Topic, definition and scope
Train-the-Trainer: Data Life-Cycle and FAIR from start
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.
Summary of Tasks / Actions:
- 1.0 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.
- 2.0 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.
- 3.0 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.
References
- Research data management (University of Virginia): https://guides.lib.virginia.edu/c.php?g=515290\&p=3522215
- FOSTER data life cycle +Open Science principles:https://docs.google.com/presentation/d/1SZ6ADqTNp_GBT3UT-JAsH-6zKfAWab_1xMOSxZhdZvw/edit?usp=sharing
- Research data management (UK Data service): https://ukdataservice.ac.uk/learning-hub/research-data-management/
- The FAIRplus Dataset Maturity (DSM) Model: https://fairplus.github.io/Data-Maturity/
- The FAIR Data Maturity Model: https://datascience.codata.org/articles/10.5334/dsj-2020-041/
- Games:
-
[Welcome to League of Data League of Data (sshopencloud.eu)](https://lod.sshopencloud.eu/) - UZH - Open Science - Open Science Game: Open Up Your Research
- Data Horror Escape Room
-
- RDM toolkit: https://rdmkit.elixir-europe.org/
- FAIR Cookbook: https://doi.org/10.1038/s41597-023-02166-3 and https://faircookbook.elixir-europe.org/content/home.html
- FAIR in Action: a flexible framework to guide FAIRification - https://doi.org/10.1038/s41597-023-02167-2
- Implementation of FAIR: https://www.fairsfair.eu/implementation-adoption-stories
- FAIR principles explained https://www.go-fair.org/fair-principles/
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.
Lesson content
Reading
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.
Exercise:
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
The terms4FAIRskills project has created a formalised terminology that describes the competencies, skills and knowledge associated with making and keeping data FAIR.