What do you do to optimize your quality of results?
As is the case everywhere in natural sciences, the documentation of methods is of the utmost importance in order to ensure reproducibility by other researchers, stresses Boris Koch. For him and his colleagues who work on the high seas, there are still some very special aspects to consider: “Sampling for a trace analysis on board of a research vessel poses a particular challenge. One example is the analysis of dissolved iron in seawater, with concentrations in the lower nanomol range and many sources of contamination at the same time. In addition, the chemical work on a ship is especially complicated – for instance, it is not possible to weigh anything on a ship. We therefore have to weigh chemicals in advance of the expedition or, if possible, pipette them on board.” And because many measuring instruments are sensitive to vibrations, Koch and his team have to carry out a lot of their analyses in their laboratory ashore. This makes sample conservation a crucial point.
“All researchers build on each other’s work,” emphasizes Talia Lerner. “If we get something wrong, others will spend time going down a wrong path, too, or trying to prove us wrong. Ultimately, the quality of our results determines how impactful they will be.” This is why quality in the laboratory matters all the time, at every single step. “If we want to help human patients, we need to be sure we are getting things right. We need to imagine how our results would translate into practical therapies. Proper controlling and careful notetaking are key”, says Lerner. “I rely on my lab members to be careful observers, always looking for reasons an experiment might be contaminated.” Her team does a lot to avoid possible systemic and measurement errors: “We can help each other do experiments in a blinded fashion, set concrete inclusion/exclusion criteria prior to analysis, and scrutinize each other’s work for flaws. And we work hard to verify that all our reagents are of high quality and our handling and analysis methods are standardized.” Though, of course, it’s always possible that there is something quirky about the lab, says Lerner. “In the end, cross-validation of our results by other groups is important.”
For Graham Diering, the validation process begins with replication: “First of all, we repeat the experiment. Quality results have to be replicable. ‘Guaranteeing’ the quality of results is not always easy, but we try to isolate as many different variables as we possibly can in our experimental design. That makes the experiment easier to interpret and easier to replicate.” Keeping that in mind and trying to reduce all possible sources of error at the lab, there is one single factor you should focus on: “The biggest variable in scientific experiments is the experimenter. A very careful design of experiments and an equally careful execution of those experiments are critical for quality results. The best way to strengthen any claim in science is to have the results replicated by different labs, ideally even in different parts of the world.”
As different laboratories are equipped in different ways, it is often less important or helpful to use the newest or fanciest methods or equipment in trying to replicate each other’s results, says Diering: “I really like to use reliable and time-tested ‘oldfashioned’ methods whenever I can.”
With regard to environmental and climate research, the scope of available data and samples is of particular importance, Boris Koch explains: “Automated measurement methods are particularly helpful because they can provide a much more comprehensive picture of changes over time, for example.”
“We need reliable laboratory equipment and – as I have mentioned – that allows for automation of manual labor”, says Talia Lerner. She stresses that “everyday lab work can be repetitive and boring, but careful science depends on standardization. Some help comes from automation – increasingly, we can make computer programs and robots take over our manual labor and give us more time for creative thought. Until then, podcasts and labmates you enjoy hanging out with are lifesavers.”
On the other hand, the rapid technological progress means a growing funding problem, as Thomas Thannickal points out: “For us, a main challenge is the rapid change in scientific methods and instruments. There is very rapid change in neuroscience right now. We are not able to adapt new instrumentations with limited funding.” But this can be overcome by using shared facilities and working collaboratively, Thannickal suggests.