Understanding a researchers’ data in 10 questions

Having almost completed the data inventory portion of our investigation, it seems a good time to reflect and explain a little about our approach.

The Archive has previously developed a data interview methodology for the JISC DMP-ESRC project, which formed an excellent basis on which to develop ours. I also spent quite a bit of time reading up on other institutional approaches, particularly those based on the DAF framework (some good examples in the methodology document). There were of course many recurring questions across these various examples. The version we settled on focused down on a critical series of questions while taking an unstructured approach to the interviews. This helped maintain an atmopshere of openess with interviewees, and allowed us to better squeeze into busy schedules.

I’ve put together 10 questions, based on a more expansive guide-sheet, which I think cover pretty much all the essential ground in a data inventory exercise:

1. What are the types of data handled, and how are they acquired?
2. What data formats are used and are they open or proprietary?
3. To what extent are datasets accompanied by documentation?
4. How are the data stored, and with what level of security?
5. How is versioning of stored data carried out?
6. Have issues of ethics and consent been sufficiently considered?
7. Have issues of copyright and intellectual property been sufficiently considered?
8. Is data shared with anyone inside and/or outside of the University?
9. How confident would you feel completing a data management plan?
10. What could the University do to make managing your research data easier?

 

It is worth noting that it might pay to have an example of each of the above in memory, in order to illustrate your question to someone not familiar with the terminology. I found a useful intro to be just sitting at a computer with the interviewee and letting them explain the process in their own terms. This can then be followed up with more focused discussion.

In light of this exercise, I will be thinking more about the role of the data interview. While it’s tempting to launch straight into a rigorously structured questioning, I now think this kind of work should be undertaken with rather more subtlety. The key aim for our project is to tease out those data challenges, and this requires a flexible and patient approach.