‘Bad RDM affects the quality of the research as a whole’
An interview with Emiel van Loon, Science lecturer
In 2020 it should be conventional that the UvA and HvA teach proper research data management. At least, that is the goal for Phase 2 of the RDM programme. Emiel van Loon, who works with the Faculty of Science Institute for Biodiversity and Ecosystem Dynamics, already systematically addresses managing research data in his lectures. ‘In their third year students are grateful to have covered it.’
What should students know or rather be able to do concerning research data?
‘They should realise that poorly documenting and managing research data can have unwanted effects on both themselves and those working with the dataset. RDM is not just a valuable skill within a university but also with future employers. Both in government and in business they often manage their research data better than universities do. Engineering firms and Philips, for example, have known for years that proper research data management is profitable in the long run. While within universities this is not cause for excelling, within business that is not the goal of RDM, they have a very rational view of costs and benefits.’
Which aspects of RDM are particularly important for students? Planning, storing, sharing?
‘The whole spectrum. It's more of an attitude. If you put your research out into the world you have a responsibility to manage the data. Sadly this does not coincide with the way university education is organised where it’s practically ingrained that one does not look at the data a few short weeks after the project is finished.’
Are there any specific knowledge/skill requirements you that expect from Science students?
‘I actually expect all UvA students to be able to name the FAIR principles and know what they entail. These are not based on any technical knowledge of software. Furthermore, I hope that Science students have practical skills with regards to data analysis and programming. Here at the Faculty of Science most students conduct experiments or observational studies that involve collecting large amounts of data with automatic sensors. This produces a lot of data that cannot be stored with a standard software and cannot be checked with the naked eye. In these types of situations it’s important to organise the research from the get go and to write a software that checks the quality of the data during collection process (e.g., via visualisation and automatic checks). Every Science student should be proficient in this.’
What is the students’ level of knowledge and skill in this area at the moment?
‘It’s inadequate. At the Faculty of Science we try to introduce the subject from the first year. Not just in the thesis phase. At that point RDM should be routine and the focus should be on the research. RDM should be should be effortless by then. It will take a while before RDM knowledge and skills are incorporated in the curriculum to such a degree that we adequately equip our students. This requires, among other things, a lot of training for lecturers. There is, however, a lot of enthusiasm and the assessment panel also realises the benefits it will have.
Last year the bachelor programme of which I am a lecturer and three other’s included RMD exit qualification in their course and examination regulations. In doing so the bachelor programmes take responsibility to cater to RDM.’
And in the master programmes?
‘Those students come from such different backgrounds that they too can be found lacking in this area. That is why I want a refreshers course. Unfortunately we can’t have everything at the same time. I have taken it upon myself to provide the agenda with a needed push.’
What data accidents do you see happening?
‘You often see small accidents. In their research students lose a lot of time restructuring the data and revising the metadata. This is inefficient and frustrating. Sometimes the cause is a flaw inherited from another student or researcher and sometimes its due to a lack of knowledge or supervision. If a lecturer is serious about supervising they have to meet with the student frequently and really question the data: what does the data mean? How has it been documented? Furthermore they must make time to check each phase of the data collection. That’s when you find out if the research is going in the right direction. This is so important because poor RDM affects the quality of the research as a whole.’
How do you address RDM in the lectures?
‘Students are asked to create a questionnaire and to label and process each part of the results. The next step is to have them collate these different parts. This is how they encounter problems that need solving and are made aware of the importance of clear structuring. We also give them contaminated datasets like the ones you meet in practice. With missing values and typing errors. They get a first-hand experience of struggling with data sets that have not been copied accurately.’
Do students appreciate RDM training?
‘It’s usually Earth Sciences and Ecology students who have just started the bachelor that don’t enjoy the subject. ‘That’s not why we chose the study’ they’ll say. However, later on in the study nearly all of them develop an appreciation for it. By the third year they are grateful to have covered it. As lecturers we should definitely focus on getting students to enjoy the subject, however, just as with learning to write or any other academic skill, you have to work at it. It would be great to have a RDM feedback session. To have graduates come and talk about how they use these skills in their current job or research practices.’
Interview: Alice Doek