For Princeton students, it’s not premature to start thinking about summer. If anything, this post may be a little behind for some of those proactive students. Rest assured though, you are not behind if you have not started the search for summer internships (even though many students will say they’ve already applied). Opportunities are aplenty, and no, you are not behind if you didn’t start applying for research internships back in the womb.
Nassau Hall on Princeton’s campus. Photo Credit: Adriana De Cervantes.
I think that November is a critical month for the fall semester. Many students feel more pressure to do better for the rest of the semester as midterm grades come in. Students in labs start to have their lab work amp up at this point in the semester, especially newbie researchers who just joined a lab in September. That ‘just getting to know the lab’ phase is over; the sun is setting at an outrageous time; the weather is getting colder; and the professors seem to make less sense. Holidays feel like they’re right around the corner, so the end of the semester feels so close yet so far. In short, November and the first half of December is a weird limbo phase that, at its best, is a transition/preparation period and, at its worst, purgatory. With this pressure in mind, it becomes important to recognize how to balance research work and studies and mental health and social life and…and…and the list goes on.
Headshot of Ciara Sanders, Ph.D. student. Photo credit: Ciara Sanders.
For this post, I decided to ask third-year Ph.D. student Ciara Sanders in Dr. Brooks Lab here at Princeton about her experience in molecular biology graduate school. She hails from California and is currently carrying out microbiology research for her Ph.D. For students considering molecular biology research/Ph.D. as a career, these questions may help answer any concerns you have, especially since medical school seems to be the popular option for molecular biology majors.
Trustee Reading Room, Firestone Library. Photo credit: Matt Raspanti.
“So what does this data mean?” My professor asked, looking at me expectantly. What does the data mean? “What does this data tell you about the cancer cells?” If he thought rephrasing it made it any better, it didn’t. I am not quite sure what I said to save face (and frankly, I really do not want to remember), but I must have said something because my professor just nodded. “When you look at your data, I want you to create a story. It may be a mystery, but then you’d be providing a certain set of clues.”
It is very easy to get caught up in generating data, especially if the data is particularly tricky and you’re concerned about making sure it looks right, generating the right graphs, having the right axes, numbers and titles. It can be a headache. By the time the graphs are done, I would rather not look at it anymore or think too hard about the numbers. However, as lab reports and analysis questions stack up for our classes, it becomes prudent to know how to analyze these graphs. While I am not a seasoned veteran, I have a few tips that helped me approach these types of situations.