The following details responses to comments posted on a previous blog about the ‘Age of Bioscience’, the BBSRC Strategic Plan 2010-2015. The comments facility was made available at the launch of the plan on 28 January and was left open for 2 weeks. […]
BBSRC’s new Strategic Plan for 2010-2015 launches today entitled ‘The age of bioscience’.
The plan is accompanied by a video including an introduction from me followed by an explanation of the place of bioscience research in the world and the vital role played by the BBSRC research community.
Comments will be collated, and we will respond as soon as possible after commenting has closed on 11 February 2010.
The availability of many records in digital format opens up many possibilities, not least in bibliometrics, a subject that I anticipate will be a regular feature of these blogs. For this blog we are going to look briefly at the distribution of scientific activity between individuals, as encapsulated by the question ‘if n individuals have published 1 scientific paper in a particular time period, how many individuals have published 2 papers or 10 papers or 100 papers?’
Now one might wonder whether one should expect there to be any regularities in such a (quantised) distribution, but there are. The question was posed and answered most pertinently by Alfred Lotka in 1926, and the relationship is known as Lotka’s Law. Lotka observed, from a study of papers listed in Chemical Abstracts and in Auerbach’s Geschichtstafeln der Physik, that the number of persons making n contributions is given by 1/na of those making a single contribution, with a equalling approximately 2. Thus for every 100 people who have published 1 paper, 25 have published 2 papers and 1 person has published 10 papers. In other words, the distribution of scientific productivity is best described by an inverse square law (a specific version of a negative exponential more generally referred to as a Zipf distribution). Although this is not universally true, it is a reasonable approximation and has some interesting mechanistic bases. The consequences, as recognised in Lotka’s original survey, included the fact that 60% of contributions were made by authors who contributed only one paper (and note that all joint papers were taken to have been written by the ‘senior’ author only). Nowadays this would be seen as a long-tail phenomenon, as popularised in Chris Anderson’s excellent book. […]
Bee and frog numbers are in decline, and we need to know why. Thus, understanding the dynamics of various species – the study of population biology and ecology – is an important component of BBSRC science, especially where this impacts agriculture. This kind of problem is in fact a classic subclass of problem common in systems biology, where many components may interact, we have little knowledge of the parameters or even the network topology of the system, and where the best we can usually do is to measure system variables. Since it is the parameters of the system that control and determine the time evolution of the (dependent) variables, how can we make progress? The answer is by using inferencing methods (including the methods of data mining and machine learning) that permit one to infer the structure and parameters simply from the measurement of such variables. This is then a data-driven or hypothesis-generating strategy (Kell & Oliver, 2004). The results of the hypothesis-generation step are then hypotheses that can be tested by making the most important inferred parameters independent variables in a subsequent experiment. […]