A Research Data Policy at Wageningen University & Research: Best Practices as a Framework

 
 

This essay presents an article entitled : "The Development of a Research Data Policy at Wageningen University & Research: Best Practices as a Framework"* - published recently in the LIBER Quarterly. The article describes the development of a Research Data Management policy at Wageningen University & Research, the Netherlands. 

To develop this policy, an analysis was carried out of existing frameworks and principles on Data Management (such as the FAIR principles), as well as of the Data Management practices in the organisation defined through interviews with research groups.  

PREAMBLE :

From insight into Data Management practices and needs to new Policy Guidelines  

Many funders ...

... encourage or demand researchers to critically plan their Research Data Management (RDM) at the start of a project, and to retain and possibly publish datasets once the research has been completed (e.g., European Commission, 2016).

Publishers...

... too, are showing awareness of the value of data, asking or sometimes requiring authors to share the datasets underlying their publications (e.g., PLOS, 2017).

Researchers...

... recognize the value of RDM too, since it brings incentives of various kinds, such as efficiency of research, more societal impact, and increased chances of getting funding (Hoetink, Broekhoven, & van den Hoogen, 2016).

Overall...

... the scholarly landscape reflects a growing concern with making data FAIR: Findable, Accessible, Interoperable, and Reusable (Wilkinson et al., 2016).

To ensure that they meet the above-discussed requirements, and that their research data assets are safely guarded and fully exploited, more and more universities are implementing Research Data Management policies, - see, e.g., ANDS project registry - Overviews of Data Management Resources

To provide policy makers with a starting point, several templates have been developed, that cover a set of important research data themes, such as data retention, ownership, and the responsibilities of stakeholders. Institutions can select and adapt these themes to suit their own context, - see, e.g., LEARN (2017) Toolkit of Best Practice for Research Data Management.

Context-specific information such as available storage and archiving services need to be taken into account, together with the actual data management needs of researchers, - see, e.g., LEARN (2017). SURVEY: Is your institution ready for managing research data? 

These resources help to identify which services are in place and which ones might need further development.

The RDM policy at Wageningen University & Research

was also set up with the help of approach chosen not only to gain understanding of the existing data management practices, but mainly to identify certain ‘best-practices’ . Such use cases that can be considered exemplary in how researchers manage their research data are highly informative in the definition of a RDM policy, for two reasons:

(1) they outline how data should be managed, thus providing a basis for policy guidelines, and

(2) they relate to the data management workflows of researchers, thereby providing concrete examples of how the established policy guidelines might work in practice.

The overviewed criteria - that should be met in terms of safety, accessibility, and findability - were then translated into guidelines for an RDM policy

Defining the Criteria for Data Management

By defining the RDM policy, it was decided that RDM criteria should: 

  1. Follow existing laws, guidelines and frameworks that apply to research data from Wageningen University & Research.
  2. Reflect the diversity of Wageningen University & Research as an organisation, in terms of the characteristics of its research data.

The entire project was framed around the two phases:

PHASE 1 : during research and 

PHASE 2 : after research, with data storage taking place during, and data archiving and registration after research.

MOSCOW-method :  (1) Must-have, (2) Should-have, (3) Could-have, or (4) Won’t-have

Once all criteria were established, the MOSCOW-method was used. In particular, all criteria were categorised as one of these four. The outcome of this was an overview of which criteria were considered crucial to follow in data storage, archiving and registration (Must-haves), and which were not.

Criteria were mostly marked as Must-haves when they followed the law or the frameworks used (e.g. a criteria key to making data Findable according to the FAIR principles), although in some cases features were marked as Must-have based on the experience of Data Management Support staff. 

Thanks to decision made to take the institutionally available storage and archiving solutions as a starting point, the RDM guidelines could therefore be set up quickly, as the infrastructure was in place.

Simultaneously, the interviews indicated for which data types the existing solutions were not appropriate (secret and very large datasets). Once identified and implemented, these solutions will be added to the guidelines.

FULL ARTICLE is cited as follows:

* van Zeeland, H. & Ringersma, J., (2017). The development of a research data policy at Wageningen University & Research: best practices as a framework. LIBER Quarterly. 27(1), pp.153–170. DOI: http://doi.org/10.18352/lq.10215

Wageningen University & Research (WUR) is an institution that constitutes a University and various National Research Institutes in the domains of natural sciences and life sciences. Wageningen University & Research was the first university in the Netherlands to introduce a data policy in 2014.

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