Summary and Schedule

FAIR Research Data Coursebook

Six Steps to FAIR Implementation

This coursebook introduces practical ways to apply FAIR research data principles through concrete data management and reuse tasks.

Keywords: Research Data Management, Research Data Reuse, FAIR, FAIR Digital Objects

FAIR Research Data Coursebook logo

This coursebook was last updated in January 2025.

About

Many FAIR training materials are theory-heavy and difficult to connect to daily research practice. This coursebook takes a more practical approach, with examples that researchers can use to understand and implement FAIR data work.

Topics addressed in the lesson include:

• understanding research digital objects and persistent identifiers
• exploring open tools for interoperability and sustainable reuse
• learning how to create data terms of use and access protocols
• comparing data description practices across research fields
• understanding rich metadata and its role in long-term reuse

Lesson Focus

The coursebook is organized around six recurring FAIR data tasks:

1. Set up your own terms
2. Speak the same language
3. Securely share
4. Publish and preserve
5. Make machines work for you
6. Responsibly reuse

Six steps to FAIR

1. Set up your own terms -> Set up your own terms
2. Speak the same language -> Speak the same language
3. Securely share -> Securely share
4. Publish and preserve -> Publish and preserve
5. Make machines work for you -> Make machines work for you
6. Responsibly reuse -> Responsibly reuse

Start Here

• Setup
• Reference
• About
• Introduction

Acknowledgements

These efforts were supported by SURF funding for strengthening the RDM landscape on Digital Competence Center and by Maastricht University Library.

The coursebook draws inspiration from the FAIR Teaching Handbook and from The Turing Way, with the goal of making FAIR implementation easier to understand through practical examples.

Cite This Coursebook

Hernandez Serrano, P. V., and Vivas Romero, M. (2022, August 8). FAIR Research Data Coursebook. Zenodo. https://doi.org/10.5281/zenodo.6974103

	Setup Instructions	Download files required for the lesson
Duration: 00h 00m	1. Introduction	Does FAIR data mean open data? What are digital objects and persistent identifiers? What kinds of persistent identifiers are commonly used?
Duration: 00h 15m	2. Set up your own terms	What are data terms of use? What should a data terms of use statement contain? What format should terms of use use? What standard licenses are available for data?
Duration: 00h 40m	3. Speak the same language	What are data descriptions? How can data descriptions be reused? Are there standard ways to create them? What is the relationship between data descriptions and linked data?
Duration: 01h 05m	4. Securely share	What are data access protocols? Is open access a data access protocol? Can data be exposed as a service through FAIR API protocols?
Duration: 01h 30m	5. Publish and preserve	What is data archiving? What are data repositories? What is a DOI, and why is it important?
Duration: 01h 55m	6. Make machines work for you	What is the difference between metadata and rich metadata? How can a rich metadata file be created? Where should rich metadata be stored?
Duration: 02h 20m	7. Responsibly reuse	How should data be cited when reusing a data source? How can you check whether data is likely to be reused and discovered?
Duration: 02h 45m	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

To follow the coursebook effectively, learners should prepare a small set of accounts and familiar tools before starting.

Recommended Preparation

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• Create a GitHub account.
• Review a short introduction to Markdown and GitHub Pages.
• Create a DataverseNL account. If you are outside the Netherlands, use Harvard Dataverse.
• Join the lesson Slack workspace if it is still being used for your delivery format.

Introductory Slides

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The original introductory slides are still available here:

• Introductory slides