many ways, this problem has got worse over the last semicentenary, with people specialising more and more as knowledge expands and – probably in consequence – becomes more balkanised. (This is certainly true of the scientific literature, where I have recently sought to provide a counterexample.)

This specialisation was further brought home at a recent workshop we held to discuss developments in mathematical biology since the publication of the Weir report (PDF) (and its update (PDF)) that looked in to how effectively numerical approaches were being deployed in our Institutes in response to our 10-year vision ‘towards predictive biology’. While there had been some excellent progress, it was clear that the ratio of ‘dry’ to ‘wet’ scientists still heavily favoured the latter. Discussion then led to the recognition that the concept of a ‘dry’ scientist covered a very heterogeneous space, that the skills of a ‘pure’ or ‘applied’ mathematician did not often overlap very much or at all with those of a computer scientist or a control engineer or a physicist or a statistician, and that many projects might need contributions from each of these. Of course the true specialisation is far worse even than this, for example with statistics and probability being seen by insiders as utterly different subjects, and with practitioners of statistical analysis either being frequentists or adherents to the Church of Bayes. (This is no different from specialisms in molecular cell biology, of course.) Within computer science, an expert on e.g. databases or real-time systems may know little about other areas (at the research level), and a subject like machine learning has specialists in sub-areas such as genetic programming, artificial neural networks, inductive logic programming and so on, none of whom typically attends each other’s conferences nor reads each other’s literature. As Maureen Lipmann’s character Beattie Bellman discovered, there are indeed a lot of ‘ologies’; it is not just two cultures that are our concern.

How we best organise such multi- and inter-disciplinary research, using the skills and knowledge of multiple specialists, is an important and open question, but there is little doubt that those who do it effectively will prosper. I hope that we will be able to continue to develop funding structures that assist this process.

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