Method

Here, we use the text collected and analysed so far (mostly via browsing the web, few direct contribution from the crowd), comparing it to a report from the research data alliance report. Both reports can be found as blog posts found under the category analysis.

Summary

We focused our analysis on three interesting content: Marketing strategies (how to reach researchers), arguments for research data management, and stories to tell.

marketing strategies

branding

Libraries seem to be sometimes viewed negatively by the researchers (as a bureaucratic hassle). In order to change this view or pass around it, it was proposed to work with the researchers, such that researchers do the marketing. Including researchers in the writing of grants might be a good way to approach them.

Similarly, RDM may be seen as a bureaucratic task. One way to go around the problem is to attract scientists with the topic of data analysis (push data management in data analysis lectures). In any case, pushing positive argumentation seem to lead to better results than showing the stick of funders requirement. Another way is to present RDM as a tool to increase efficiency, as arguments about data sharing were not working.

Using specific times (love data week, or other) to target campaigns into restricted time is an effective strategy to get coverage and traction. On the other hand, this kind of pooling events in a small time widow, coinciding with international events, might be best used in conjunction with frequent, small-scale messages.

Finally, the use of doomsdays scénarios and story-telling seem to be most effective, at least at the first contact.

Communication channels

• Series of talks
• Advertisement offline through posters, buttons,
• Using mailing list, event lists and other internal communication
• Social media (twitter, facebook)

Additional thoughts

I (Julien Colomb) was suprised not to see any use of graduate schools in the communication channels, as I think it might be a good way to access young researchers. The use of comics and videos seem not to be very broad in the community, maybe because most marketing happens via direct contact ?

Content

Stories and scénarios

A particularly effective strategy seem to be to use doomsday scénarios, either data loss via fire or flood, or accusation of fraud. While it cover only a fraction of RDM, it seems to be effective in raising the interest of researchers. There are no real stories available at the moment, these scénarios are kept on a theoretical level.

Arguments

In a second phase, one can use different arguments to convince people to do RDM and open data. There are some “stick” arguments (open data is/will be required), counterarguments (dissolving fears about open data) and positive arguments, either practical (effectiveness, better analysis, data security,) or ethical (more effective science, sharing values, data quality). In addition, for teaching RDM, one can use arguments about how important these skills are for career advancement (also outside academia).

Conclusion

Only the efficiencies and ethical arguments were reported in the research data network blog (2017). This is in contrast with the reaction of scientists when asked about their happies data moment: they only refer to data analyses. I therefore think there is a high potential in presenting a pragmatic approach to rdm, where its importance for data analysis (on the long term) is emphasized.

Distributed under a CC-BY license

Julien Colomb, data manager