Make your data fair

Why & how to make your data FAIR

Making your data Findable, Accessible, Interoperable, and Reusable (FAIR) can help you enhance the impact of your research.

What are the FAIR principles?
The FAIR principles were formulated in 2014 to guide data producers and publishers on how to increase the findability, accessibility, interoperability and reusability of their data. The goal is to ensure that scholarly data can be used as widely as possible – accelerating scientific discoveries and benefiting society in the process.

The FAIR principles were rapidly adopted by Dutch and European funding agencies. If you receive a research grant from NWO, ZonMw, or the European Commission, you will be asked to make your data FAIR.

How can you benefit from adhering to the FAIR principles?
You do not need to adhere to the FAIR principles in their entirety to start benefiting from making your data FAIR. Applying even just some of the principles, will increase the visibility and impact of your data, leading to:

•    Increased citations of the datasets themselves and your research.
•    Improved reproducibility of your research.
•    Compliance with funder and publisher requirements.

Making your data FAIR will also make it possible for you to easily find, access and reuse your own data in the future. You may be the first and most important beneficiary of making your own data FAIR. As said elsewhere: “As a scientist, you should treat your data like a love letter to your future self.”

Making data FAIR – how to get started in three easy steps?
1.    Start with a data management plan

A DMP is a living document in which you specify what kinds of data you will use in your project, and how you will process, store and archive them. Preparing a data management plan should be your first step in the process to make data FAIR. It is also a requirement from funding agencies and some faculties at the VU. At the VU, you can make use of DMPonline to create and share a DMP.

2.    Describe and document your data

To be findable, data need to be described with appropriate metadata. Metadata can include keywords, references to related papers, the researchers’ ORCID identifiers, and the codes for the grants that supported the research.

To be reusable, data need to be accompanied by documentation describing how the data was created, structured, processed, etc.

If you have questions about metadata and documentation, contact the RDM Support Desk and we will be happy to help you and to provide advice.

3.    Make your data available through a trustworthy repository

If you choose a repository that: assigns a persistent identifier to both the data and the metadata; attaches metadata to the data according to standard metadata schemas; releases data with a license; and provides access to the data and metadata via an open and standard communication protocol (such as http) – then your data will meet many, if not most, of the FAIR principles. The VU provides DataverseNL, which meets all of these conditions. Read how Dataverse makes it easier for VU Assistant Professor Sander Groffen to store, archive and share his data. Researchers at the VU can make use of Dataverse for datasets up to 50 GB in size at no cost to themselves.

What if I cannot share my data?
Data do not need to be open to be FAIR. The FAIR principles allow for controlled access, which can be important for certain types of data, such as medical data. The guiding principle is always that data should be as “as open as possible, as closed as necessary”. If data cannot be openly shared, because they are too sensitive, then “the FAIR approach would be to make the metadata publicly available and provide information about the conditions for accessing the data itself.”

Further resources
PARTHENOS Guidelines to FAIRify data management and make data reusable

F1000 guide to making your data Findable, Accessible, Interoperable, and Reusable (FAIR)

The FAIR Principles explained by GO FAIR