It was 4 a.m. and I was standing, eyebrows furrowed, staring at the table of UniFrac distances I had made, in an empty 5th floor Student Center Athena Cluster. Yeah, not the kind of fun you imagine yourself having on Saturday nights, when you first come to college. But, I had finally gotten the mountains of data from our microbiome characterization experiment to tell me something, and I was excited. Maybe I’ll finally be able to reach a conclusion after staring at this data for the last 12 hours (give or take a few naps).
But it wasn’t telling me what I wanted to hear.
“What do you mean the UniFrac distances between birds who don’t have anything in common are smaller than between birds that share sex or location or both?!!”
I look around to make sure the room is, in fact, empty. Good, no one’s around to hear me shout at inanimate objects.
Now back to these numbers. I stand there, staring at the whiteboard, trying to think of a reason why this data would be like that. Did I analyze it wrong? Did I mix up the numbers when I was writing them? I go back to my computer. No, everything is where it should be. Could there really be an underlying factor causing birds who didn’t share sex or location to have the most similar microbiota? Are these numbers even significant? A few hours ago, I would have never imagined this thought crossing my mind but man, I wish we had more bird samples to analyze. Maybe, then we’ll get some real answers.
Looking back at my journey through the Abstract and Data Summary, I found that it perfectly exemplifies my love-hate relationship with doing science. Being a fan of lists, making lists, reading list, reordering lists, and anything to do with lists, really, I decided to list some of the things I love and hate about doing science.
Accidently deleting the data I had just analyzed from the UniFrac website without saving it was like that one time I aspirated the supernatant from my just finished miniprep. There goes the DNA I spent the last 45 minutes extracting…
Uploading the spaced ID file instead of the underscored ID file and having nothing work was (though admittedly not as dangerous) like the first time I loaded a centrifuge and didn’t balance it and had it shake violently on the bench until I stopped it.
Analyzing my data so that it gave me a conclusion and then realizing my analysis was done wrong. Working on an experiment for a week and a half only to realize that I forgot to add the essential reagent that makes everything work.
Realizing the one little thing that was making my last ten experiments not give me any results, fixing it, and seeing my experiments begin to work again.
Finally being able to reach a conclusion (based on correct analysis this time) and having that conclusion tell me something that I didn’t know before.
Finally being able to reach a conclusion and having that conclusion be so completely unexpected that I have no idea what it could mean. Doing a ton of research and figuring out that some factor that I wasn’t even considering was giving me my weird results.
A year ago, I would have never imagined myself being able to enjoy doing science. Spending ten hours a day pipetting clear liquids into other clear liquids. Working on a project for six months and having it not work. That’s how I go insane.
Now, I feel differently. I look fondly upon the vast amounts of pipetting as the tribute we need to sacrifice for good science. Each obstacle is another puzzle to be solved. Every problem has a logical solution and when that solution hits you in the face, you immediately forget the frustration you felt before, bask in the beauty of your new solution, and then go on to the next obstacle.