Metadata

Overview

This lesson plan has been created with the aim to educate PhD students and researchers on metadata standards using tangible examples and practical activities. It assumes a low level of prior knowledge regarding metadata but assumes experience in research and familiarity with the FAIR principles. Resources can be provided asynchronous to bring everyone up to the same level.

We recommend starting by building up a theoretical baseline of all participants before giving the opportunity for participants to practice and start working directly with metadata, metadata standards and general concepts.

Note that many activities can be done as an individual or a group. This can be changed based on what type of session you are giving. Working in pairs or small groups can increase peer-learning and reduce the threshold for asking questions when confused (as these participants can discuss with their peers rather than having to ask the instructor). Working individually can also be advantageous when doing asynchronous work, hybrid or online sessions or with self-paced study.

Impact for research

The adoption of high-quality metadata standards significantly enhances the visibility and longevity of research outputs. By mastering these concepts, researchers ensure that their datasets are not only archived but are actively discoverable by search engines and aggregators, preventing data isolation. Rich, semantically annotated metadata enables sophisticated query retrieval and facilitates machine-to-machine communication, allowing software agents to validate and process data without human intervention. Ultimately, this streamlining of data brokering and validation accelerates scientific discovery by making it easier for the global community to find, cite, and build upon existing research.

FAIR element(s)

Findable: Data should be available in a discoverable resource (i.e. repository), have appropriate description (i.e. metadata) and have a persistent identifier (PID)

Data are described with rich metadata

TAKE-HOME MESSAGE

High-quality metadata is the foundation of scientific visibility. By applying domain-standard metadata and persistent identifiers early, researchers prevent data isolation, ensure long-term citation, and enable automated discovery across global research networks.

Learning outcomes

1

Understand what metadata is and how it relates to FAIR

Beginner
2

Understand that good and discipline specific metadata enhances findability and FAIRness of data

Beginner
3

Evaluate how different standards of metadata influence findability and FAIRness of data

Intermediate

Lesson agenda

10 activities · 245 min total · grouped by phase

0:00
Individual exercise LO 1

Activity
Watch or read summary of what metadata is.

Resources
- Documentation and metadata from RDM kit
- What are metastandards video
Purpose: General background information on what metadata is
60 min Beginner

0:60 End of phase · 60 min

0:00

Group discussion LO 1

Activity

Ask participants what they already know about metadata and if they can form a definition. Can be group discussion or think-share-pair

Purpose: Get participants to talk to each other (ice-breaker) and gain insight on prior knowledge

10 min Beginner
0:10

Lecture LO 1

Activity

Short lecture covering basics of metadata, including definition and how it influences findability and relates to FAIR data. Show how metadata may be different when it needs to be interpreted by computers or humans. (slides to be created).

Show examples e.g., manuscript + fields to fill in as well as documentation and description of data

Resources: Slides Interactive poll for formative assessment

Purpose: Get everyone to same level of knowledge on metadata

25 min Beginner
0:35

Individual exercise LO 2

Activity

Short lecture that Provide examples of metadata (standards) used by researchers in different fields (three: biomedical, social sciences and humanities, and science and technology domains) examples of domain specific metadata standards. If domain specific metadata standards don’t exist, provide some examples about the development/use of custom metadata.

Resources: Examples of published data with good (and bad) metadata, custom metadata fields examples

Purpose: Give tangible examples of metadata and how it is used in research in different fields

15 min Beginner
0:50

Individual exercise LO 2

Activity

Personal Data “audit” (Richness & Standards)

Participants have 10 minutes to write a description of some of their data (without looking at it). After 10 minutes they can self-assess or peer review with a partner whether their description was sufficient to understand what the data is and how to use it

Resources: Participants should bring a dataset that they have worked with over 6 months ago. Participants should have their own laptop

Purpose: This highlights the need for “generous” description and semantic annotation.

30 min Intermediate
0:80

Group activity LO 3

Activity

Free Text vs. Controlled Vocabulary. Ask participants how they would describe the sex of a female mouse in a data spreadsheet. Have them write in a shared document (e.g., google doc) or share with the group. Introduce the concept of ontology terms and semantic annotation maps

Resources: Shared doc. Ontology terms. Participants should have their own laptop

Purpose: You will likely get variations like: “female,” “F,” “Female,” “fem,” “doe.”

20 min Intermediate
0:00

Individual exercise LO 2

Activity

The “Mystery Data” Challenge (Motivation & Concept). Send participants a messy dataset and ask them to spend 5 minutes guessing what it represents.

Resources: Messy dataset e.g., an Excel sheet with ambiguous column headers (e.g., “Temp,” “T,” “Val1”) and no units or context. Participants should have their own laptop

Purpose: They will likely fail or guess incorrectly. This sets the stage for defining metadata as the “missing context” required for understanding

15 min Beginner
0:15

Individual exercise LO 3

Activity

Findability scavenger hunt. Assign a specific, niche life-sciences query (e.g., “RNA-seq data for Arabidopsis thaliana under drought stress”). Get participants to either search for this using google or domain specific repository.

Resources: Specialised repository e.g. EBI ArrayExpress or NCBI GEO. Participants should have their own laptop

Purpose: Participants should reflect which method was faster and why (i.e., the repository offered filters/facets based on metadata fields like “organism” or “study type”)

30 min Intermediate
0:45

Individual exercise LO 2

Activity

Registry Exploration (Standards Identification). Do a short demo on how to use FAIRsharing and what kind of keywords might be best (starting from specific, moving towards generic). Remind them that keywords are not only scientific fields but also methods and species. Get participants to find a relevant metadata standard to their field.

Resources: Participants should have their own laptop

Purpose: Focus finding minimum requirement/minimum information standards.

20 min Beginner
0:65

Group activity LO 2

Activity

“Facet Filtering Challenge” (Retrieval). Go to a Life Sciences portal (e.g., EBI Search or NCBI Datasets). Challenge: “Find a dataset about Breast Cancer.” (Too many results). Refinement: “Now, use metadata filters to narrow it down to: RNA-Seq data, published within the last 2 years, involving Homo sapiens.” Discuss how structured metadata contributed to easy filtering and searching.

Resources: Participants should have their own laptop. Life sciences portal

20 min Beginner

0:85 End of phase · 185 min

Summary of tasks and actions

No separate task summary is stored yet. The activity agenda above is the current source for trainer actions.

Materials and equipment

For Participants: A computer and a stable internet connection (if the training is delivered online or uses cloud-based tools).

For the Trainer: A digital collaboration tool (such as Miro or an equivalent virtual whiteboard). For in-person delivery, this can easily be substituted with physical chart paper, sticky notes, and markers.

Sources and attribution

FAIRsharing. Metadata standards. [Internet] [cited 2026 Sep 1].

Overview

Learning outcomes

Understand what metadata is and how it relates to FAIR

Understand that good and discipline specific metadata enhances findability and FAIRness of data

Evaluate how different standards of metadata influence findability and FAIRness of data

Lesson agenda

Before the lesson 1 activity 60 min

Activity

During the lesson 9 activities 185 min

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After the lesson 0 activities 0 min

Summary of tasks and actions

Materials and equipment

Sources and attribution