Analyzing Data

Year in School: Junior
Major: Physics
Hometown: Somonauk, IL

Research is going well. There have only been a few hiccups thankfully – either Gaea is down being worked on, or there is a bug in the code – but otherwise I am mostly on track with my timeline. I would have liked to start collecting new x-ray data by now, but we are still looking at data from last year. Regardless, analyzing the 2014 data works just as well for our purposes of finding any correlations. In fact, we could make a conclusion after analyzing all of the previous data without collecting new data, although I would still like to collect new data.

stringer_tyler_analyzing-02

Some things I have learned in the past few months have been computer skills. Since we are analyzing data, we have been working on a Linux computer, as well as utilizing Matlab to process the numbers coming from Gaea. So, I am becoming more comfortable with Linux’s command prompt, as well as Matlab’s syntax, although I am not fluent in these languages. I am also learning more about how the CCD camera collects the photon data, and the equations used to look for correlations in the data.

This year I feel like I can relax on the parts of the Research Rookies program that require me to write a research proposal and put together a poster, yet I can focus on learning and analyzing data. Since I already know what to expect from the format of things such as the proposal and poster, I feel more comfortable with those things. That takes some stress off of the whole experience, and I can focus on the research itself.

Likewise, from my experience I can offer advice to first year students in the program. I would like to list several bits of advice: follow formatting instructions, find a system that keeps you well organized (such as an app or notebook), and don’t procrastinate. However, the single most important piece of advice I could give would be to ask questions. While you’re performing your experiment, ask yourself why am I doing it this way? What would happen if…? What does this mean? How do I know I’m looking at accurate data? These are just a few questions that may come up. Be curious, and voice these questions aloud to your mentor. Your mentor is an expert in his or her field of study, so your mentor will have insight into problems that will trump your assumptions. That’s not to say that you won’t come up with an improved way of running the experiment either, yet the more brains working on a project the better.

Tyler Stringer

Written by Tyler Stringer

Along with my studies at NIU, I am also involved in the Honors Program, the PROMISE Program, am a TLC Peer Leader, a IMSA Mentor, and am Vice President of Society of Physics Students. I also enjoy nature while camping or canoeing, playing video games, helping with children's church, and having deep, philosophical talks.

Website: http://www.niu.edu/researchrookies/participants/1415_profiles/tyler_stringer.shtml