There is a time in nearly everyone’s PhD in Biological Sciences when you spend months finding yourself identifying and fixing problems in the experimental process. It’s a dark time. Time spent optimising a protocol or waiting for spare parts to fix your set up is time spent not getting data. It may sound melodramatic, but it the length of these periods and how they are dealt with can define the success of one’s PhD, and ultimately the desire to stay in science. Like stages of grief, frustration caused by faulty or poorly optimized consumables is manifested in several forms. First, there is anger at BioTech companies for making duff products, then disbelief of scientists who successfully used them, and finally there is inwardly directed despair before sacking the experiments off.

However much we complain, optimising protocols and experiments is a part of research that scientists must face. We should always strive for better ways of doing experiments which improve the quality and reproducibility of data. Quality assurance and optimisation are tedious processes; ones that require anally retentive pedantic traits that one cultivates in science but carefully masks in social situations. In reality it is the job of PhD students and post-docs to do just these tasks. To know the limitations and extents of one’s experimental set ups is to know the limitations and potential of one’s work.


Quality assurance and optimisation are tedious processes; ones that require anally retentive pedantic traits that one cultivates in science but carefully masks in social situations


How do we pass on this information? Internally, this question largely depends on whether a lab has technical support staff and how anal retentive they are. In my view, technicians are the glue that holds research groups together and the key knowledge links as PhD students and post-docs come and go over the decades. Good technicians who help optimise protocols and experimental procedures can work wonders to boost the productivity of research groups (Conti and Liu, 2015). Communication between technicians and post-docs and PhD students are vital for keeping technical information pertinent.

Externally, reporting experimental procedures and quality assurance is mixed. Methods sections of different publications or websites of BioTech companies give pretty variable amounts of information about how to use experimental designs and products in the future. For publications, it us up to the journal editorial team to decide what methodological information they deem sufficient for others to reproduce published experiments. For BioTech, it is up to them to show that they sell reliable and quality products. If their own product descriptions are poor then that is just poor marketing.

In those dark recesses of experimental optimization during PhDs there is a controversial mantra to keep in mind: fail quickly. If experiments are failing for months on end, learn from those failures and move on. PhD students are brimming with ideas; there is no shame in switching project to use those other ideas. Failed experiments are a part of science that everyone has to get used to.

How do we develop a system for quality assurance and reporting of performance for scientific consumables and products?
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