Session Kit: Module 21: Reproducibility and FAIR Principles in Connectomics

Everything needed to run Module 21 as a taught session: prep, timing, materials, misconceptions, rubric.

Generated from modules/module21.md. Edit the module page, not this file.

At a glance

   
Duration 4-5 hours
Capability target Publish a reproducibility-ready connectomics package (data + methods + metadata + limitations) that an external group can audit and reuse.
Learners leave with FAIR metadata form

Before you walk in

Learners should arrive having covered:

Materials

Run of show

Time Segment Your note
00:00-06:00 Framing: the silent bug  
06:00-16:00 The five-element checklist, modelled  
16:00-30:00 Guided practice: audit your own work  
30:00-40:00 Clean-environment rerun  
40:00-50:00 Known limitations, written honestly  
50:00-57:00 Competency check  
57:00-60:00 Exit ticket  

The activity

Scenario: Your lab plans to release a connectomics analysis package to collaborators.

  1. Build a FAIR metadata sheet for one analysis output.
  2. Create a reproducibility checklist with pass/fail criteria.
  3. Draft a “known limitations” section and one deprecation note.
  4. Peer-test another team’s package for reuse friction.

What learners hand in

Misconceptions to target

These are the errors this session exists to prevent. Surface them in the debrief rather than pre-empting them in the lecture — a misconception a learner has voiced is far easier to correct than one they are holding silently.

Naming the norm

Every session is a chance to make one piece of the hidden curriculum explicit. Pick a moment where you would normally just do the professional thing, and say out loud why you are doing it — then ask whether anyone was taught that.

For this session, the candidate is whichever norm the activity most depends on: stating an assumption in the same sentence as the claim, recording the version a number came from, or saying “uncertain” and having it count as a real answer. See the hidden curriculum for the collected set and why naming them is a fairness intervention rather than etiquette.

Assessment

Grade the reasoning, not the answer. A correct call with no evidence chain should not outscore a well-reasoned incorrect one — and saying so publicly changes behaviour within one session.

Exit prompt

Take one prior analysis output and add:

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

If this session goes wrong


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