After ten years of postdoctoral research there is one important piece of advice I would give to anyone embarking on a research career:
Spend as much time managing your data as you do generating it
Take time at the beginning of each project to organize how you will record what you are doing day-to-day. The ultimate goal is to develop a map that anyone can follow to retrace your steps and perform the same experiment.
The most important questions to ask yourself are:
How do I record how I carried out my experiment?
How will I organize and store my data?
Here are several tips to help you make your map.
Decide Where You Want to Record Your Day-to-Day Experiments
The paper lab book is the traditional recording medium. Often however, (myself included) lab books become laced with short hand and bad hand writing as you become swept up in the chase of that precious experimental result. A couple of years down the line it can be difficult to remember or repeat exactly what you did. So make sure you spend time organizing your lab book. Look back over your notes while the experiment is still fresh in your mind. Rewrite sloppy entries and add in notes that you may have forgotten during the rush of the day.
Although most people I know still keep a paper lab book, digital lab books are appearing in more labs. There is a range of available software options to suit everyone and make things easier. Just make sure your PI is ok with an electronic notebook.
Document Your Analyses
As important as it is to document how you did the actual experiment, it’s equally important to record how you analyze data and save all the steps along the way. When it is time to publish, you will need to produce all your data as well as the analyses, and keep it archived for a number of years. Also, you never know what the future may bring. You may wish to look at something again in a new light armed with a new technique or form of analysis.
Write down what you used for controls. What statistics did you employ? What software did you use? Additionally, it’s a good idea to keep a note on how what you did compares to other labs along with copies of the relevant papers.
Electronic Data Management is Important
Right from the beginning, think about data storage. It’s easy to lose track if you have hundreds or thousands of big data files. In addition, data can be corrupted or lost if the software crashes or there’s a power outage (both of these things have happened to me). But you’ll have nothing to worry about if you have all your original data, organized, stored and backed up. Make sure your data is stored in multiple locations in different, easily accessible formats. Computers can be stolen and hard drives fail (I have witnessed both these events).
There are a number of data storage solutions out there to suit everyone. See www.data-archive.ac.uk for guidance, training and data services. If you already have your plan in place, you’ll be a step ahead.
Don’t Forget About the (Seemingly) Small Stuff
Finally, double check and record the small stuff, no matter how dull it may seem. Over time, experience will influence what you think is important to remember, but at the beginning, it can’t hurt to write everything down somewhere. Take pictures or screen shots. A picture can convey all the information you need to remember or pass on to a colleague if needed.
For instance, keep a record of the exact form and manufacturer of chemicals and reagents you use (storage conditions and how a compound is prepared can vary from place to place). You can easily put the datasheet in a binder when you receive the order the first time. This will also make re-ordering easier.
If you receive a gift from another lab get all the details you can. Document who you received it from (the PI and the person who sent it to you), how much you received, and recommended storage conditions, etc.
Also, don’t forget about your instruments. Check equipment settings, makes and models and record that too.
A little bit of preparation and time spent thinking about how you will conduct your experiment at the beginning will save you time and avoid unnecessary stress later on.
In the last years, the need for using statistical testing in bioscience has grown exponentially and so has the development of statistical software. It is now common that everyone is using some sort of stats in their basic research. Among the skillful biostatisticians, R is the most popular software for data analysis, but not all […]
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