FAIR vs open data/science

Lecture:

Introduction to FAIR and Open Science

Introduce participants to the concepts of FAIR and Open Science

Exercise:

Comparison between FAIR and Open Data

Students will compare two different scenarios in groups of 3. They will at the end discuss whether it is FAIR or Open Data in the examples.

Time 15 minutes

Scenario 1: Patient Trial

A highly structured, machine-readable dataset of clinical trial results. It uses standardized medical vocabulary, possesses a unique DOI (digital footprint identifier), and features detailed metadata. However, because it contains private medical data, researchers must sign a strict privacy agreement to get an encrypted access token.

Correct Answer: FAIR but closed

Scenario 2: the Github Treasure

A genomics lab uploads a gene-sequencing dataset to a public repository. It has a unique DOI, uses standard FASTA file formatting, includes rich metadata explaining the methodology, and carries an open-use license.

Correct Answer: Open and FAIR

Plenary discussion:

Time 10 minutes

The following questions can be used in the Plenary discussion:

• If Scenario 1 isn’t ‘Open,’ why is it still incredibly valuable for science? (This helps them realize that protecting privacy doesn’t mean data should be messy or unfindable).
• What would we need to change in Scenario 2 to make it only Open, but no longer FAIR? (This tests if they can reverse-engineer the concepts—e.g., stripping the metadata and DOI, and dumping the data as a raw, unlabeled text file).

Exercise

Identifying Benefits and Challenges of Open Science and FAIR

Time 10 minutes

• Put students in pairs. Assign every pair one specific stakeholder from the research world (e.g., Pair A looks at the Individual Researcher, Pair B looks at The Public/Society, Pair C looks at The Scientific Community.
• Instruct the pairs to write down two things on their sheet or digital board:

1. Describe The Benefit: Describe one major reason why Open/FAIR science helps their stakeholder.
2. Describe The Cost The Challenge: Describe one major roadblock or headache this stakeholder faces when trying to do it.

• Call out each stakeholder group and have one pair rapidly read aloud their descriptions. The instructor notes them on the board to build a collective map.

Lecture:

Mapping the Open Science and FAIR Institutional Landscape

Introduce the participants to the Open Science and FAIR support infrastructure in your Institution.

Exercise:

Identify the right Help-Line

Time 15 minutes:

Present in a Mentimeter different catastrophic scenarios for a researcher. Then have the participants choose who they right contact person might be.

• Scenario 1: “I am writing a grant proposal and the funder requires a 2-page Data Management Plan (DMP) by next Friday. I don’t know where to start.”
  • Correct Channel: ➡️ Your Central Data Steward at the Faculty or University Hospital (They provide DMP templates and review drafts).
• Scenario 2: “I have 600 gigabytes of human neuroimaging data. I know it needs to be FAIR, but I don’t know what metadata standard or file repository my specific faculty prefers.”
  • Correct Channel: ➡️ Your Central Data Steward at the Faculty or University Hospital (They are embedded experts who know domain-specific standards).
• Scenario 3: “I want to publish my article Open Access, but the journal is charging an 1800 euro fee. I need to know if our university has an agreement to cover this cost.”
  • Correct Channel: ➡️ The Library Open Access Team (They manage journal publisher deals and funding pots).
• Scenario 4: “My dataset contains highly encrypted personal identification keys. I need a secure server to store it while we analyze it.”
  • Correct Channel: ➡️ The University or University Hospital Privacy Team (They handle secure infrastructure and GDPR storage compliance).

Lecture:

Introduction to Repositories

Exercise: Choosing the right Repository

Time 15 minutes

• Explain the Case Study:

Have the participants read the following case study and decide which repository they would choose to publish at least the study’s meta-data

Dr. Alcaraz is leading a qualitative research project exploring long-term health outcomes and access to medical care for children living in temporary immigration communities in the Philippines.

• The Data: 45 hours of raw audio recordings and transcriptions from deep, semi-structured interviews with children (ages 6–12) and their legal caretakers. The transcripts mention specific medical conditions, illegal housing arrangements, and exact geographical locations.
• The Dilemma: Dr. Alcaraz knows the raw data is far too sensitive to be completely public under data protection regulations (like GDPR or local privacy laws). However, her funding body requires her to make the project FAIR. She decides she will keep the raw transcripts securely locked away, but she wants to publish the metadata record openly so other global health researchers know the project exists and can request collaboration.
• Give the participants the following list of Repositories to choose from:

• Debriefing choices:

When the groups make their choice and present their arguments, there isn’t one single “perfect” answer, but there are distinct advantages they should explain:

• If they choose DataverseNL: This is an excellent choice if Dr. Alcaraz is based at a Dutch institution. It ensures local institutional compliance, provides institutional backing, and has reliable infrastructure for restricted-access metadata.
• If they choose QDR (Domain-Specific): This is technically the Gold Standard according to the repository selection hierarchy (domain-specific first). Because QDR specializes in qualitative data, their curators will actually look at the metadata to ensure no identifying information about the children accidentally slipped into the abstract or project description.
• If they choose Zenodo: While highly accessible and great for rapid DOI generation, it is a generalist repository. It lacks the human-curated oversight that a highly sensitive project involving minors might require to prevent accidental data leaks in the public text fields.

Exercise:

Explaining the Challenges of Making data Open

Project a fake, high-risk research snippet on your Mentimeter screen. Tell the students: (e.g.) You want to share this raw study dataset openly on GitHub today. Look at the text—which details are a massive legal or ethical hazard if published?

2.The Mentimeter Vote:

Time 2 - 5 minutes

Have students select from a multiple-choice list or use a word cloud to flag the dangerous data points. Ask them to think about GDPR, Participant Privacy, and Intellectual Property .

3.The Peer Explanation Debrief:

Time 5 - 10 minutes

Turn to the class and ask: “Why can’t we just use a simple ‘Find and Replace’ to delete the names and call it a day?” Have 2 or 3 students explain the hidden challenges of anonymization.