A Pre-Meltdown Blog Post for Normalcy

From the command center of my humble abode, ie bedroom, I present to folks the first blog post. Disclaimer alert, this was written largely before stuff hit the fan so it only addresses the crisis in my writing skills and not so much the other slightly larger crisis.

I would consider myself a passable writer, at least I did fairly well in my high school writing classes. The HASS classes with writing assignments also went smoothly so I figured that 20.109 would also be equally smooth. I’m sure you can tell from my setup of this blog post by now that the Data Summary went contrary to that belief. From the very first assignment of creating a simple figure of a gel electrophoresis experiment, I began to realize that there were far more nuances and caveats to scientific communication than I had originally anticipated. Whereas in the past, normal writing espoused being thorough albeit long-winded, the hyper-specific wording needed for scientific captions turned out to be a hard skill to grasp. I felt like almost all the writing classes I had before seemed to almost actively work against what I now needed to produce in a decent data summary. I think the overarching difference I could sense in a scientific writing work with data, as opposed to other forms of written communication, was the foundation and framework. Whereas all the previous writing I did before needed to stand firm in its own basis, in that everything that needed to be communicated had to be wholly found in words, in scientific writing the data provides the foundation and framework around which the words connect, acting as the cement. 

Likewise, it becomes important to be mindful of how much you’re writing, for example, I found it hard to write less and more succinctly. It was hard for me to figure out what was extraneous and what wasn’t especially as it pertains to what should be in a caption and what could be left for the analysis section. Trying to delineate between what is strictly needed to parse the figure given and provide context for the figure’s inclusion while simultaneously not overexplaining the experiment was tough. I think I just instinctively tended towards over-explaining. In the future, I would try to make my writing less flowery which is a habit I have. Also, I found that with the bullet point format as well as the PowerPoint presentation style trying to connect between points was a bit difficult. There were definitely points where it felt hard to connect the purpose of two adjacent bullet points or figure out what information on the next slide should be introduced in the prior slide. When we transition over to paragraph and long text formats for the research article, I want to be mindful to be even more coherent and to the point, as it would be easier to get lost with those styles of writing presentation.

The last point that drove me slightly crazy was that I became hyper-aware of how “understandable” figures should be. From the label size and color to where they are located, little details can seemingly detract from the readability of a figure or even render it not understandable at all. For example, red-colored labels were seemingly a big gamble and having labels on the outside of the main body of the figure was aesthetically unpleasing but made it more readable. There was also the question of how much information could and should be presented in a figure without inundating the reader with info. I think one thing I learned was that less is more, and it's always better to present a very targeted part of your finding that was relevant to your story. Although a lot of data has been collected, you should go through with a fine-toothed comb and only present the ones that add to your story while not failing to include some unexpected results to make sure you understand or reconcile these findings. 

Given all of these statements, I would rate my Data Summary writing experience a 7.32/10. Passable, might write again :P