Over the course of my career in academic research, I have observed substantial shifts in science and how it is practiced. One notable difference is how data-intensive our work has become; with today’s technology and computers, we now have the possibility to acquire huge quantities of data in short periods of time.
Now that I have this huge amount of data, I have to figure out what to do with it.
To compound this issue, science is becoming increasingly interdisciplinary. To identify areas of study and collaborate effectively with researchers in other disciplines, we have to develop a broader knowledge of science, and that means keeping up with research in other fields and assimilating even more information.
I completed my PhD in biophysics, an interdisciplinary science apparent from its name alone. Luckily, I have a strong understanding of biology, chemistry and physics; but it seems that nowadays, that alone is not enough – I also have to learn at least a basic level of programming to even interpret my data. To use an old idiom, this is a blessing and a curse; on one hand, I am forever learning, a situation that usually excites and pleases the academic scientist. On the other hand, this process can be slow-going, and since our careers rely very much on our publication record, especially at my level, this is obviously problematic.
Fortunately, there are tools that help us deal with this deluge of information and make it work in our favor.
For example, I recently started a postdoc using nuclear magnetic resonance (NMR), a technique that is new to me. I am studying ribonuclease H (RNH), an enzyme that catalyzes the separation of DNA/RNA hybrids in nearly all organisms. Specifically, I am investigating the protein from the anaerobic bacterium known as S. oneidensis. In humans, this protein is important in certain rare diseases and AIDS; my lab has characterized the RNH from various organisms that live in different temperatures to find the structural differences.
S. oneidensis was isolated from a lake in upstate New York, so it lives in relatively cold climates. I will compare my structural data with the data my lab has already acquired. To start my project, I purchased the DNA that is the code for the protein and inserted it into the bacteria E. coli, which are easy to grow in the lab. After the E. coli produced my protein, I purified it using protocols that have been extensively developed in the field of biochemistry. I also incorporate radioactive isotopes into the bacteria so that when they build the protein they are incorporated; this makes RNH visible in my experiments. Once my sample is ready in the correct buffers, I can do my NMR experiments.
The way this data is acquired by the spectrometer is somewhat complicated; it is Fourier transformed twice. This means that I must have an intricate understanding of several steps of data processing to transform it, which was initially difficult for me since I was learning how to do this processing from scratch; most people at my level already have a great deal of experience.
The data interpretation is done with computers, and the operating system we use in my lab is Linux – that alone can be challenging if one is not used to it. Fortunately, I’ve had experience with Linux in the past. I know people that are learning Linux for the first time because the technique that they employee uses software that will only run on that operating system. This can be very daunting at first for some researchers.
Technology has enabled a new level of efficiency in many ways – for example, via faster and heavier data acquisition. This is no surprise as we see this in many fields. However, what this means is that what used to take a couple years now takes a couple months. With that turnover comes the expectation of being even more productive with your data. Science is moving at a very fast pace, and it can be hard to keep up if you’re not on your toes at all times.
Using technology for research
While technology creates challenges for researchers, it can also help us manage our research and overcome challenges. For example, I use ScienceDirect to find articles relevant to whatever topic I am researching. It’s comprehensive, which can mean there’s a huge amount of information to find your way through, but there are tactics to navigate. Also, like Netflix and Spotify, the platform detects what your interests are and recommends related research you may have missed, which helps to stay current.
In addition, ScienceDirect has a 3D molecular viewer that enables me to view protein structures in articles quickly. For example, when there are protein structures from experiments involving X-ray crystallography, I can simply click in the article and view them without downloading the structure and opening it with a separate program. I greatly appreciate this tool because it allows me to view these structures without wasting minutes; even if it’s something that isn’t a core interest of mine, I can take a quick look in seconds. And if I see something that I want to look at in more depth, I can open the protein structure with a more specialized program, like PyMOL.
Another platform I use is Mendeley – a free reference manager and academic social network. We all know how difficult it can be to submit to different journals, all with their own citation requirements. Mendeley allows you to change citation styles after you have written the article, which saves so much time. It has a desktop interface and a Chrome extension that allow quick publication searches and additions to your library. I use LibreOffice as my word processor; Mendeley has an addition to this program as well that makes it extremely easy to insert citations into an article I’m writing. This function exists for Microsoft Word as well.
Whether using general software or the more specialized tools specific to my field, I have spent many hours watching software tutorials on YouTube and finding information online for troubleshooting. I would be forever lost without the help of others that have experienced similar problems and taken the time to share with the community.
Advice for young scientists
My advice to young people who aspire to pursue a career in academic science is to get some training in other types of science while you are still in school; instead of taking that art class, which might be fun, go for computer programming. If you are training to be a chemist, an extra physics or biology class might be helpful somewhere down the line. You never know what you are going to end up doing and who you will end up collaborating with, and you will likely have a big advantage if you have a foundation in other types of science.