Take data management seriously...
...and ask the data steward for advice
WUR will be training at least 100 data stewards over the next while. These stewards will be tasked with informing colleagues about how to collect, process and store research data properly, and helping them do this. Who are these people and what data usage issues do they run into at WUR? “The really difficult part is convincing fellow workers to take data management seriously.”
‘You have to convince someone who has been working in a certain way for years’
Reuse of research data makes our work more effective and transparent. That is why WUR will be working on its FAIR data policy over the next few years. FAIR means that data must be Findable, Accessible, Interoperable and Reusable. Essentially, the idea is to improve the links between the data produced by research and researchers’ publications.
On paper, WUR’s policy ties in well with national and international developments, says data coordinator Jacquelijn Ringersma of the Wageningen Data Competence Center (WDCC). “But the difficulty is that researchers hardly ever stick to it. We’ve put a lot of effort into communication about why the policy is needed and how it works, and we’ve set up some pretty good services. But implementation is lagging behind. I’m not sure whether that is because researchers don’t see the urgency or because they still don’t see data management as part of the research process. We have four FTEs manning the Data Desk and that is obviously not enough to support 3.000 researchers. That’s one reason why we are introducing the data stewards.”
What does the role of a data steward involve, exactly?
Joeri Kalter, data steward in the Human Nutrition & Health division: “At present, the food science researchers don’t generally pay enough attention to sound planning and execution when it comes to digital data. This applies before, during and after a research project. As a data steward, the challenge is to convince someone to use a different approach to their work after that person has been working in a certain way for years. For example, you may write up a good data management plan but it is important to implement it too. If you don’t, that may affect the usability, reusability and reproducibility of the data. A data steward can help you improve that process.”
What are the tricky issues?
Sylvia Brugman, assistant professor of Cell Biology and Immunology: “I find the really difficult part is convincing fellow workers to take sound data management seriously. If the chair holder, the person with ultimate responsibility for the group, is not particularly interested in the topic, then people are not held to account. Or at any rate, any failings have no consequences.”
“Another tricky aspect is that the data stewardship is one more additional task on an employee’s plate. So far, various people have taken on this role in addition to their regular work. A few groups have appointed dedicated data stewards but most groups are using existing staff.”
Datastewards during a workshop of the WDCC. Photo: Judith Jockel
What do you see as a focal point?
Felix Homa, Bioinformatics specialist in the Laboratory of Microbiology: “Ideally, we would store all our research data free of charge and have access to it whenever we want. But unfortunately that is not yet practicable. My role at Microbiology is to make sure that researchers and students have access to enough computing power to process their gigabytes and terabytes of data. In the future, I can imagine all the data being chucked into a system along with some metadata, with the computer then storing that data and processing it automatically. In such circumstances, users will no longer have to think about where to store and use the data or how.”
‘I make sure researchers have enough computing power to process their gigabytes of data’
‘Poorly documented datasets are sometimes difficult to interpret, even for the original researcher’
How is WUR doing at present in terms of the FAIR data policy?
Theo Viets, Animal Sciences education and research employee: “We’re at the start of a process. First, we need to convince researchers of the importance of documenting data properly. I know from experience that poorly documented datasets are difficult for others to interpret, and sometimes even for the original researcher. If you look at FAIR, we are doing well for the ‘Findability’ and ‘Accessibility’ aspects. You can find all kinds of data in the WUR Library and it is very accessible. Now we are focusing on ‘Reusability’, and we should really include the ‘Interoperability’ at the same time. That’s quite a challenge as you can’t assess which datasets will still be readable in the future.”
Photography: Judith Jockel
The FAIR principles are guidelines for describing, storing and publishing scientific data. The ultimate goal is to publish verifiable research results with reusable data so that others can find the data and use it themselves.