FAIR Across Disciplines

This project works with the implementation and application of the FAIR principles across science, research disciplines and data types

F (Findable) - A (Accessible) - I (Interoperable) - R (Reusable)

The "FAIR Across Disciplines" activities create value by contributing to a common understanding of the FAIR principles and the challenges and opportunities related to FAIR in concrete research contexts, thus forming the basis for future initiatives, policies and requirements. The project runs until the end of 2018.

"FAIR Across Disciplines" Conference, November 20th 2018

The project hosted a final conference on November 20th. Below are the presentations from the day:

Project Participants

Participants are from Aalborg University, Copenhagen University, the Royal Library, Technical University of Denmark, Copenhagen Business School and the National Archives. The National Archives is project manager.​

Project cases

"FAIR across" contributes to a common understanding of the FAIR principles through a number of data pilots/cases (active research projects and research data services). The challenges of complying with the principles are covered and suggestions are provided for how pilots can work further towards FAIR data. Links to the descriptions of some of the data pilots/cases are found below.

The project's deliveries of graphic nature

Some of the project's deliveries will be published/available here; posters and other material related to the specific use of the FAIR.


The poster to "The path to a successful research project - how to get on the right track with FAIR data management" is available in digital version here og in print version here.


The PowerPoint presentation to "The path to a successful research project - how to get on the right track with FAIR data management" is available here.


"The path to a successful research project - how to get on the right track with FAIR data management":

"Debunking FAIR myths":


The book "A FAIRy tale - A fake story in a trustworthy guide to the FAIR principles for research data" will be published soon. The front page can be found here.

The FAIR toolbox

This overview guides you on finding the right tools for making your research data (more) FAIR – depending on what data you have and what you want to do with these data. The presentation can be found here.

Create: Collect or generate new data from scratch, e.g. through measurements or surveys.

Process/analyze: Use data for any kind of research activities, including digitization, conversion and interpretation.

Document: Add context to the data, including provenance and metadata.

Recycle: Use previous output as input for new analysis or interpretation, also in collaborations.

Publish/disseminate: Make selected datasets available for other researchers or the general public.

Archive: Deposit selected datasets in systems suitable for long-term preservation.

Exploit: Perform research directly on previously published data.

Discover & re-use: Use previously published data for new research, e.g. from public databases and repositories.

Release: Provide access to raw data for others to use. preserve:

Retain: Raw data on long-term storage.

Discard: Destroy or delete any data – due to legal or contractual obligations, for example. This is not a FAIR process and is therefore not considered further.

FAIRification services

Services/tools facilitating FAIR data, including  including information resources with information about FAIR and sharing tools facilitating data sharing, can be found here.

List of FAIRification services: