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Data policy

We find that, following mandated publisher policies, data availability statements have become common by now, yet statements containing a link to a repository are still just a fraction of the total | Colavizza, 2019

By data policy we mean all the procedures and guidelines that have been developed within an institution or partnership for data management, archiving and the sharing of research data. Different stakeholders - such as publishers, research funders and research institutions - all formulate their own data policy. In this section you will find a number of examples.

Examples of policies

Data policies are becoming increasingly common. Different stakeholders in the scientific process have formulated data policies. In the accordion below you will find a number of examples.


Examples of institutional policies

  • The policy of Dutch universities, universities of applied sciences and research institutes can be found at this LCRDM page (LCRDM, n.d.). Click on the institution of your choice and a new page with information will open. An example is the policy of Saxion Universities of Applied Sciences:
    • Saxion Guidelines for Research Data Management (Saxion, 2018a).
    • The Saxion Data Reuse Protocol deals specifically with how researchers at Saxion can open up their research data to others and also deals with FAIR data. (Saxion, 2018b).
  • A list of institutional data policies at institutions in the UK (DCC, n.d.).


Examples of the data availability policies of a number of journals/publishers:


For examples of the data management policy of research funders, see the paragraph 'Datamanagementplan'

Strong similarities between data policy documents exist (Neylon, 2017):

  • The requirement of a data management plan (DMP), often when submitting grant proposals;
  • The expectation that research data in support of the conclusions of a scientific article will be made available in published scientific articles;
  • The expectation that data will be available for the long term.

The impact of data policy

On the way to FAIR data, it is of great importance to continue to investigate whether the measures taken - such as having data policies - have the desired effects. An example of such research is given below. 

Since March 2014, PLOS journals have been among the first to require that all entries contain a data availability statement describing how readers can access "all data and related metadata underlying the findings reported in a submitted manuscript". A reader should be able to use the data to reproduce and confirm the findings of an article, or to reuse the data for new research. But how effective has the policy been? Research into 4.500 data availability statements published in the first 28 months after the policy came into effect (Federer 2018a & 2018b) shows that:


  • 18% of the data availability statements referred to data in a data archive;
  • 70% of the data availability statements referred to supplementary material or to data in the article itself.


The authors conclude that a data availability policy such as that of PLOS is a good first step towards more openness and availability of research data, but it's not enough. More is needed. The authors have two suggestions:

  • PLOS and other journals with similar policies may consider including data availability in the peer review process;
  • Data archives can play a role in making datasets easier to find and making it easier for authors to write a data availability statement, for example by offering a template for it. 

Our survey suggests that strong encouragement from institutions, journals, and funders will be particularly effective in overcoming barriers for data sharing, in combination with educational materials that demonstrate where and how data can be shared effectively |  Houtkoop, 2018

In the spotlight

RDA interest group 'Data policy standardisation and implementation'

The interest group 'Data policy standardisation and implementation' of the Research Data Alliance focuses on the data policy landscape (RDA, n.d.). The implementation and implications of data policy may be unclear to researchers and the working group wants to clarify things. 


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AER (n.d.). Data and Code Availability Policy.

DCC (n.d.). UK Institutional data policies.

Federer, L.M., Belter, C.W., Joubert, D.J., Livinski, A., Lu, Y-L., Snyders, L.N., et al. (2018a) Data sharing in PLOS ONE: An analysis of Data Availability Statements. PLoS ONE 13(5): e0194768. 

Federer. L.M. (2018b, June 14th). Journal data sharing policies are moving the scientific community towards greater openness but clearly more work remains. LSE Impact Blog [Blog]. 

Houtkoop, B. L., Chambers, C., Macleod, M., Bishop, D. V. M., Nichols, T. E., & Wagenmakers, E.-J. (2018). Data Sharing in Psychology: A Survey on Barriers and Preconditions. Advances in Methods and Practices in Psychological Science, 1(1), 70–85.

LCRDM (n.d.). RDM in the Netherlands.

Nature (n.d.). Reporting standards and availability of data, materials, code and protocols. Retrieved from

Neylon, C (2017) Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research. Research Ideas and Outcomes 3: e21773.

PLOS ONE (n.d.). Data availability.

RDA (n.d.). Data policy standardisation and implementation IG.

Saxion (2018a). Saxion Research Services. Saxion Guidelines for Research Data Management.

Saxion (2018b). Saxion Research Services. Saxion Data Reuse Protocol.

Wicherts (2019). The citation advantage of linking publications to research data.