Gradually, the structural problems in sciences are making their way to the surface. There have been articles in newspapers, The Economist and other magazines around the world on the subject. These are stimulated by the constant dripping of information and studies that sit awkwardly with the perceived notion of how science functions.
The high profile controversies tend to catch our attention, simply because of a sense of outrage amongst the wider community that nothing has been done to fix the problem, or that the fixes have been inadequate. Despite the outrage, it remains the case that only a very few are willing to put their head above the parapet and say something. There has been an interesting discussion of this on Athene Donald’s blog here.
Not surprisingly, the “reproducibility question” has gained quite a lot of traction( e.g., here and here). This leads to a simple question: what qualifies as a reproduction?
I argue that an important aspect of reproduction is that it is not necessarily actual reproduction, but a re-examination of observations made with better methods, which includes analytical tools. I have two examples of how scientists deal with the changing landscape of data and their interpretation in these circumstances. The first example is an instance of good practice and is common (or should be). The second seems to ignore the past and the clear message provided by the new data.
Example 1
This is from an excellent 2012 paper in Journal of Biological Chemistry that we discussed (again) in a recent lab meeting. It deals with the molecular basis for one member of the fibroblast growth factor family, FGF-1, being a universal ligand. That is, FGF-1 can bind all FGF receptor isoforms, whereas other FGFs show clear restriction in their specificity. These differences must lie in the structural basis of the recognition of the FGF ligand, the FGF receptor and the heparan suflate co-receptor. The first model put forward by Moosa Mohamadi was superseded in his 2012 paper, when he and his group obtained higher resolution structures of the complexes. This is a great step forward, as FGFs are not just important to basic biology, but they also impact on a wide range of diseases, as well as tissue homeostasis and regeneration. I highlight the following from the paper:
To quote (page 3073, top right column)
“Based on our new FGF1-FGFR2b and FGF1-FGFR1c structures, we can conclude that the promiscuity of FGF1 toward FGFR isoforms cannot be attributed to the fact that FGF1 does not rely on the alternatively spliced betaC’-betaE loop of FGFR for binding as we initially proposed (31).”
This paper provides a great example of how science progresses and is how we should all deal with the normal refinement of data and the implications of such refinements.
Example 2
This is from the continued discussions on whether the ligands on the surface of gold nanoparticles can phase separate into stripes. This has been the subject of a good many posts on Raphael Lévy’s blog (from here to here), following his publication a year ago of his paper entitled “Stripy nanoparticles revisited“, as well as commentary here and elsewhere.
Some more papers from Stellacci and collaborators have been published in 2013. The entire oeuvre has been examined in detail by others, with guest posts on Raphael Lévy’s blog (most recent here) and comments on PubPeer relating to a paper on ArXiv that takes apart the entire body of evidence for stripes.
What is quite clear, even to a non-specialist, is that the basics of experimental science had not been followed in the Stellacci papers on the organisation of ligands on nanoparticles published from 2004 to 2012. These basics include the importance of signal being greater than noise and ensuring that experimental data sample at sufficient depth to avoid interpolation; note that in no cases did instrumentation limitation require interpolation. This might happen to any of us, we are, after all “enthusiasts”.
To conclude, I refer to my quote from Seneca “Errare humanum est sed perseverare diabolicum”
This excellent advice is clearly being followed by one FGF lab. It would be good if this advice was adopted more generally across science. When we see real data and analysis (the hard stuff) that challenges our previous data and interpretations, we should all be happy to change these. This is how science (should) move forward. If everyone did this, then there would be no discussion regarding reproducibility. When we see more of the same stuff, without a clear hypothesis testing experiment, we are veering towards metaphysics.
Metaphysics is not science. I note that when data are hidden, so that analysis is restricted, we again enter the realm of metaphysics – hence, for example, the call for open access to clinical trials data.
Links with some relevance to the Seneca’s advice, reproducibility and so on:
There is an excellent post at The Curious Wavefunction’s Sci Am blog
PubPeer: here and here
Neuroskeptic’s post at Discover
Chembark’s post in response to an ACS Nano editorial on reporting misconduct.
Ferniglab Blog 
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