Last week the great majority of meetings were internal meetings in Swindon, although one important external meeting I attended was that of our Appointments Board. Here a considerable degree of scrutiny is attached to ensuring that we have the right intellectual (and where possible diversity) balance on our Committees and Panels. One criterion we use in selecting members for Committees (apart from asking folk to apply) is the effectiveness with which they undertake refereeing assignments that we send to them. Another is the effectiveness of their own grant applications, as both of these metrics pertain to their likely effectiveness when serving on Committees.
In a number of cases of potential Committee and Panel members, their response to refereeing requests was remarkably low, including individuals – in our own Institutes and elsewhere – who receive considerable funding from us. I shall be writing to Heads of Institutions, Departments and Institutes, reminding them that the peer review system works best only when everyone plays their full part.
One interesting general issue revolves around the question of whether a particular action actually ‘works’ in the sense of having the effect intended. This could be a medical or pharmaceutical intervention (as with NICE) or a change in a policy or tactics in more ‘social’ questions such as how cities are policed, or teaching delivered, or renewable energy or healthy eating encouraged, or whatever. This was one of the discussion topics at a meeting of Research Council CEOs and Government Department Chief Scientific Advisers held by Sir John Beddington last week. Often the main ‘causes’ of an effect are in fact quite distant from (and independent of) a specific intervention or proximate ‘cause’, in that they would have happened anyway. The well-known book Freakonomics, that I have only just got round to reading, gives many nice examples. However, as I have long blogged, inferring causality in complex systems from observations of their variables is now a perfectly attainable goal. It is not that long ago that medicine was less than fully evidence-based, so I am optimistic that a combination of lots of data and (mainly Bayesian) computational inferencing might have major impacts in areas far wider than biology. I have already referred to Tim Harford’s book in a similar vein, though maybe not much inferencing is required in some sectors.
I enjoyed a lovely paper on infographics from David Spiegelhalter and colleagues.
The Bioprocessing Research Industry Club (BRIC) Steering Group recently met, and assessed the outcomes of some of the first round of proposals that had been funded. Two projects (the full list is here (PDF)) were highlighted, as they had scored both a top grading and also led to the creation of major Centres as a result of the BBSRC funding. One led by Mark Smales on post-transcriptional constraints on recombinant protein production pertained to the Centre for Molecular Processing, while another by Alan Dickson on metabolomic profiling of animal cells pertained to the Centre of Excellence in BioPharmaceuticals.
Professors are often the stuff of caricature, and my attention was drawn to a blog post about fictional Professors that probably did little to dispel this myth. A more useful source of inspiration, perhaps, is the Career guide for biology students and graduates produced by the Society of Biology.
- Harford T. Adapt: why success always starts with failure. London: Little, Brown; 2011
- Levitt SD, Dubner SJ. Freakonomics: a rogue economist explores the hidden side of everything. London: Allen Lane; 2005
- Spiegelhalter D, Pearson M, Short I: Visualizing uncertainty about the future. Science 2011; 333:1393-1400