Repositories

Lecture:

Repositories as FAIR Enablers: A brief opening presentation defining what a repository is (and what it isn’t).

• Key Concept: The trainer contrasts simple cloud storage (like Google Drive or Dropbox) with a true FAIR-enabling repository. Key differentiators highlighted include:
  • PIDs: Automatic assignment of persistent, citable DOIs.
  • Structured Metadata: Indexing schemas (e.g., Dublin Core) that make data searchable by external search engines.
  • Long-Term Preservation: Commitment to bit preservation and open, migrate-ready file formats.

Exercise:

The Data Hunting Exercise

1. Set up the challenge: Choose a topic the participants could explore while looking for data on a particular repository examples can include:

• Environmental Science: Ocean acidification rates in the North Sea
• Health: Genomic Sequencing and antibiotic-resistant bacteria
• Education: Statistics on numbers of International Students in Medical Schools in Europe

2. Looking for Places to Search: Provide the participants with different locations to look for this data:

• Generic Repositories: Zenodo, Figshare, DataverseNL, Dryad, Dataverse, DANS Data Station
• Domain Specific: Pangea (Earth Science), NCBI (BIO), GBIF (Biodiversity)
• Search Engines: Google Dataset Search, DataCite Commons, OpenAIRE Explorer

3. The Scavenger Hunt Checklist: Participants must find a data set in at least two of the categories provided and fill out this evidence check list: ****

• Persistent identifier: (PID) Can you find a DOI?
• Meta-data Richness: On a scale of 1-5, how well is the data described? (Are there column definitions, read-me files, methods?
• Interoperability: What file formats are used? (Proprietary like .xlsx. or open like .cvs)

4. The Debriefing: “Comparison Gallery”

• Which repository felt most trustworthy and why?
• Did you find the same dataset in two different places (this introduces the concept of data harvesting and mirroring
• Which metadata record made you feel like you actually re-use the data right away?

Annotations: Have participants made annotations on a common document they would take home. This could also work as feedback and improvement mechanisms for this particular activity.

Lecture:

Presenting Local Repositories & Workflows : A practical demonstration of institutional data tools, featuring local systems like DataverseNL.

Key Concept: The trainer walks through the local data deposition pipeline:

1. Deposit: Creating a dataset draft within the department’s dedicated collection.
2. Curation: The local data steward/curator reviewing the metadata, checking for direct or indirect personal data, and verifying stable file formats.
3. Publication: Activating the DOI, setting appropriate Creative Commons usage licenses, and configuring file access (Open Access vs. Restricted/Embargoed).

Exercise:

The “In Silico” Shortcut Exercise

Scenario: A new variant of a rare respiratory virus has emerged. Your team needs to find an existing drug that can be “repurposed” to treat it.

1. The Challenge: Wet Lab vs. Data Mining

Divide participants into two groups (or have them compare the two strategies): The strategies were led by two different groups of researchers.

• Strategy A: The Traditional Bench Scientist (Primary Data)
  • Task: Synthesize 1,000 new chemical compounds and test them against live virus cultures.
  • Cost: $2 Million + 3 years of clinical trials.
  • Risk: High. Most compounds will be toxic or ineffective in humans.
  • Data Produced: Very specific, high-resolution data for a small set of molecules.
• Strategy B: The Bioinformatician (Data Discovery & Reuse)
  • Task: Use data discovery to mine the Protein Data Bank (PDB) for the virus’s structure and ChEMBL for existing FDA-approved drugs.
  • Cost: $100k (mostly computing power and researcher time).
  • Timeline: 3 months.
  • Action: Run a “virtual screening” (docking) to see which already-approved drugs might “stick” to the virus protein.
  • Data Reused: Structural biology data from a lab in Japan, chemical properties from a database in the UK, and clinical safety data from the 1990s.

2. The “Discovery” Checklist (Life Science Specific)

Have students identify which specific life-science repositories they would need to “discover” data from to succeed in Strategy B:

1. Genomics: Where is the virus’s RNA sequence? (e.g., NCBI GenBank).
2. Proteomics: Where is the 3D shape of the virus’s “spike” protein? (e.g., UniProt or PDB).
3. Pharmacology: Where are the records of drugs already safe for humans? (e.g., DrugBank or PubChem).

3. Discussion: Why Reuse is Vital in Life Sciences

After the comparison, lead a discussion on these “Bio-Specific” benefits:

• The 3Rs (Ethics): How does reusing data reduce the need for animal testing? (If the data already exists, it is ethically questionable to repeat a painful animal experiment).
• The “N of 1” Problem: In rare disease research, there might only be 10 patients in the whole world. No single hospital can do a study. Discovery and Aggregation of data from 10 different countries is the only way to get a statistically significant result.
• Long-tail Data: A lot of life science data is hidden in “Supplemental Materials” of old papers. How does semantic annotation (Goal 2) help us find a gene mention hidden in a PDF from 2005?

Take away: Have participants note down their discussion points. These points might be valuable insight to reproduce the exercise.

Exercise:

Repository Speed-Dating

Objective: Match a “Data Profile” to the correct “Repository Type” based on the discovery and reuse principles learned earlier.

1.The Setup (5 minutes)

Give each participant (or small group) three “Data Profile Cards.” You can display these on a screen or print them:

• Profile A: A small spreadsheet of water temperatures from a local lake, collected over 2 weeks. Needs to be cited in a paper.
• Profile B: 500GB of high-resolution 3D protein structures of a new virus variant.
• Profile C: Sensitive patient records from a rare disease study across three hospitals (requires restricted access).

2. The “Speed Match” (10 Minutes)

Participants must “match” their profiles to the most appropriate repository from the lesson (e.g., Zenodo, PDB, DANS, or NCBI) and defend their choice based on the FAIR principles.

The Twist: For each match, they must identify one “Dealbreaker.” (e.g., “I can’t put Profile C on Zenodo because it’s open-access and the data is sensitive/un-anonymized.”)

3. The “Legacy” Handshake (5 Minutes)

To finish, each participant writes one “Note to the Future” on a post-it or a shared digital doc (like a Jamboard or Padlet).

Example: “I am depositing [Type of Data]. To make sure someone can find and reuse this in 10 years, the most important metadata tag I will include is ____________ because ____________.”