Data Life Cycle approach to FAIR: FAIR by design

Overview

This topic introduces the research data life cycle, breaking down each phase alongside the practical best practices required to embed FAIR principles from day one. By fostering deep awareness of these cyclical processes, this lesson equips trainers to help researchers plan proactively, engage with the appropriate institutional infrastructures early, and ultimately ensure their research data is "FAIR by design" rather than as an afterthought.

TAKE-HOME MESSAGE

You can relate different elements in a research project in general to different phases of the data life cycle. At each step, different methods, tools, or infrastructures are required to make your data FAIR. Planning for this in advance is extremely beneficial for your projects, not only to produce FAIR data by design but also in terms of organisation and budgeting. This information should be recorded in a data management plan (DMP) at an early stage.

Learning outcomes

1

Define the FAIR principles (Understand/Remember)

Beginner
2

Define the different phases of the data life cycle (Understand/Remember)

Beginner
3

List the reasons why you should start implementing FAIR at the start of the research life cycle/your project

Beginner
4

Recognise several versions/models of research data life cycle

Beginner
5

Describe ways of making your data FAIR at every phase of the research data life cycle

Beginner
6

Identify the different tools, technologies and methods that are available to help with FAIR data at different phases of the research data life cycle (Understand/Remember)

Beginner
7

Apply tools that are available to assess the FAIRness of your data

Intermediate
8

Participants will be able to apply the FAIR principles to their data in alignment with institutional and funder requirements.

Intermediate

Lesson agenda

3 activities · 95 min total · grouped by phase

0:00

Reading LO 1

Read and Think about the FAIR article

Prior to the session, participants will read the foundational 2016 paper by Wilkinson, Dumontier, et al. and identify the core distinctions between ‘human-readable’ data and ‘machine-actionable’ data as defined by the authors.

The cited article is the following:

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., … & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1), 1-9.

The Lesson will start with the teacher asking questions in the PBL (Problem Based Learning) style.

45 min Beginner

0:45 End of phase · 45 min

0:00

Lecture LO 2

Lecture

For the teaching of the following sections the teacher can make use of the FAIR Metroline Resources:

[Define FAIRification objectives FAIR Metroline](https://fairmetroline.org/metroline_steps/define_fairification_objectives)

1.0 Define the FAIR principles

2.0 Explain the stages of the data life cycle

3.0 Define the stages of the data life cycle inside the institution where the training is provided

30 min Beginner
0:30
Group discussion LO 2

Describing the Data Life Cycle
- The Launch & Grouping:
- 3 minutes.
Break the room into trios. Instruct them to choose one person in their group to act as the “Synthesizer” who will speak to the room later. Share the link to your electronic whiteboard board.

Individual Sticky Note Brainstorm:
- 5 minutes.
Ask each participant to think about their own current research data. On their own individual digital sticky notes, they write down 2–3 specific tasks, tools, or data types they use.

The Collaborative Sort (Column Dragging):
- 7 minutes.
As a group, participants drag their sticky notes into the 5 columns on the board. They must discuss why a certain step belongs under “Analysis” versus “Archival.”

The Group Synthesis:
- 5 minutes.
The designated Synthesizer from each group looks at their column or board zone and identifies **one common thread
20 min Beginner

0:50 End of phase · 50 min

Summary of tasks and actions

1
Pre-Workshop Reflection
Participants read and critique a foundational article on the FAIR principles prior to the session, bringing their notes and reflections to kickstart the workshop.
2
Contextualizing FAIR in the Data Life Cycle
The trainer presents the data life cycle stages, mapping the specific FAIR requirements onto each phase according to the institution's localized workflows and the specific scientific disciplines of the participants.
3
Collaborative Life Cycle Mapping
Participants work in triads (groups of three) using an interactive whiteboard. They are tasked with mapping specific research activities and corresponding FAIR principles onto the correct stages of a blank data life cycle diagram.

Materials and equipment

For Participants: A computer or tablet with a stable internet connection (essential for online delivery or for accessing cloud-based collaborative tools).

For the Trainer:- Virtual Delivery: Access to a digital collaboration platform (e.g., Miro or an equivalent online whiteboard).

In-Person Delivery: A physical whiteboard or chart paper, sticky notes, and markers.

Sources and attribution

University of Virginia Library. Research data management. [Internet] Library Guides; [cited 2026 Sep 1].
FOSTER Open Science. Data life cycle + Open Science principles. [Google Slides presentation] [cited 2026 Sep 1].
UK Data Service. Research data management. [Internet] Learning Hub; [cited 2026 Sep 1].
ELIXIR Europe. RDMkit: Research Data Management toolkit. [Internet] [cited 2026 Sep 1].

CITE THIS LESSON PLAN

Bouhuis A, Holinski A, Muhammad N, Ahokas M, Lindvall J, Vivas-Romero M. Data Life Cycle approach to FAIR: FAIR by design. Version 1.0. FAIR Lesson Plan Handbook [Internet]. 2026. Available from: https://doi.org/10.5072/zenodo.627817

Licence CC BY 4.0 DOI 10.5072/zenodo.627817 (sandbox DOI, not citable) Last updated 29 October 2026

Data Life Cycle approach to FAIR: FAIR by design

Overview

Learning outcomes

Define the FAIR principles (Understand/Remember)

Define the different phases of the data life cycle (Understand/Remember)

List the reasons why you should start implementing FAIR at the start of the research life cycle/your project

Recognise several versions/models of research data life cycle

Describe ways of making your data FAIR at every phase of the research data life cycle

Identify the different tools, technologies and methods that are available to help with FAIR data at different phases of the research data life cycle (Understand/Remember)

Apply tools that are available to assess the FAIRness of your data

Participants will be able to apply the FAIR principles to their data in alignment with institutional and funder requirements.

Lesson agenda

Before the lesson 1 activity 45 min

Read and Think about the FAIR article

During the lesson 2 activities 50 min

Lecture

Describing the Data Life Cycle

After the lesson 0 activities 0 min

Summary of tasks and actions

Materials and equipment

Sources and attribution