Improve your data management in clinical research

Data management is boring and quite likely, you are not happy about how you do it today. Research data collected in practice typically evokes complaints when it finally reaches the statisticians or bioinformaticians as they claim that it is not being properly organized. That’s not because you made a mistake: it’s simply hard to do when you set up your small scale study. Tools like Excel will give you practically infite freedom with little guidance and every study seems to be so different from the last. Basic data management isn’t really taught in most research environments unless you’re talking about clinical trials that require elaborate and specialized software systems. Luckily, there is a Coursera course starting on June 2nd, 2014 that teaches the basics of data management. While it is focussed on the tool the organizers provide, the contents of the course should allow you to build better data structures.

Instructions.  The course requires no particular IT knowledge and only modest experience in clinical research. Primary concerns are set-up of a study, regulatory compliance and software that helps you to get your job done. The time commitment is fairly limited, spanning 5 weeks with only 2-4 hours weekly. These are the numbers given by the instructors, I certainly didn’t spend more than 8 hours in total on the last installation of the course when I took it last year.

redcap_clear_high_resREDCap not Excel. Allegedly, the course is built around the use of a particular software system called REDCap but covers all the important aspects of managing clinical data. REDCap itself is easy to install for a clinical system but most likely, there are installations available around you that you can use, for instance the one we maintain at the Luxembourg Centre for Systems Biomedicine for projects in EuroEPINOMICS. I would recommend the course even if you’re not planning to use any system online.


Roland Krause

Roland is a bioinformatician at the Luxembourg Centre for Systems Biomedicine. He received his undergraduate degree in biotechnological engineering and a PhD in biochemistry from the University of Heidelberg. His postdoc was in computational biology at the MPI for Molecular Genetics, Berlin, shared with the computer science and math department of the Free University Berlin.