3 Lessons Ligands Taught Me

I have never considered myself a great communicator or technical writer (spoilers: I still don’t). However, something about the journey of collecting my own data and interpreting it in a meaningful way helped me pick up a couple of tricks that I hope to use in my future scientific and scholarly writing.

As a matter of fact, ligands may not have given me significant data, but they did give me significant life lessons:

1.     Resources are your best friends.

Mendeley, Excel, PowerPoint, PubMed, ChemDraw… Believe it or not, that isn’t a list of my favorite Koch Café sandwiches. That is a list of just some of the resources that helped make my research findings presentable and cohesive.

Surprisingly, it turns out that some of the hardest aspects of the Data Summary for me were making line graphs look clean, being consistent with colors across figures, lining up panels, and writing proper citations. These tasks weren’t hard in the sense that they required a lot of critical thinking and looking, like the hefty data interpretation did, but they were hard because they required a lot of time and considerable maneuvering through tools I had been previously unfamiliar with.

Thus, one of the most productive preparative steps I would advise all researchers to do, including future me, is getting comfortable with citation- and figure-generating accessories, so that you spend your time more efficiently and make your paper more digestible to readers.

Not knowing how to remove minor axes from your bar graph because you are not familiar with Excel formatting features just adds frustration and subtracts productivity from your work schedule.

2.     Data has a voice of its own.

When diving into data analysis, don’t go in with any assumptions. Leave all hypotheses at the door. Let the data speak to you.

What I mean by this is although raw data can sometimes be overwhelming and it is commonplace to start data analysis with a preconceived notion of what you want to see out of it, I found it more productive to graph and ask questions later.

A general process that worked for me was first making an admittedly cluttered graph with all my data, treating the graph as an image on a paper I was trying to critique, and asking myself what conclusions or trends was the data making glaringly obvious to me.

Even when your data isn’t statistically significant, it can serve as significant insight into something you might want to focus on in future experiments or into a pattern or trend you can try to understand.

3.     What would [insert awesome 20.109 professor name here] do?

Something I struggle with when I take on any new endeavor is feeling unqualified and thus shrinking from the task at hand. I get intimidated and frustrated and don’t necessarily produce my best work.

I definitely got flustered in the beginning of this assignment, feeling like I didn’t know where to start or how to crank out quality material.  When you’ve never written a research paper or presented scientific data before, it can be hard to grasp the big picture of what you want to accomplish in assignments like the Data Summary.

Something I used to do in high school a lot when test taking was putting myself in the shoes of the test creators or graders. What did they want me to get out of this exam? What were they looking for in the answers? In this assignment, whenever I got stuck or was confused, it helped me to think about what my 20.109 instructors, and in general a scientifically literate audience, would want to read.

With that being said, congratulations to everyone for making it through Mod1 and ahead we charge towards Mod2!