Reviewing papers for journals can be a chore sometimes, especially if the submission is from a non-English speaker and you spend all of your time trying to decipher what the authors are trying to say. However, it is an aspect of my job that I enjoy as it makes me think about some of the deeper, philosophical issues that arise in science.
Today, I just finished a second review of a paper that I had rejected previously but which the other reviewers had accepted with major revisions. My major concern was that I fundamentally disagreed with the authors approach to interpreting the data. In chemical engineering, it is very common to present some experimental work and develop a mathematical model to ‘explain’ the data. My biologist colleagues sometimes refer to this as using mathematics to tell you what you already know! Maybe they’re right. Inevitably it is concluded that the model fits the data ‘well’!
The problem (for me) with this paper was that the model did fit the data reasonably well but I felt it was fundamentally misguided. Without going into too much detail, the authors were using suspension theory on what I consider to be solutions. While their model seemed to work, I think I can come up with a much simpler and elegant explanation for the data. Yet, without doing further experiments (of a different kind) we do not know, for sure, who is right. In the end, I decided I had no real ‘right’ to reject the paper and let it through – the second draft was much better all round anyway and it might serve as a focal point for future discussion.
This got me thinking about the huge number of publications out in Journal World. The vast majority of them seem to be ‘bitty’ in nature, providing plausible ‘explanations’ for limited amounts of data. A result of the ‘publish or perish’ culture I suppose. In the case of the paper I reviewed, the authors really needed to go back to the lab and devise a whole new set of experiments that would provide additional, independent, evidence for their model. The fact that their mathematical model seemed to fit the data did not mean that it represented ‘truth’.
So what about the really important, fundamental sciences such as particle physics? Does our ability to explain the data in the LHC, for example, mean that we really understand nature? What the hell is an electron anyway?