FAIR research data

Norsk versjon - FAIR forskningsdata

Topic page about research data | Pages labeled with Open Data

On this page you will learn what FAIR data is and how to make your research data FAIR.

What is FAIR data?

The international FAIR principles are a set of overarching guidelines for preparing research data for further use by both humans and machines. FAIR stands for findable, accessible, interoperable and reusable.

In other words, FAIR data is organized in a way that makes it possible to find it, and where the use of format and additional information makes it possible to understand and reuse it. Remember that in very few cases will data be completely FAIR. The purpose of working according to the FAIR principles is to increase the potential for reuse, and in many cases even small steps can significantly increase the degree of FAIRness.

FAIR data involves, among other things, the use of:

In addition to the basic principles of Findable, Accessible, Interoperable, and Reusable (FAIR), FAIR also includes the requirement that data and metadata should be designed in a way that makes them easy for machines to understand and handle. Ideally, machines should be able to discover and utilize this data without the need for manual facilitation.

It is important to think FAIR even if the data material in question is not to be shared openly - data should still be FAIR during the project for everyone who will have access, including yourself. Use the data management plan actively to plan for FAIR data. Here you can describe how the files will be organized, which formats will be used and how documentation will be created and preserved. By choosing an archive well in advance, you can also plan your data management in light of the criteria of the archive in question.

FAIR during the project

While working on the project, it is important to think about organization and overview.

Version control
New versions of data files are often created, and it is recommended that each new version is given a unique name. A common way to do this is to number the versions, for example "v1", "v2", or "v2.1".

Naming of files
To keep track, you should also pay attention to the naming and dating of files. Use a date format such as YYYYYMMDD, create short and explanatory file names, avoid special characters and use underscores instead of spaces.

Documentation and ReadMe file
Keep all documentation that can be useful for understanding the dataset and create a ReadMe file to accompany the files.

Other types of results and resources can and should also be made as FAIR as possible, such as source code, software, models, protocols and learning resources.

FAIR when archiving and publishing data

Although it is important to work to make research data FAIR throughout the entire research data management process, it is particularly important to think FAIR when making data available, for example when publishing an article. Often, many of the basic FAIR principles are met by choosing a certified repository for research data when making the dataset available.

A good archive will, among other things, make the dataset searchable in search engines for research data, provide the dataset with a DOI or other identifier, and often have some options for licenses.

It is important to consider file formats and the use of open software rather than manufacturer-owned (proprietary) software. When archiving datasets, the document should therefore often be saved in a different format than the one used during the process, for example plain text rather than Microsoft Word, and CSV rather than Microsoft Excel.

NTNU's research data archive in DataverseNO is an example of an archive that provides a high degree of FAIRness by, among other things, providing DOI, having standardized metadata and using open formats.