As a data supporter you find out what your customers really need and are able to connect to their concerns while highlighting possible solutions and opportunities at the same time. In this section we will discuss the 'art of connecting'.
The question as a starting point for data support
Depending on the stage of a research project, researchers may have various questions such as:
- I have old tapes in my cupboard and now I want to digitise and archive them. Can you help me?
- We want to be able to share our research data with other research groups more easily. Are there solutions for that?
- Can you help me write a data management plan?
- Can you arrange access to externally collected data sets?
- How do I make my data anonymous?
- How do I create a data package for storing my research data on Zenodo? (Neylon, 2017)
I have data from a set of interviews. I have audio and I have notes/transcripts. I have the interview prompt. I have decided this set of around 40 files is a good package to combine into one dataset on Zenodo. So, my next step is to search for some guidance on how to organise and document that data. What you don’t get is a set of instructions that says “this is the best way to organise these” or good examples of how other people have done it..... | From the blogpost As a researcher…I’m a bit bloody fed up with Data Management. (Neylon, 2017)
The researcher's question is the starting point for data support. From the quote above you can see that this researcher is not looking for general information about data management. It's practical information that provides an answer to a question right now which is needed instead. Often questions researchers have are quite practical and at that moment you face the challenge of translating the available information into bite-sized chunks.
Before you are tempted to give an answer, it is always important to keep uncovering the question behind the questions. A question often already implies a solution. Take, for example, the question "How do I make my data anonymous?" The researcher wants to anonymise data in order to solve a certain problem. For example, he or she wants to publish the data in a data archive and at the same time comply with the GDPR. After asking further questions, it may turn out that pseudonymisation of data and the provision of restricted access for this data set is a better choice.
When making an inventory of the questions for data support, you naturally listen to the researchers themselves. In addition, you can check the scientific literature to see what questions other researchers have. A study from Springer Nature (2018) shows, for example, that researchers - to a decreasing extent - see the following challenges in sharing data:
- Organising data in a useful way;
- Uncertainty about copyrights and licences;
- Uncertainty about the most appropriate data archive;
- Lack of time;
- Sensitivity of the data;
- Data policy and culture within the institution;
- Fear of misuse of the data.
Sometimes data support isn't so much about arousing motivation, but about removing barriers or misunderstandings. For inspiration, you can have a look at the video below. It shows a number of concerns that researchers may have when sharing data. The video is based on a blogpost by UC3 (2013).
RDNL-video on responses to possible concerns that researchers have about sharing their research data. Select HD quality for the best viewing experience.
Data supporters are faced with the challenge of repeatedly finding out what the customer needs now and what motivates or hinders him or her. The concerns, uncertainties and wishes of researchers will continue to shift. Take FAIR data management. In 2018, 15% of the researchers are familiar with the FAIR data principles, 25% have heard of them and 60% have never heard of them (Digital Science, 2018). This shows that it will take quite some effort to ensure that this image will look very different in a number of years' time. For your success as a data supporter, the art of connecting to the 'now' may prove to be just as important as having substantive knowledge about data management.
Click to open/close
Digital Science et al. (2018). State of Open Data Report. figshare. https://doi.org/10.6084/m9.figshare.7195058.v2
European Commission (2018). H2020 DMP Template. https://ec.europa.eu/research/participants/data/ref/h2020/other/gm/reporting/h2020-tpl-oa-data-mgt-plan-annotated_en.pdf
Freiman, W.C.; Molly, L.; Jones, S.; Snow, K. (2011). Making sense: talking data management with researchers. International Journal of Digital Curation, 6(2). http://eprints.gla.ac.uk/49201/
Grootveld, M., Leenarts, E., Jones, S., Hermans, E., Fankhauser, E. (2018). OpenAIRE and FAIR Data Expert Group survey about Horizon 2020 template for Data Management Plans (Version 1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1120245
Neylon, C. (2017). As a researcher…I’m a bit bloody fed up with Data Management [blog]. http://cameronneylon.net/blog/as-a-researcher-im-a-bit-bloody-fed-up-with-data-management/
Strasser, C. (2013, April 24). Closed data ... excuses, excuses. [blog]. Data pub. http://datapub.cdlib.org/2013/04/24/closed-data-excuses-excuses/
Stuart, D., Baynes, G., Hrynaszkiewicz, I., Allin, K., Penny, D., Lucraft, M., Astell, M. (2018) Whitepaper: Practical challenges for researchers in data sharing: figshare https://doi.org/10.6084/m9.figshare.5975011.v1
University of Twente (n.d.) LISA. Data Librarian. https://www.utwente.nl/nl/lisa/bibliotheek/hulp-nodig/bentum/