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Proper research data management is very important to SDU, which is why it is highlighted in the SDU Open Science Policy (internal link to RDM Open Science Policy page). The following are some of the components necessary for solid data management practices. Visit the linked pages for detailed information that will help you keep your data well-organized.


  1. Data Management Plan (DMP)
  2. Use descriptive and informative file names.
  3. Choose file formats that will ensure long-term access.
  4. Track different versions of your documents.
  5. Create metadata for every experiment or analysis you run.
  6. Find helpful tools for analyzing your data.
  7. Handle sensitive data in an appropriate manner.

Watch this 20 minute e-Learning video for more about the importance of good Research Data Management (in English with subtitles). 



This video e-Learning module is the result of a collaboration of the Danish universities, Rigsarkivet and the Danish e-Infrastructure Centre (DeiC). Holmstrand, K.F., den Boer, S.P.A., Vlachos, E., Martínez-Lavanchy, P.M., Hansen, K.K. (Eds.) (2019).  Research Data Management (e-Learning course). DOI:10.11581/dtu:00000047

For more information on managing data, check out the article "Nine simple ways to make it easier to (re)use your data" by White, et al. or the book: Data Management for Researchers by Kristin Briney.


Long-term planning

Document your plan
Once you have started to implement best practices for yourself and your research group, make an effort to document these plans. Include your and your group's procedures for the following:

  • Naming files
  • Saving and backing up files
  • Describing data files
  • Tracking versions

You might consider using a OneDrive or Google doc that everyone in your group can access when needed. Be sure to define who is responsible for each task and for setting the overall policies.

Plan for knowledge transfer

As a last step, don't forget to create and implement a plan for how to transfer knowledge about a project when it changes hands or when someone leaves the group. This will help prevent valuable information from getting lost!

This text is adapted from the Stanford University Library Data Management Services website

Research data alliance (RDA)

What is the RDA?
The Research Data Alliance (RDA) is a grassroots initiative supported financially by the EU, US and Australia with the aim of building the social and technical bridges for sharing and reusing research data - specifically in the form of working groups where researchers and professionals develop, for example, technical standards, guides and domain specific best practices in research data management (RDA outputs).

The Research Data Alliance (RDA) was launched as a community-driven initiative in 2013 by the European Commission, the United States Government's National Science Foundation and National Institute of Standards and Technology, and the Australian Government’s Department of Innovation with the goal of building the social and technical infrastructure to enable open sharing and re-use of data.

The RDA community includes researchers, scientists, IT specialists, data professionals, and other experts. All data lifecycle stages are covered - topics include i.a. data publication, data citation, data management planning, data processing, data interoperability. 

What can the RDA do for you?

RDA recommendations (formally endorsed) enable data sharing, exchange and interoperability - they include i.a. specifications, taxonomies, ontologies, workflows, data models. RDA recommendations and outputs contribute to tackle real-world challenges.

To get an understanding of how recommendations are applied by different disciplines such as climate research and agricultural sciences, read more here.


The Danish RDA node

Join the Danish RDA community hereMembership is free!



Frequently asked questions

Open Access (OA) refers to free, unrestricted online access to research outputs such as journal articles and books. OA content is open to all, with no access fees.

What is open access? Nick Shockey and Jonathan Eisen of PhD comics take us through the world of open access publishing and explain just what it's all about.

There are many types of OA, but the two main routes to making research outputs openly accessible are “Gold Open Access” and “Green Open Access”.

Gold OA involves publishing articles or books via the OA route on a publisher’s platform.

Green OA involves archiving a version of the manuscript in an OA repository, like SDU Pure.

Content published via the Gold OA route is accessible immediately on publication at the publisher’s website, but may come with a hefty fee. Manuscripts published via the Green OA route may, in many cases, be made accessible only once a self-archiving embargo period has elapsed. The terms for onward sharing and re-use of OA content will depend on the licence under which it has been made available. (Adapted from SpringerNature)

You can apply for SDU library to pay the cost of publishing OA in non-hybrid journals through the OA fund here. Please note, funds are limited and distributed on a first come first serve basis.

To learn more about publishing OA, such as “How to find a suitable OA journal or repository for your publications” visit this OpenAIRE web-guide.

FAIR research data is data that has been prepared in accordance with the FAIR guiding principles published in 2016. These principles contain good data management practices that aim at making data FAIR: findable, accessible, interoperable, and reusable.

"Data" refers in this context to all kinds of digital objects that are produced in research: research data in the strictest sense, code, software, presentations, etc. However, for the sake of simplicity, we use "data" and not "digital research objects" on this website.

FAIR research data can improve the reproducibility of published research and ensure that research data are used to their full potential to the benefit of society. 

What is fair?

To learn more about FAIR research data, watch this 20 minute video e-Learning module that aims to help researchers understand:
1. The key elements that help make research data discoverable, accessible, interoperable and reusable
2. How these key elements are used in different research disciplines and different research workflows
3. The differences between FAIR data and open data

This video e-Learning module is the result of a collaboration of the Danish universities, Rigsarkivet and the Danish e-Infrastructure Centre (DeiC). Holmstrand, K.F., den Boer, S.P.A., Vlachos, E., Martínez-Lavanchy, P.M., Hansen, K.K. (Eds.) (2019). Research Data Management (e-Learning course). DOI:10.11581/dtu:00000047 

How can you make your research data more FAIR?

The website  will take you on a deep dive into the subject matter of FAIR research data. Over the course of about two hours, it will show you that FAIR is not a time-consuming administrative mantra, but a set of principles that makes your research efficient, transparent and sustainable. Working in line with the FAIR principles to make your data more FAIR will improve your research data management and safeguard your research data for the future.

This website is the result of a collaboration of the Danish universities, Rigsarkivet and the Danish e-Infrastructure Centre (DeiC). D.B. Deutz, M.C.H. Buss, J. S. Hansen, K. K. Hansen, K.G. Kjelmann, A.V. Larsen, E. Vlachos, K.F. Holmstrand (2020). How to FAIR: a Danish website to guide researchers on making research data more FAIR. DOI:



Last Updated 01.06.2021