SWFsEUROPE

I am a political economist currently researching the actors and processes involved in realising Chinese state-owned foreign investment in Europe. Fundamentally, I am interested in researching questions of political, economic change, the inter-relatedness of states, markets and their political construction. I am trained in both qualitative and quantitative methods (R, Stata, Python). Before embarking on an academic career, I worked as a book editor for six years.

The research I’m doing is primarily qualitative, and my primary source is semi-structured interviews, specifically elite interviews. That can sometimes present its own challenges, and in terms of FAIR, this means that I essentially make my meta-data available.

I apply FAIR in my own research first from the faculty’s perspective, where we have chosen to adopt the F and the A of the principle, which is findable and accessible.

I work with archival data and semi-structured interviews and for that means ensuring that I’ve catalogued my data correctly. So practice sound data management principles, I ensure that my data is first findable to the members of my research team and then storing it in a data repository for us that’s dataVerseNL.

The next step in the project is to make the data inter-operable to the members in our team and that involved coming up with a standardised way of reading all about the data we collected as a team in a variety of contexts, so, for instance, the nature of the data, context, which kind of people are interviewed.

Data Stewards are essential people to be working within making data FAIR. And what they do is really help you bridge the gap between a set of abstract principles [Ed. Imogen and her team used the Data Steward services available at the University Library].  How to go on step by step when sitting in front of my computer or sitting in front of an interviewee to work FAIR.

Since September 2020, when we started working together, we have uploaded draft versions of their meta-data on DataVerseNL. We have agreed to publish it once the final data is collected, publications are accessible, and the project is finished.

This decision has been taken based on the data’s sensitive nature and to protect the participants’ interest in the study. Our last interview took place in July 2020.

The data is essentially qualitative data physically gathered by the research. This data is, in essence, not machine-readable.

There are many advantages to FAIR. The challenge is to find out how to go on? Especially to find out how our data translate specifically to different forms of data.

The challenge with qualitative data is that I am doing interviews with elite interviewees, and the data can be sensitive. So it touches on issues like confidentiality, trade secrets, data privacy, and all these issues are things I need to think about in terms of ensuring FAIR and that it does not infringe on any ethical issues that the respondents have consented to.

I think with any form of systematic accountability, there is additional work involved. I think the challenge now is we are just trying to make it off the ground. So, you know, maybe then it means the setup costs are higher. In the end, if everything is more standardized if researchers are thinking more about FAIR, then it pays off in the long run.

As explained before a draft version of the project’s data sets has been upload. We have agreed to publish at the end of their PhD projects. Consent, in qualitative research, is often a continuous process. This means that until the end of the PhD, the researchers won’t be sure which meta-data is highly safe for the researcher and the participants.