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, 2013
Data support is designed at various business units and at different levels. Top-down and bottom-up. To work as effectively as possible, it is useful to gain insight into the various interests, roles and responsibilities within and outside your organisation. In this section we outline the force field and invite you to think about the opportunities that exist to shape data support.
The previous chapters have made it clear that there are quite a few players and forces that you may encounter as a data supporter.
A data supporter works in a force field of:
- Laws and policy
What laws and policies do you have to deal with? Organisation-wide, national, European?
- Technical infrastructure
What tools are available? Are they accessible and user-friendly to work with? Do the tools fit in with the researcher's workflow?
Who are involved in RDM? Think of research funders, journals, data archives, etc. And who are your fellow data supporters? Think of the privacy officer, fellow data stewards, the medical ethics review committee, data protection officer, lawyers, policy makers, information security experts, data managers, etc, both in the back office and front office.
What are the prevailing research practices? How do we 'do things' over here?
What does a researcher know about storing, managing, archiving and sharing research data? What do you know and how can your knowledge complement that of the researcher and vice versa?
- Conversational skills
How do you convey what you know? Are you able to find out the question behind the question? How good are your conversational and influencing skills?
What drives a researcher? What makes him or her tick? And what makes you tick? Why are you a data supporter?
In the diagram below you can see a model of the ways in which a number of the above mentioned forces influence each other (RIDLS, 2013).
Influencing the force field
Interventions at one level affect other levels. For example, if an accessible, user-friendly way of sharing research data becomes available within a discipline, this can change the culture within a group (in the long term). Usually, however, it is not possible to draw these kinds of conclusions on a 1:1 basis and there is a 'concurrence of circumstances'. For example, research into the motivation for open sharing of research data shows that there are many visible and invisible dependencies that determine whether researchers change their behaviour (Zuiderwijk & Spiers, 2019). The video below will give you more insight into these dependencies.
Motivations for openly sharing research data and for re-using open research data are interrelated to each other in a complex manner. For instance, if researchers are demotivated because it takes great effort to interpret a particular open dataset, they may still be motivated to re-use that particular dataset if it is strongly relevant to their research. Thus, the different factors have different weights and their combinations need to be considered rather than looking at individual motivation categories in a ‘stand-alone fashion’ | Zuiderwijk & Spiers, 2019
In the spotlight
Case on influencing politics
Horizon 2020 is the current European funding programme for research and innovation. 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' (Delft University of Technology, 2013) to influence the political decision making surrounding the opening up of research data. It was successful.
The European Commission has set the course and set up an Open Research Data Pilot (European Commission, 2013) :
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
Also see the 'Guidelines on data management in Horizon 2020' (European Commission, 2016) and the section on Data management planing.
Case on culture change
A situation may arise within a domain that encourages researchers to store their data mainly in local research centres. This was also the case in the linguistics and text sciences. The increasing visibility and activities of the CLARIN (n.d.) and DARIAH (n.d.) infrastructures led to a rapid increase in the awareness of the existence of a good data infrastructure. The NWO investment subsidy for Nederlab(n.d.) and CLARIAH (n.d.) was an important driver for the further development of the humanities in the Netherlands.
Case on influencing the evaluation of research and researchers
In order to ensure that the use of research data (qualitative RDM) is included in the evaluation of Dutch research and researchers, the LCRDM working group 'Awareness and Commitment' wrote a memorandum on data management evaluation in 2017 (LCRDM, 2017 (in Dutch)). The document served as input for a working group reviewing the Netherlands Standard Evaluation Protocol (SEP, VSNU, 2016).
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Andrews, Heather, Beckles, Zosia, Dunning, Alastair, Jans, Jan, Kraaikamp, Emilie, Kurapati, Shalini, von Stein, Ilona. (2018, September). GDPR in research - what does it mean for research institutions?. Zenodo. http://doi.org/10.5281/zenodo.1408108
CLARIN (n.d.). https://www.clarin.eu/
CLARIAH (n.d.). https://www.clariah.nl/en/
DARIAH (n.d.). http://www.dariah.eu/
Delft University of Technology, Erasmus University, Leiden University, Twente University, Eindhoven University of Technology (2013). Position Paper on Open Access of Scientific Data in Horizon 2020. https://weblog.library.tudelft.nl/wp-content/uploads/2013/11/Position-Paper-on-Open-Access-to-Scientific-Data-in-Horizon-2020.pdf
European Commission (2013). Horizon 2020. Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020. https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf
European Commission. (2016). H2020 Programme. Guidelines on Data Management in Horizon 2020. http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf
LCRDM (2017). Conceptnota RDM als onderdeel van het SEP. https://www.lcrdm.nl/files/lcrdm/2019-04/Conceptnota%20RDM%20in%20het%20SEP_v20170914.pdf & https://www.edugroepen.nl/sites/RDM_platform/Bewustwording/Incentives,%20notitie%20RDM%20in%20het%20SEP.aspx
LERU. (2013). LERU Roadmap for Research Data [Advice Paper]. Retrieved from https://www.leru.org/publications/leru-roadmap-for-research-data#
Nederlab (n.d.) Nederlab. https://www.nederlab.nl/onderzoeksportaal/?action=verkennen
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 http://www.informall.org.uk/wp-content/uploads/2015/11/CC536D001-1-2-training-for-making-research-data-more-open-FINAL-VERSION.pdf
VSNU (2016). Standard Evaluation Protocol 2015 – 2021 Protocol for Research Assessments in the Netherlands Amended version, 2016. http://www.vsnu.nl/files/documenten/Domeinen/Onderzoek/SEP2015-2021.pdf
Zuiderwijk, A., Spiers, H. (2019). Sharing and re-using open data: A case study of motivations in astrophysics. International Journal of Information Management. Volume 49, pages 228-241. https://doi.org/10.1016/j.ijinfomgt.2019.05.024