Liam Heaney

Meet Liam Heaney

I have been fortunate to build a career in analytical science having started out in a different discipline area (physiology and nutrition). I believe this has given me an excellent opportunity to understand, consider, and reflect on analytical science from the viewpoint of the analytical perfectionist but also, and importantly, the applied end user. This has allowed me to appreciate the appropriate trade-offs between developing assays that are perfect in every sense, and those which are just focused on being fit-for-purpose.

However, this consideration for both sides of the coin has also made me realize that many of our up-and-coming researchers are using analytical science as black box tool, with little-to-no appreciation for the science (and statistics) driving their ability to obtain reliable and meaningful data to answer their research question. This has been particularly notable through my focus on quantitative analyses. For instance, it has been concerning that many researchers are using quantitative assays without understanding the mechanisms in which the assay works nor applying scrutiny to the reliability and uncertainty that the approach provides. We know not everything can be perfect, but the saying “better the devil you know” rings true for analytical science.

For example, as I work at the intersection between physiology and analytical science, I have noticed many of our younger scientists performing quantitative experiments using commercial systems such as kit-based immunoassays. When I discuss these with the researchers, I am often told that the quantitative performance is excellent as an r squared value of 0.99 was obtained. When I challenge them by asking about the accuracy and precision of the calibration levels, this usually initiates a look which suggests I have just invented new words and terms. In addition to this, they sadly often cannot explain what mechanism is being used to capture/analyze the marker they are interested in. Now, it’s not unreasonable to assume that an exercise physiology researcher would not be well-versed in analytical procedures, but to consider that much of the field of exercise physiology relies on analytical science to infer answers to research questions, it feels imperative that some knowledge and scrutiny are applied.

Therefore, I believe that we can help analytical science rise to prominence by better educating a wider audience in the principles of analytical science, and, most importantly, what it means for them completing the science within their chosen field. Improving the knowledge of theoretical and computational aspects of analysis outside of just the analytical science community, especially for quantitative analyses, will improve the overall quality of analytical science applications across the wider remit of scientific research, making it a keystone of all good science. Furthermore, topics that rely heavily on analytical science, such as exercise physiology, should encourage their students to improve their knowledge around analytical science to help critically analyze its use within both their work and that of their peers. I believe we can achieve this in three ways:

1. Integrating teaching of theoretical elements of analytical processes which are prominent within the field of study (e.g. the theory behind immunocapture assays).

2. Embedding the reasoning and relevance of quantitative validation for students and staff working in all fields which require quantitative assessments (i.e. accuracy, precision, etc.).

3. Improving the quality of analytical reporting within research that is published in journals/fields that would usually fall outside the scope of the analytical scientist.