In this Neuronet spotlight on early-career researcher interview, we speak with María Arroyo-Araujo, who currently carries out her PhD as part of a subproject in the EQIPD (European Quality in Preclinical Data) consortium, a combined effort of universities and pharmaceutical companies, with the purpose of increasing the quality of preclinical data. The objectives are speeding up the discovery of new medications, in particular for neurodegenerative disease like Alzheimer’s and a safer transition from preclinical to clinical research at the University of Groningen. We asked María about her motivations, research, as well as about challenges & opportunities in her field.
What made you decide to pursue a career in research?
I think the fact that I am a curious person had a big influence. I like understanding what is behind of the things I find interesting, that being an activity or a topic. Thus, doing research felt good right away, it fits well with my personality and importantly, it also fulfils my need for having a purpose in what I do.
What do you enjoy most about your work?
I really enjoy how dynamic it can be. For my PhD I had different types of experiments, some in the animal facility doing behavioural testing and some others in the ‘wet-lab’ processing samples. Both of these ended up on my desk with me drafting papers. In this sense, there was a little bit of everything: planning experiments, ordering compounds, running up and down the building fixing equipment, discussing data with colleagues, preparing posters and presentations, and sitting quietly in the office trying to process it all. It was tough at times, but never boring.
You recently published a scientific article as part of research, funded through the Innovative Medicines Initiative’s EQIPD project.
What were your main conclusions? / What is a quality system and how can it contribute to the improvement of research?
The quality system (QS) developed by EQIPD is a tool to support the planning, execution and data management of scientific research. The step-by-step implementation basically guides you through the process, it is very clear and practical. It also allows for self-reflection about possible unintended gaps in the process of doing science. In this way, when a research group adopts EQIPD’s QS it promotes good research practices that contribute to boost data quality and accuracy. The bigger the research unit that implements the QS (e.g., research department, research institute), the bigger the impact will be.
From our lab’s experience, adopting the EQIPD’s we learned that although implementing a QS is known to be burdensome a time-costly, this is not the case of EQIPD’s QS, certainly when the research lab is already operating at a high level. Moreover, the QS’ adoption served as a self-reflection exercise to realise there were procedures, like the onboarding of new staff, that could be easily improved by following the QS’ guidelines. In this sense, we got more out of adopting the system than the time that we had to invest on the implementation.
What impact do you hope your work will have in the long run?
I honestly hope that fellow researchers, specially those working in academia, will adopt EQIPD’s QS. Research data can certainly benefit from good research practices, the best time to learn these is when learning how to do research, meaning as students. If students nowadays would have a QS such as EQIPD’s as a standard practice, it would boost research data in the coming years making it highly reliable and fit for the intended use.
What do you see as the key challenges & opportunities for your field?
I find consensus a big challenge in the sense that the development of new tools and methodologies to perform and analyse data goes faster than the validation of those methods, even more as they become quite specialized too fast. Therefore, it becomes really difficult to compare published papers although they use the same techniques. However, this opens some room for collaborations, which I think it’s a great opportunity to create something bigger by joining forces. It has become more difficult for one researcher to master several highly specialised techniques/methodologies thus, collaborating allows researchers to not loose the focus on their own court while also opening the doors to do something beyond the limits of their court.
Arroyo-Araujo M. & Martien J.H. Kas. The perks of a quality system in academia. Neuroscience Applied, Volume 1, 100001 (2022). https://doi.org/10.1016/j.nsa.2022.100001
Arroyo-Araujo, M., Graf, R., Maco, M. et al. Reproducibility via coordinated standardization: a multi-center study in a Shank2 genetic rat model for Autism Spectrum Disorders. Sci Rep 9, 11602 (2019). https://doi.org/10.1038/s41598-019-47981-0