© 2016 JUSTIN SU. ALL RIGHTS RESERVED.

A Home Away from Home

When a power outage occurs in Isla Vista and the library closes at 2 AM, there is only one option left for me – the lab. That actually happened. I did not sleep there, of course, though I desperately needed power, warmth, and speakers that can blast me awake. Interestingly enough, I was not alone. One of the graduate students working in my lab was also there. It simply felt like home, where that oh dark hundred became one of the most productive I have been in all summer.

© 2016 JUSTIN SU. ALL RIGHTS RESERVED.

This world also contains a direct view to the ocean and the freshman dorms!

Working in Dr. Zach Ma’s lab opened a new world for me, one that I would not have expected to be in a year ago. A world that contains one of the purest, sterile water on campus, countless bags of pipette tips, and some laboratory equipment that cost more than most luxury cars. Though a chemistry major, I have always found the field of biology intriguing, and joining a cell biology lab before even taking general biology is quite a challenge and a reward. Learning biochemical techniques and operating laboratory equipment these past months were all fascinating fun, yet the reiterated realization of the difference between doing and thinking was a defining moment of this summer. Acknowledging the fact of doing from memorizing was extremely difficult until problems began to arise sporadically. Every experiment has their own situations. Doing based on understanding the scientific concepts and their rationales adapts to those situations, and would have optimized the ideal circumstances for each experiment I ran. Boy, would that help me in organic chemistry.

© 2016 JUSTIN SU. ALL RIGHTS RESERVED.

A year ago, I did not know that it was possible for a freshman to get involved in research, especially cutting-edge research. Though in research, one thing to hone in on is resiliency. No matter if your hypothesis was horrendously wrong, you go back to the drawing board and patiently and logically crank it out. As a part of a research group, I have a family that is willing to come to my aid and motivate me. I can even go to other families around campus and find ways to collaborate, whether they be ideas or equipment. Simply put, we are a family, and this is my home, where I will be making life-defining memories (and scientific discoveries, hopefully) for the years to come.

A Physicist’s Chemistry Research

I’m a second year physics major, and I work in Songi Han’s physical chemistry lab. This summer I have been very grateful to have the opportunity to be financed to work through the Gorman Scholar Program. I started working in the Han lab Spring quarter 2016. We do research on nuclear magnetic resonance (NMR); it’s the natural phenomena that is utilized in medicine for magnetic resonance imaging (MRI) and in chemistry for spectroscopy, which looks into the properties of materials, structures of molecules, and dynamics of molecular systems. Specifically, my responsibilities have been to help design and build NMR probes. This entails a lot of soldering parts, fashioning coils, crafting other probe components, and operating 3D design software. I’ve been lucky to be for the most part in charge of myself, being able to make a lot of my own decisions and manage my own time, as well as to have the opportunity to learn so much more than I can in the classroom. Working here has not only taught me software and design, but also how to teach myself complex concepts and read scientific publications effectively. Overall, the time I’ve spent in lab has only made me more enthusiastic and driven to do science as a career. I look forward to what the future holds for me and where the work I am doing now will lead me.

One unique aspect of our experiments is the incredibly low temperatures we go to. We often work at liquid helium temperatures, which is around 4-10 Kelvin (room temperature is around 298 Kelvin). We go to these low temperatures to get a noticeable increase in the performance of our coils.

One unique aspect of our experiments is the incredibly low temperatures we go to. We often work at liquid helium temperatures, which is around 4-10 Kelvin (room temperature is around 298 Kelvin). We go to these low temperatures to get a noticeable increase in the performance of our coils.

One of my mentors dogs that he brings to work =D

lycos

My mentor brings his dogs to work =D

Yes, that data is going in the opposite direction of the sensibly-expected trend, and is horrendously scattered. Such is life!

A Resource for Fellow Undergrads: Advice About Data in Professional Talks

You’ll be surprised how important this is. Remember how you learned to use Excel in junior high (or maybe earlier)? It was probably for a science project. Well, don’t worry, because as a researcher, you’ll never have to make another Excel graph in your life!

…because we use things like Origin and Igor instead.

…which are complex enough to have their own programming languages.


Why? Well, once you pull super-cool results from your research (which I’m starting to now, by the way, and it’s awesome), you’re presented with a problem: people don’t have much time, and you have a lot of information. Often tens of thousands of data points, from different tests and techniques over months of narrowly-focused work, with layers of data analysis in between, that you somehow have to compile in a way that these busy, tired people will understand.

And they don’t want to deal with lazily-made graphs.

If you think I’m making a mountain out of a mole hill, think again – it’s this kind of stuff that makes papers take years to publish. But that’s why it’s good to get a handle on it now, when you’re just starting out your career!


It’s easy to get mired in the details of each individual graphing software. You can probably use any, as long as it offers enough flexibility, so instead I’ll keep my advice general. The predominant choice here is Igor, which is the source of the plots in the “good” column. The “bad” is a combination of MATLAB and Excel.

This wisdom comes from the EUREKA program, and my research mentor.

Disclaimer: none of the “good” column graphs have titles. This is only because I like to interchange titles based on what I use the graphs for, without having to edit the graph image over and over. Always, always include a title.

Bad

M - Three times more filtering

You’ll probably see a lot of this. This is fresh out of MATLAB, and yes, something I actually sent my mentor at some point. It looks great at the time, since you’re buried in your process and don’t take a step back to look at it! But in reality,

  • The lines are too thin
  • The labels are tiny
  • Most of the labels are unnecessary, at least for a presentation
  • Two graphs are overplotted for no particular reason
  • It’s not cropped, and the axis range is poorly selected to include extraneous regions (including regions in which my filtering technique had some unprofessional hiccups!)
  • Image quality is low
  • Almost everything is whitespace or noise (extra “ink on the page” that doesn’t contribute to meaning) – which is a guaranteed way to make your graph confusing

But lastly, and most importantly, the purpose of the graph is not clear. Unless you need it to portray something, why would you show it?

Better (but not perfect)
MagneticTransition-2

This is the same analysis, but a version of it which could conceivably be used for professional work. It’s not quite what you would use for a paper (which is more formal), but it’s pretty decent for a presentation. And certainly it’s leagues ahead of the other one. The reasons why turn out to be good general tips:

  • Thicker lines
  • Bigger labels
  • Only the important and necessary things are labelled
  • Nicer colors* (this happens to coincide with color coding elsewhere in the presentation, which is another good strategy)
  • Overplotting can be fine, but if data sets cross each other, it can get confusing
  • The most important point on the graph is clearly identified
  • High image quality – never screenshot; any good graphics program will have ways to export high-resolution figures

*  Never use red and green to distinguish between different data sets on the same graph. Red-green colorblindness is surprisingly common!

** Only important in a presentation – complicated graphs are often the only way to go, in papers.

 Bad

BadCVsDopantFit

Ah, the other ancient enemy of clear presentation: the default graphs from Excel. This is something I had for personal use, to organize the outputs of my data analysis. Don’t assume that you can just put it in your slides like this!

  • Even if you’re going to explain what it means, there should minimally be some clues which are written
  • Both in the graph area and in the legend, there is duplicate data! That becomes noise to people who are trying to read your graph – or, in this case, it could make people think “Wow, that’s a really good fit!” when in fact the red points are what’s supposed to correspond to the trend line
  • Don’t use “e” or “E” for scientific notation. Write out *10^x, if you need to. Whatever takes up the fewest characters is usually the best choice, so in this case, it would be better to just use decimals!
  • There are some extra pieces which are legacies from analysis work, some points which are unverified, etc. – you will be expected to explain every single item on your graphs, and that can take time away from the good science!
  • More of the same stylistic issues as before
Better (but not perfect)
Graph0

So again, this is meant for a slide presentation. Readability and simplicity are key, as before. Here are some more things to point out:

  • Color choice is key. Notice that the red here is neither a primary color nor a default color – going slightly towards pastel from the brightest colors available to you often works well. (It comes across more clearly if you have a graph with many different colors, and makes you look like quite the professional.)
  • Credit is given to prior work – that fit line was obtained by a former group member, and previously published. If you don’t attribute work to those who did it, the assumption will be that you did – which can constitute plagiarism, with quite serious ramifications if it makes its way into a publication. Be overcautious!
  • Be creative with your axis labels. That doesn’t mean making them polka-dotted. That means choosing the format that works best for the graph you’re working with. You’ll notice that the x-axis variable is dimensionless. But I still had to convey what it meant! So rather than putting the units in parentheses, I included the chemical formula. I stopped short of explicitly pointing out the x in the formula because it’s expected that people will digest your graphs to some degree, and at some point more text just becomes noise.

I’ll leave you with a thinking point: How to deal with data that isn’t what you expected. Sometimes there are just deadlines, and you don’t have time to conduct the tests you would have to in order to correct your mistakes. What can you do? Be honest. Still choose the most effective way to present the data, even if it’s presenting your mistakes, or things you’re still seeking to understand. Most of your work is complicated. People won’t blame you for setbacks. But, as you’ll see time and time again, they’ll nail you for hiding them.

Yes, that data is going in the opposite direction of the sensibly-expected trend, and is horrendously scattered. Such is life!

Can you see the setback?

Stronger Together

IMG_2770

My mentor, Devyn Orr, and me at Tejon Ranch

One of the first things you learn when conducting field research is that almost nothing ever goes according to plan.   Whether it’s the field truck having engine issues, forgetting the food you packed for a three day camping trip, or the sun setting long before your “to-do” list is finished,  something is bound to go awry.  But all of these seemingly overwhelming issues become a lot less daunting when you aren’t the only one dealing with them.  Throughout these past couple of weeks, I’ve begun to understand what it takes to be a field biologist and how the people who are on your team with you make all the difference.

When it comes down to it, field research is all about teamwork.  Recently, we have been capturing Western Fence Lizards to scan for ticks.  We soon discovered that working in pairs was the most efficient method to catch them.  Bella, one of my fellow researchers, and I often scour plots for Western Fence Lizards, hollering when we’ve found one so we can set up an ambush.  If you’ve ever caught a Western Fence Lizard you know that they can be wicked fast but with the help of a grass noose, you can catch them if you’re smart about it.  Working in pairs is often the most efficient method for other tasks as well, such as conducting a bird survey or setting up cafeteria trials.  Though building the small mammal exclosures is technically my project, everyone pitches in to help dig the foot deep trenches and assemble the exclosures.  No one’s work in the field is truly independent and could not be accomplished without the help of many others.

I know my experience at Tejon Ranch would be radically different if not for my fellow researchers and my mentor. When you wake up at 5:45 AM after having spent the night in a tent there’s no better way to be greeted than by a beautiful sunrise and the smiles of your fellow happy campers.  And when the closest research plots are a solid 20 minutes drive from camp, there’s no better way to make time fly than shamelessly singing along to Fleetwood Mac.   Being together in the field means being there for each other, whether it’s helping each other identify species or sharing food or laughing about #fieldwork problems.  Conducting field research this summer has taught me about so much more than science and has shown me the value of surrounding yourself with smart, friendly ecologists.

Choosing a Career Path

Academia or Industry? This seems to a dilemma that almost all students in the science major faced. Just like them, I have not a single clue on what I want to do after I graduate with a degree in Biology. With no one in my family in the science field, both industry and academia seems to be a mystery to me. Thanks to the two amazing events that CSEP put on for the summer intern program (the Dinner with Faculty and the Dinner with Industry) I was given an opportunity to talk to people who are experienced in each field. These two events are extremely helpful for learning what it is like working in industry or in academia.

I have always thought of academia and industry as two parallel worlds that are so different from each other that they never cross path. To my surprise, I found out that there is actually not as big of a difference as I previously believed. Research in the industry and academia actually overlaps in many ways and are very similar in many aspect. Often, professors in research university collaborates with companies for new discoveries and some professors even started their own company. Scientists in the industry also conduct researches in similar fashion as professors. They have to meet deadlines, write research proposal for money, be the leader of the lab just like any research professors. In addition, for science major students who are fresh out of college, working in academia and in industry seems to be very similar as well. Both as a graduate student and as scientist in industry, one always start out with lots of hours behind the bench and slowly rises to a leadership position.
After the opportunity to talk to professors and leaders in industries, I completely change my view on my career path. In both industry and in academia, one has tremendous flexibility to switch from one to another or maybe even work in both.