A Path To Data Management
Frank originally worked in a laboratory where he also performed research. Once his research delivered a substantial amount of data, he discovered that he actually enjoyed the processing of the data more than the work he was doing in the lab.
“That was the moment I started to delve more deeply into the world of data and transitioned into data management. Because of my lab and research background, it is easy to talk to researchers and understand how a study is set up, what data they need, and what the eventual dataset should look like.”
“Good data quality is essential for any study. One can only draw reliable conclusions if the study data is reliable. ”— Frank Leus
From Beginning To End
Data management is involved from the beginning to the end of a study.
“At the start of a new study, we first talk with the study team and principal investigator to discuss the study protocol and clarify what data management needs there are. We then develop a data management plan, coordinate data collection and create any necessary tools. During the study, we ensure data quality and provide regular reports. The data management team also serves as a helpdesk. At the end of a study, we are responsible for data cleaning and the delivering of the data sets. Once a clean data set has been delivered, data management’s involvement comes to an end.”
- “What I enjoy most in my current role is being part of a lot of studies, from the start until the very end. It’s exciting to work in an international environment. It’s a large community where you frequently come across different European colleagues and develop long-lasting work relationships. Especially now that I’ve been working in data management for over 15 years. The main advantage of working on European projects at the University Medical Center Utrecht is that there are less specific roles within data management, with very specific tasks assigned to specific people. I am thus able to work on a broad number of tasks and have more freedom to organize my work.”
During the COVID-19 pandemic, scientific teams within academia and industry worked around the clock to find desperately needed answers, resulting in a huge number of new studies. This also resulted in a lot of work for the data management team. “The tasks that usually were accomplished in a couple of months, now needed to be ready in a matter of weeks.”
Not only did the number of studies increase during the pandemic, but new innovative study setups also arose. “We learned a lot during the pandemic. Platform studies, such as the REMAP-CAP study, turned out to be perfectly suited to conduct research in a pandemic. For the data management team this meant adapting to a new study design, including adaptive randomization, which is very different from other clinical trials.”