As somebody mostly interested in medical microbiology, I often forget that aquatic, agricultural, and ecological microbiology exist. Reading other people’s research proposals reminded me that these subdisciplines exist and were actually very interesting. In general, they made me want to read more about the topics they were on. This is similar to the course content since we are at a land grant school, meaning a lot of our professors have a research focus other than medical microbiology. This is reflected throughout the course content, an example being that in experiment one we were doing environmental sampling and analysis of pond/river samples, something that is clearly not medical microbiology. Considering I will have the rest of my career to exclusively focus on my main subdiscipline of interest, I find the aquatic, agricultural, and ecological microbiology we cover in this course, and others at OSU, to be refreshing and enriching.
Reading proposals was also beneficial because it showed me what did and did not work well. After reading them, I know it is important to have numerous sources supporting the proposed research and to have a good explanation as to why the research is important. Otherwise, it can just fall flat and seem like performing the research would simply be a waste of resources. It’s also important to have a clear focus; some proposals had several loosely related variables they were examining and it seemed like for the length that the proposal was, doing a deep dive on one of the variables would have made a better case for actually performing the experiment.
In my proposal, I did not include any real, in-depth discussion on data analysis and none of the ones I read did either. After doing the bioinformatic work we have done in class this week and last in, I think many students, myself included, would include discussion as to how the data would be analyzed using PuTTY since it seemed to be able to really improve the quality of data and ensure that you did not mess up the experiment in a significant way that led to 80% of the data being too low quality to analyze.
