Topic, definition and scope
In this lesson plan, we describe the data life cycle and its steps, describing for each of them the best practices to make your data FAIR. Awareness of these processes would allow you to plan in advance and get in touch with the right infrastructures, with the final scope to make your research data FAIR by design.
Summary of Tasks / Actions
- Exercise for the participants: List the research life cycle phases and the applicable FAIR elements with explanations
- Prepare an exercise for the audience: using a particular tool to assess FAIRness in your research data/research data of researchers that you are supporting (maybe this exercise is more suitable in other units? Instructors can also provide datasets)
- Exercise for the participants: Try to find datasets (through a repository) that are FAIR
- Exercise: play games (see references)
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:
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[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
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- 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
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.