PhD Reflections
PhD Reflections
After reading this thesis, this final section may seem unconventional and perhaps unnecessary; At the beginning of it, my acknowledgements thank all the wonderful people (and creatures) that have supported me throughout this long, long period in my life. It seems appropriate, if a little indulgent, that a glimpse into the rear-view mirror of my PhD somewhat bookends my thesis. Something that I think often goes unacknowledged is everything that the PhD teaches you. For many students starting out in their PhDs, it might seem impossible and unattainable to produce a final thesis. I certainly had my moments. Others, at the end of their journey, might forget to look back and take stock of everything that they have learned, as if that knowledge had always been there. I think that does the PhD a disservice; We are here to learn, grow, and develop as researchers. Often through trial and error, and more error. I would like to take this short section to acknowledge all the things I didn’t know or wasn’t as skilled in, before I started, but I have now learned how to do. After all, a PhD is an incredible opportunity to learn and develop the skills needed to succeed in the future.
Practical Skills
Before starting this PhD, I had no appreciable experience in in vivo animal maintenance, handling, or the general research skills required to work with rodents, including administering treatment by gavage or behavioural testing. The months spent in the animal unit helped me learn all those skills. The same goes for all the in vitro experiments carried out in this PhD: never before had I done tissue sectioning, a radioimmunoassay, radionucleotide-labelled in situ hybridisation assays, or 16S rRNA sequencing. These are all skills that I can now say that I have learned and learned from. I have learned from the trials and the errors. I have learned from the successes and from attempts that never even made it to a finished experiment. Finally, I have learned from writing up the thesis and from my viva. Without mistakes, nothing can be learned.
Computational and Statistical Skills
What I learned in this section came as a big, beautiful surprise to me. Learning how to use R had been a goal of mine when starting the PhD; I knew that undergraduates were given the opportunity to learn this and it felt like an incredibly powerful tool that I wanted to become at least familiar with. I never imagined how far that goal would take me. Starting with a simple 10-line R script made available to me by Dr Crispin Jordan, I took each piece apart to learn what it did. I then added more lines, and took even more away. I slowly learned how the language worked and became familiar with how to use it effectively. For someone with a computational background, learning R may sound terribly dull, as it is in fact a very intuitive language, but for me, this was the first time I had undertaken to learn any type of coding language. The most surprising thing about this however, was that I fell in love. I loved the logic puzzles new problems would present. I loved that I could just try something and see what happened. I could get lost in solving these and took pride in succeeding to find a solution. I learned how to carry out complex analysis, picking up new skills in statistical analysis and theory, as well as how to create the figures that I wanted.
It was a good thing that I became so delighted by coding, as I soon discovered that the initial pre-processing steps required for the microbiome analysis required me to code in the command-line interface using a different language, Bash. If you are computer scientist, I will remind you again at this point that I am not. For anyone wondering: this is the equivalent of saying I learned how to eat with a knife and fork, and feeling quite pleased about it. But in this analogy, I didn’t even know cutlery existed before trying to use it. So I set to learning what I needed to know and managed to navigate my way through the bioinformatics portion of the microbiome analysis. Even then, when I was able to return this prepared data to my beloved R, I had to learn how to apply specific microbiome-analysis commands and processes. So I did. I tried and I met hurdles and impasses. I made mistakes. I sought help on forums and from colleagues, until I succeeded.
When I moved on to the image analysis step of my thesis, which was not without its own set of problems, I realised that many of these steps could be automated and thereby made more efficient and reliable. So I set about learning the complex functionalities of ImageJ and QuPath, eventually learning how to write macros that knitted these steps together in a streamlined process in order to create a new analysis pipeline that can be used by others. The section of Chapter 4 in this thesis about the development of the pipeline is an ode to the joy and pride I felt developing it. From scratch. By myself. I think if I told pre-PhD Danny that I would end up creating something like this, she wouldn’t believe it.
Finally, and this may not be obvious to the reader of this formatted pdf document, I wrote my thesis in LaTeX. I had no prior knowledge of this particular language when I sat down to start writing, but it seemed like a good idea to learn. So I did. More trial and more error ensued, and I think you will agree that the outcome looks good. It was certainly preferable to negotiating with a certain common text processor about image placement.
Research Skills & Professional Skills
Everything I mentioned so far has been a very practical, hands-on skill. However, I think it is also worth mentioning the more inexplicit, but important research skills I learned. The first one being experimental design, both from reading papers and learning from the experiments I carried out, but in all honesty, a portion of this skill has also come from the 20:20 hindsight of my own work, wishing I had done things slightly differently. Now I know better. I think this a common lament amongst PhD students. Additional skills that I have learned and will carry with me, and hopefully continue to improve on are data management, scientific writing, presentation skills, and critical scientific evaluation. I know so much more about these than when I started, but I don’t think learning ever ends for skills like these. Finally, and I think I have made this point clearly enough throughout so I won’t belabour it any more: independent problem solving.
Professional Skills
The PhD also taught me incredibly valuable skills in collaboration, asking colleagues for advice, adapting to challenges and meeting those challenges with a resiliency I did not know I possessed. That is not to say I have not had my set-backs or my moments of despair. But I learned to get back up and try again tomorrow. And finally, I learned what my personal strengths and weaknesses are. I learned what I am passionate about and how that passion can fuel my creativity to become a better scientist, and remain as ever, a life-long learner.
In closing, it has been a privilege to learn how to be a scientist.