Whether you’re an entry tech, an optimistic grad student, or a seasoned post-doc, we’ve all managed to get into a rut with lab etiquette and how we “do” science. With spring-cleaning on everyone’s minds, here are a few reminders that may help you clean out the proverbial cobwebs and start afresh.
1. Label EVERYTHING
Several of my friends grew up in households where leftovers would be stored in the freezer, with contents and dates marked with a Sharpie. These people know what’s up. Keeping things straight is difficult enough in the civilian world, but in science it’s down right impossible. The numbers of intricate details you have to recall about experiments and data, as well as the theories and hypotheses you’re either consciously or unconsciously mulling over, is enough to drive anyone to the brink. So please, write it down, whatever “it” is. Be kind to your future self and over-label/explain everything. You won’t regret it.
2. Memorize This Equation
The most dependable, constant equation that so many reach for time and again is:
C1 x V1 = C2 x V2
It may not look like much, but it will guide you through dilutions, molarity calculations and so much more. Keep this gem in your back pocket at all times. For more useful lab equations and maths solutions see our previous article on A Guide for Solving Your Lab Math Problems.
3. When in Doubt, Turn it Off…
And then on again. This goes for just about any computer or machine in your vicinity. It may not always work, but it’s the simplest and quickest place to start before you head into a downward spiral at the thought of losing your experiment because something crashed on you halfway through the run/analysis.
4. Slow Down!
I admit this is something that I still struggle with myself. I have been known to move too fast in my attempt to multitask several experiments, leading to everything suffering in the end. Plus, running yourself ragged is terrible for that beautiful brain of yours! So whether it’s how you plan out experiments or how you run your gel (trust me on this), Turtle was right: slow and steady is the way to go.
5. SD v SEM
Statistics can be a scary thing for scientists, and that’s ok. Still, that’s no excuse for not knowing the difference between standard deviation (SD) and standard error of the mean (SEM), and when it is appropriate to use either one (see below for some great websites to help you with these). Your PI will thank you.
6. Have a System
Remember when your mom kept telling you to clean your room, or that everything needed to go in its place? That woman is smart. Keeping organized is absolutely essential in science. You will acquire mounds of data (some usable, some not) in your years as a scientist. As relevant as it all seems now, you will likely push it to the back of your memory to make way for newer things (see point 1). So have a system in place for organization that makes sense to not only you, but also some one else looking at your set up for the first time. Descriptive names, allusion to a specific project and date all come in handy. Trust me. And your mom.
7. Clean Up After Yourself
Your mom doesn’t work in your lab, but I’m sure if she did she’d tell you to clean up after yourself. Dirty labs are disgusting. You can’t find anything and it doesn’t bode well for sterile technique. Don’t be that person.
8. Back It Up!
For the love of all that is holy, back up your data! Whether it’s a work computer or a personal one, you NEED to have several places your data is backed up. There are so many decently priced options now (some that are even FREE), there’s really no excuse anymore for lost data. In the category of free storage, I am particularly fond of Dropbox, but I know several people that get a good amount of work out of Box and Google Drive.
What do you think? Are these hints helpful, or old hat? What useful nuggets have you implemented in your day-to-day science life? Comment below!
So, you’ve just received a call from the core facility that you hired to prepare and sequence your libraries. The facility director tells you that the sequence data from your next generation sequencing (NGS) experiment does not look good. You panic and, perhaps, let loose a scream of frustration—aaarrrrggghhhh! This project was going to be […]
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