By Cameron Kieffer
Cameron Kieffer is a graduate student studying pharmacology at the Creighton University School of Medicine in Omaha, NE.
Computer Skills for Scientists
The dawn of the computer age is over. Computers are no longer just hobbyist toys or specialized equipment; they have become an integral part of the scientific process. In fact you are almost certainly reading this on a computer. As science’s reliance on computing grows, so too will its need for able-minded computer users. Graduate school or post-graduate training is not only a time to develop your bench top skills, but also a time to focus on honing the “soft skills” of independent critical thinking, teamwork, networking, and scientific writing. Allow me to propose that you should also spend some of your (limited) time expanding your computing prowess.
Streamline Workflow and Save Time
Many commonly used programs, like Excel or ImageJ, allow you to write macros that will repeat a task over and over. Macros are sets of instructions that tell a program to repeat a specific sequence of tasks. Need to perform the same analysis on hundreds of images? A macro might work. Do you need to import, manipulate, and format a series of datasets in Excel? Try a macro. Some require a bit of coding knowledge, while others are able to record a sequence of clicks. You could potentially save yourself hundreds of hours by automating some of your more monotonous work. Take a day to look through your favorite program’s documentation or help files to learn what options are available to you. When in doubt, Google it. Chances are someone has already done something similar.
Improve Clarity of Scientific Reporting
Mistakes are inevitable, but they are a huge problem for data reproducibility. Automated computing tasks help to remove unintended biases in data analysis by essentially “blinding” the researcher leading to increased reproducibility. Journals are pushing for researchers to publish their raw data and program code. Peer review only works though if you, as a trained scientist, can interpret that computational information. Systemic statistics illiteracy is also a common problem that is closely tied to computational proficiency. The more you practice and explore a particular program, the more positive you can be that you are using the appropriate statistical tests. If you are familiar with GraphPad, check out their thorough documentation which can answer most of your stats questions.
Open New Doors to Different Career Options
Bench top skills make you a good technician. Pick your favorite bench top technique. Are you really good at it? Then chances are high that so is everyone else in your field. Your competition likely has a similar publication record. They probably even took the same basic classes as you. How are you going to stand out in the academic field? Computer skills are, as they say, another tool in your toolbox that may help you stand out from the crowd. Universities are looking for faculty and post-docs that will add something to their team. That something could be working knowledge of a unique (and hopefully useful) computer program.
While almost any skill may give you a competitive edge when applying for jobs in general, certain careers are looking for scientists with specific skills. Clinical trials and high throughput screening create monstrous mounds of information. While the clinicians and technicians are running the experiments someone has to translate the results into information that the decision makers can use. Data management tools like SQL, R Tool, SPSS, or SAS are great ways to practice making sense out of huge amounts of information, are broadly applicable, and are sought after skills in industry. “Big data” is an emerging field that may provide a potentially satisfying career path for PhD level scientists.
If you are interested in lab management as a career post graduate school then experiences with software like Labguru and Quartzy are good places to start. Because this software can link specific reagents to each protocol they can add reproducibility to your current lab’s data too. Software like these allow for better sharing of data between lab members and/or collaborators. Finally, “Open science” tools like the Open Science Data Cloud allow researchers to access public datasets as well as make their own data available to collaborators across the planet.
Data visualization is a trendy new field that revolves around transforming raw numbers into thought provoking graphics. Journalist organizations, both traditional and web-based ones, need people who are familiar with turning complex scientific ideas or large data sets into attractive and informative images. Professional biostatisticians and bioinformaticists also should be able to communicate their complex results to diverse audiences. Plotly has a Python library all about visualizations that might be a good place to start.
While not every job outside of academia will require a PhD and each discipline has its own set of heavily used programs, it would be unreasonable to try and learn them all. However, the simple act of independently learning a new skill demonstrates that you are driven to learn and can readily adapt to whatever technology is presented to you. Data visualization is a trendy new field that revolves around transforming raw numbers into thought provoking graphics. Journalist organizations, both traditional and web-based ones, need people who are familiar with turning complex scientific ideas or large data sets into attractive and informative images. Professional biostatisticians and bioinformaticists also should be able to communicate their complex results to diverse audiences. Plotly has a Python library all about visualizations that might be a good place to start.
While not every job outside of academia will require a PhD and each discipline has its own set of heavily used programs, it would be unreasonable to try and learn them all. However, the simple act of independently learning a new skill demonstrates that you are driven to learn and can readily adapt to whatever technology is presented to you.
How to Get Started
One option is to ask your PI if they or your research group has a website. If they do not, help make one to showcase your lab’s current research and makes it accessible to potential new members browsing the web. You can also make a website for yourself. This can serve as both an online resume and tangible display of your HTML/CSS abilities. A completed website gives you a concrete “finish line” to your independent study. Plus with websites like Squarespace creating a new website is easier than ever.
You could enroll in a computing class at your university or seek out online courses. Codecademy has free lessons on programming basics and website design. Another potential avenue for learning is Software Carpentry. Software Carpentry is a global network of courses and instructors that seek to teach basic computational workflow skills to scientists. Classes like these can help you to start thinking like a programmer and open your mind to potential uses for code in your own research.
These are not your only options. You could make a video of one of your laboratory methodologies for YouTube. Learn a new program to calculate ANOVAs. Start a podcast with members of your lab. Ask someone in another lab if they can show you what software they use. It doesn’t have to be a huge time commitment. You can get a free week-long trial at Lynda.com where they have hundreds of videos on cutting edge software, but you have to start somewhere. Even practicing some of the advanced functions in Excel can go a long way towards being an effective, tech-savvy scientist.
Is this list comprehensive? No. If you learn a new skill are you guaranteed a job? No. But computers will only become a more integral part of quality science. With a toolbox full of computer based skills you will be able to more nimbly assert yourself in the job market and the scientific community. The key is to try something new that you find interesting and maybe you can turn a hobby into a new career.