I have a vision of a future in which scientists who think more about others' results than producing their own are valued just as much…
“I think you've probably done enough reading now; you should start producing some results.” The PI smiled at the neophyte, who dutifully closed her laptop and reached for her lab notebook. And so the rampant pathogen Dataproducus compulsivans claimed another victim. The infection had spread to the whole group now, and yet some strongholds of resistance could still be identified in the larger institute. In a laboratory just down the corridor, there was a scientist who, at a recent conference, had unashamedly announced during his talk that he “[didn't] produce any data at all”, but rather “[did] research entirely in the published literature and existing datasets”. And yet he wasn't a bioinformatician; and he wasn't part of a dedicated data analysis service either. Rather one could call him a “professional integrator”: someone who gains higher level insights by critically analysing and integrating the discrete findings of others to create a new model that is more than the sum of its parts. And these days such a person – particularly if young – raises eyebrows. Why? Because our culture of science funding so overwhelmingly demands primary results, which are easily measured; to start a career that doesn't do service to that maxim could be regarded as suicidal.
Part of the problem is that a laboratory's output is not necessarily measured in intellectual advances, but in advances in quantities of new basic results that can be published. Primary results are crucial to science, and sometimes they do represent major intellectual advances. Furthermore, infection by Dataproducus compulsivans is necessary in a proportion of the population, otherwise there would be no results for professional integrators to work on. But which science funders specifically support endeavours based primarily on thought, rather than a constant stream of primary results? One could observe that thought is cheap compared with reagents and equipment in many fields. However, in other areas, e.g. genome sequencing, the lure of massive amounts of cheap data probably still distracts funders from the task of integrating them with other fields; beyond bioinformatic analysis, truly transdisciplinary thinking is needed here.
The benefits to science of professional integrators are enormous in my opinion – particularly if one considers science a creative profession requiring dispassionate analysis. The lack of “ownership” when considering numerous research papers in a given area is a real benefit for the professional integrator: she/he can weigh papers for and against a particular thesis with greater objectivity. A professional integrator will likely identify studies that are wrong, with minimal personal bias: as a larger picture of a biological phenomenon – a “synthesis” – forms, the inconsistencies in certain areas of the field are revealed. Moreover, an excellent synthesis almost always suggests new experiments. That observation addresses the perception that contemporary science is driven too much by data-collection and too little by hypotheses. Today, biological research produces orders of magnitude more primary results than secondary, synthetic insights, compared with the era in which Crick, Watson et al. thrived.
Hence I seriously propose professionally recognising integrators, starting with training in crossdisciplinary understanding, critical analysis and creativity, to name a few. Some scientists-in-the-making are naturals at these; others lean toward the “dryer” side of research. How can we culture them? I envision a new course at universities called “Synthetic Life Science”, as subsidiary study, and – for specialists – as a major or specialisation. Students would practise generally useful skills such as literature analysis and integration of concepts – competencies that are presently rather left to chance.
The problem starts early: as undergraduates, students learn the foundations of the subject; they then passage to learning how to do research – the emphasis being on generating results. Why the overwhelming preoccupation with generating more results? Aren't there enough being produced? Arguably there are so many results around that we need more dedicated people who explicitly don't produce new results, but rather distill out higher level insights. Naturals at this kind of science can also be spotted in the lab: supervisors should be mindful not to automatically denigrate diffuse interest or lack of single-mindedness: perhaps they are the signs of an “integrator”. And an “integrator” is every bit as much a scientist as a “producer”.