Module 21: Reproducibility and FAIR Principles in Connectomics

Teaching Deck

Learning Objectives

• Apply FAIR principles to connectomics data products
• Define minimum reproducibility metadata for analysis releases
• Build transparent methods/parameter logs for peer reuse
• Identify hidden-curriculum norms in reproducibility expectations

Session Outcomes

• Learners can complete the module capability target.
• Learners can produce one evidence-backed artifact.
• Learners can state one limitation or uncertainty.

Agenda (60 min)

• 0-10 min: Frame and model
• 10-35 min: Guided practice
• 35-50 min: Debrief and misconception correction
• 50-60 min: Competency check + exit ticket

Capability Target

Publish a reproducibility-ready connectomics package (data + methods + metadata + limitations) that an external group can audit and reuse.

Concept Focus

1) FAIR as implementation checklist

• Technical: findable identifiers, accessible storage, interoperable formats, and reusable metadata each require concrete engineering choices.
• Plain language: “FAIR” only counts if someone else can actually find, open, and use your work.
• Misconception guardrail: posting files online is not enough.

Core Workflow

• See module page for details.

60-Minute Run-of-Show

• See module page for details.

Misconceptions to Watch

• Misconception guardrail: posting files online is not enough.
• Misconception guardrail: a notebook that runs once does not guarantee robust science.
• Misconception guardrail: learners should not be penalized for norms that were never made explicit.

Studio Activity

Activity Output Checklist

• Evidence-linked artifact submitted.
• At least one limitation or uncertainty stated.
• Revision point captured from feedback.

Assessment Rubric

• Minimum pass
• Core provenance fields are present and clear.
• Re-run instructions are testable by peers.
• Limitations and assumptions are explicit.
• Strong performance
• Identifies hidden reproducibility norms and makes them explicit.
• Anticipates downstream reuse failure points.
• Produces concise, audit-friendly documentation.
• Common failure modes
• Missing version identifiers for data/code.
• Methods descriptions that omit key parameters.
• “Reproducible in principle” claims without validation evidence.

Exit Ticket

Take one prior analysis output and add:

1. provenance metadata,
2. reproducibility instructions,
3. a 5-line limitations section.

References (Instructor)

• Wilkinson et al. (2016) - FAIR Guiding Principles.
• Peng (2011) - Reproducible Research in Computational Science.
• Project-specific release documentation for H01/MICrONS/FlyWire.

Teaching Materials

• Module page: /modules/module21/
• Slide page: /modules/slides/module21/
• Worksheet: /assets/worksheets/module21/module21-activity.md