Module 3 - FAIR data principles and their main elements#

In this module, we will discover the FAIR data principles and their main elements.

At the end of this module you should be able to:

  • Understand the FAIR principles and their relation to RDM

  • Identify key elements that help make research data Findable, Accessible, Interoperable and Reusable (FAIR)

There are different activities in this module you should complete

There are different activities in this module you should complete:

✅ Read topic 1: Why and how were the FAIR principles created?
✅ Watch the 📽️ video about the key elements to make data FAIR and complete the ‘fill in the blanks’ quiz

3.1 FAIR data principles#

Why and How Were the FAIR Principles Created?#

The FAIR principles were created in order to maximise the reuse of scientific data, to promote best practices on Research Data Management and to enable Open Science.

Applying the FAIR principles means to make research data Findable, Accessible, Interoperable and Re-usable.

  • Findable means that others (both human and machines) can discover the data

  • Accessible means that the data can be made available to others

  • Interoperable means that the data can be integrated with other data and can be easily used by machines or in data analysis workflows.

  • Reusable means that the data can be used for new research

These four principles should be applied (as much as possible) throughout the entire research cycle and they are closely interconnected with each other.

The FAIR Data principles are NOT:

  • A standard. The FAIR principles need to be adapted and followed as much as possible by considering the research practices in your field. The FAIR principles should be rather seen as progressive steps that help you make your data re-usable.

  • Equivalent to Open Data. FAIR data does not necessarily mean openly available: it should be clear to others that the data exists and which steps they could take to potentially access the data.

  • Applied using a particular technology or tool. There might be different tools that enable FAIR data within different disciplines or research workflows.

There are important elements to consider within your research workflows if you aim to make the data of your project FAIR:

  • Documentation & Metadata

  • Interoperability

  • Access to data

  • Persistent identifiers

  • Licences

In the next 📽️ videos we will explore these different elements and their importance to make the data of your project FAIR.

📽️ Videos: Key elements to make data FAIR#

What are the key elements to make data FAIR? In the following videos we will explain these elements and give you the opportunity to test your knowledge.

The objective of the following videos is to identify the key elements that help make research data Findable, Accessible, Interoperable and Reusable (FAIR). The following videos are an elaboration on these key elements of FAIR. Test your knowledge afterwards with the ‘Fill in the missing words’ assignment beneath.

Documentation and Metadata#

Test your knowledge!

Interoperability#

Test your knowledge!

Access to Data#

Test your knowledge!

Persistent Identifiers#

Test your knowledge!

Data and Code Licences#

Test your knowledge!

Summary#

By now, you have learned about different elements that help you make the data of your project FAIR. Many of them are straightforward to implement in your daily work, for example organising your files and folders, keeping documentation of the data, reflecting about data access, etc. Other elements might require special tools or at least using special tools would make your work easier. We will talk about some tools for documentation and data publication in Module 4.

Additional Resource: How do the FAIR principles apply to software?#

If Research Software is one of the main outputs of your projects, we strongly recommend you to take a couple of extra minutes exploring this section!

To improve the sharing and reuse of research software, more than 500 contributors around the globe worked for two years in the creation of the ‘FAIR Principles for Research Software’ (FAIR4RS). Many of the FAIR Guiding Principles for research data can be directly applied to research software by treating software and data as similar digital research objects. However, specific characteristics of software — such as its executability, composite nature, and continuous evolution and versioning — make it necessary to revise and extend the principles. Let’s hear from Maurits, one of TU Delft research software engineers, how the FAIR principle apply to Research Software and the distinction between FAIR software and open source:

FAIR and Open Software. Video recording from TU Delft MOOC Open Science: Sharing Your Research with the World. Presenter: Dr. Maurits Kok. Credits: TU Delft Extension School, TU Delft New Media Center, TU Delft Digital Competence Center. Licence: CC-BY-NC-SA.

If you develop software within your PhD project, we highly encourage you to follow the FAIR software checklist prepared by the TU Delft Digital Competence Center (DCC): https://tu-delft-dcc.github.io/software/checklist.html

If you are interested to learn more in depth about FAIR Principles for Research Software, check this publication: https://doi.org/10.15497/RDA00068