ODISSEI FAIR Support
The FAIR principles were first introduced in 2016 and have since then become a golden standard in the research data world. FAIR stands for Findable, Accessible, Interoperable and Reusable. The principles describe ways in which data and (by extension) other research output should be described, documented and stored to allow the information to be easily accessible and re-usable to others – humans and machines.
FAIR has a close relationship with the Open Science movement which also focuses on making science reproducible and data reusable for others. There are several Open Science communities in the Netherlands that researchers can join. It is important to realise that data can be FAIR and follow Open Science principles without being openly available for everyone. Documenting and storing data well and making it FAIR is important even if data cannot be shared openly, for instance if it contains sensitive personal information.
ODISSEI has established the ODISSEI user policy to help ODISSEI users to make their data FAIR and to provide information on the steps ODISSEI users should take. This webpage will continuously be updated with information, resources, and tutorials, to guide researchers and data supporters in the implementation of the ODISSEI user policy and FAIR principles.
Make your data FAIR
The best way to make your data FAIR, is to dive into good Research Data Management (RDM) practices.
Managing your data well at each step of the research process will make it easier to make your data FAIR at the end of your project. An excellent guide on RDM and FAIR data is provided with the Data Management Expert Guide (DMEG) from CESSDA (Consortium of European Social Science Data Archives).
The DMEG gives a detailed explanation of the data management steps that you can take in your project to make your data FAIR. It follows the Research Data Life Cycle and explains the different aspects that you can consider while planning and executing your research. It provides links to various tools and other resources available that you may want to consult, including Data Management planning and data archiving considerations.
As you can see in the DMEG, storing data in a Trustworthy Digital Repository (TDR) takes care of a lot of the aspects of FAIR. In the Netherlands, the TDR for social science data is hosted by DANS, the Dutch national centre of expertise and repository for research data.
Next to the DMEG, there are many tools available that can help you increase the FAIRness of your dataset. A nice tool to test your knowledge and see how the FAIRness of your data can be improved is FAIRAware Tool.
The importance of data supporters
Many ODISSEI member organisations have dedicated data stewards who work at the universities to support researchers with RDM and FAIR data recommendations. This overview provides a list of the data stewards known to us at the various ODISSEI member organisations. Data supporters can be part of local Digital Competence Centres (DCCs).
An interesting group for data stewards to come together and exchange is the Data Steward Interest Group (DISG) initiated by members of Leiden UMC, UMC Utrecht, and Dutch Techcentre for Life Sciences (DTL). This group supports data stewards in their work and provides a platform to exchange materials and guidance as well as discuss new developments. On a national level, the LCRDM is active as a data support collective and RDNL provides training for research data support professionals.
Training materials for research supporters
Research supporters can find various training materials online that can be re-used for training or advice for the local researchers. RDNL’s Essentials 4 Data Support is a well-known course that many data stewards have followed.
The DMEG has dedicated train-the-trainer materials which can be downloaded here. The FAIR Aware Tool also includes options to use in training. More training materials on a variety of topics can be found through the SSH Training Discovery Toolkit.
ODISSEI FAIR support
The ODISSEI FAIR support team is here to help the ODISSEI community with questions around RDM, Open Science and FAIR data. We also regularly publish blog posts to explain some of the concepts and resources that are relevant in the Open Science, FAIR and RDM landscape. You find the list here:
- Controlled vocabularies for the social sciences: what they are, and why we need them (published on 03-10-2022)
- I love PIDs – and so should you! (published on 10-11-2022)
- FAIR yes, but how? FAIR Implementation Profiles in the Social Sciences (published on 14-12-2022)
- FAIRsharing for the social sciences: What’s in it for me? (published on 6-2-2023)
- Find, Use, Cite, Repeat: A short guide on how to cite microdata from Statistics Netherlands (published on 20-3-2023)
- Collecting vocabularies in the social sciences: the Awesome Ontologies for the Social Sciences (published on 25-4-2023)
- FAIR Software for the Social Sciences (published on 9-6-2023)
- Dare to share! How the DANS Data Station SSH supports FAIR data in the ODISSEI Community (published on 22-9-2023)
Implementing FAIR within your own project may be challenging and you can get in touch with us if you have any questions. Experts from ODISSEI and DANS will look at your situation together and provide advice.
You can get in touch with us via email: firstname.lastname@example.org. If you would like to ask your question live, you can also visit the SSH Open Hour from DANS every Monday at 10:00 (Amsterdam time).
Latest posts on FAIR & RDM
Icons used in the infographic: Database by amy morgan from NounProject.com; Software by M. Oki Orlando from NounProject.com; Presentation by James from NounProject.com; Paper by Aidan Cooke from NounProject.com; Arrow by iconcheese from NounProject.com;