Influence sphere

    Main points

You’re never a data supporter all on your own. Data support takes shape in several company units and on several levels. Top-down and bottom-up. In order to be able to work as effectively as possible, it is helpful to gain some insight into the various interests, components and responsibilities inside and outside your organisation.

"It is important to note the linkage between Research Data Policy, Technology and Support. To promote conscious and successful use of research data, these three aspects should be offered simultaneously to researchers." - LERU Roadmap for Research Data(1)

A data supporter functions within an influence sphere of:

  • Legislation and policy.
    What laws and policies will you encounter? Organisation-wide, national, European?
  • Technological infrastructure. 
    What tools are available? Are they accessible and user-friendly? Do the tools fit in the researcher’s workflow?
  • Culture.
    What are the research practices? How are things done here? 
  • Knowledge. 
    What does a researcher know about storing, managing, archiving and sharing research data? What do you know about this, and how can your knowledge complement that of the researcher, and vice versa? 
  • Skills.
    For example, conversational and influence skills. How do you communicate what you know?
  • Motivation.
    What motivates a researcher? What makes him tick? What makes you tick? Why are you a data supporter?

 The below diagram offers a model of the ways these influences can interact.

Interventions on one level will influence what happens on another. If, for example, an accessible, user-friendly way to share research data is developed within a discipline, the culture within a group may change (see the Cases below).

In the Cases section, a number of researchers will offer their views. Sharing positive user experiences can also lead to a culture change.

    Case culture (change)

"All too many observations lie isolated and forgotten on personal hard drives and CDs, trapped by technical, legal and cultural barriers." - Bryn Nelson(3)

The earth sciences (from geology to climatology) already have a culture that greatly appreciates sharing data. The social sciences and humanities offer a very varied picture. For an impression of the situation across many disciplines, see  ‘The Dutch data landscape in 32 interviews and a survey’ [pdf].(4) 

Within a domain, a situation may arise that encourages researchers to mainly store their data in local research centres. This used to be the case within linguistics and text linguistics. Growing visibility and activities of the CLARIN (5) and DARIAH(6)  infrastructures has increased awareness within the field of the availability of a good data infrastructure. The NWO investment grant for Nederlab(7) represents an important support for further developments in the fields of Dutch linguistics and text linguistics.

    Case influencing politics

Horizon 2020(8) is the current European funding programme for research and innovation. The TU Delft, Leiden University, Erasmus University Rotterdam, University of Twente and TU Eindhoven wrote a 'Position paper on open access to research data in Horizon 2020' [pdf](9) to influence political decision-making concerning opening research data.

The European Commission has chosen her position (cf. the 'Factsheet Open Access in Horizon 2020' [pdf](10)) and founded an Open Research Data Pilot(11):

"Projects participating in the Pilot will be required to deposit the research data described above, preferably in a research data repository and as far as possible, take measures to enable third parties to access, mine, exploit, reproduce and disseminate this research data. At the same time, projects should provide information about tools and instruments at the disposal of the beneficiaries and necessary for validating the results, for instance specialised software or software code".

See also 'Guidelines on data management in Horizon 2020' [pdf](12) and the section Datamanementplanning(13).


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  1. League of European Research Universities. (2013). LERU Roadmap for Research Data [Advice Paper]. Retrieved from
  2. Research Information and Digital Literacies Coalition RIDLS. (2013, July). Helping to open up: improving knowledge, capability and confidence in making research data more open. Retrieved from 
  3. Nelson, B. (2009, September 9). Data sharing: empty archives. [news]. Nature 461, 160-163. Retrieved from
  4. DANS. (2011). The Dutch data landscape in 32 interviews and a survey. Retrieved from 
  5. CLARIN. Retrieved from
  6. DARIAH. Retrieved from
  7. Nederlab. Retrieved from
  8. Horizon 2020. Retrieved from
  9. TUDelft; EUR; Universiteit Twente; TU/e; Universiteit Leiden. (2013). Position Paper on Open Access of Scientific Data in Horizon 2020. Retrieved from
  10. European Commission. (2013, December 9). Fact sheet: Open Access in Horizon 2020. Retrieved from
  11. European Commission. (2013, December 16). Commision launches pilot to open up publicly funded research data. [press release]. Retrieved from
  12. European Commission. (2013, December 11). Guidelines on Data Management in Horizon 2020. Retrieved from
  13. RDNL. Essentials 4 Data Support. Paragraph Datamanagementplanning. Retrieved from