Session Kit: Module 17: Scientific Writing for Connectomics
Everything needed to run Module 17 as a taught session: prep, timing, materials, misconceptions, rubric.
Generated from modules/module17.md. Edit the module page, not this file.
At a glance
| Duration | 4-5 hours |
| Capability target | Produce a manuscript-ready results section (figures, legends, and claims) where each conclusion is traceable to explicit connectomics evidence and stated limitations. Students will also be able to write methods sections with the level of detail required for connectomics reproducibility and respond to peer review with technically precise, non-defensive language. |
| Learners leave with | Claim-evidence matrix (complete, with no empty cells) |
Before you walk in
- You can state the capability target in one sentence without reading it.
- You have one worked example you will narrate, including where you are unsure.
- Data access works — accounts, viewer, notebook — verified today, not last week.
- The rubric is visible to learners before they start, not after.
- You have decided what “uncertain” earns, and you will say so out loud.
Learners should arrive having covered:
- Basic statistical interpretation of connectomics outputs
- Ability to read method sections in technical papers
Materials
Run of show
| Time | Segment | Your note |
|---|---|---|
| 00:00-08:00 | Good writing vs bad writing in connectomics | |
| 08:00-18:00 | Claim-evidence matrix construction | |
| 18:00-28:00 | Results paragraph drafting | |
| 28:00-38:00 | Methods and provenance exercise | |
| 38:00-50:00 | Reviewer response practice | |
| 50:00-58:00 | Peer exchange and feedback | |
| 58:00-60:00 | Competency check |
The activity
Scenario: You are preparing a short paper section on motif enrichment from a connectome analysis. Your team has identified that reciprocal connections between excitatory and inhibitory neurons in cortical layer 2/3 occur 2.1x more frequently than expected under a degree-preserving null model. The analysis used MICrONS minnie65 data, CAVE materialization v795, with synapse detection via the CAVE synapse table (cleft score threshold > 50). A total of 1,247 reciprocal pairs were observed across 12,891 possible excitatory-inhibitory pairs.
- Draft three result claims from the provided scenario, each with different confidence levels (strong, moderate, exploratory).
- Build a claim-evidence matrix (claim, figure panel, metric, statistical test, effect size, dataset version, caveat).
- Write a 300-400 word results subsection with calibrated uncertainty language.
- Write a methods paragraph with full dataset provenance and reproducibility details.
- Respond to two mock reviewer comments:
What learners hand in
- Claim-evidence matrix (complete, with no empty cells)
- Results subsection draft (300-400 words, every claim traceable)
- Methods paragraph with full provenance
- Reviewer response draft with revision notes (structured format: quote, response, manuscript location)
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.
- They may believe: Treating the methods section as a formality to write last. In connectomics, draft the methods first because they constrain what you can legitimately claim.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Writing stronger language does not strengthen weak evidence. Adjectives like “striking,” “remarkable,” and “clearly” do not substitute for effect sizes and confidence intervals.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Uncertainty statements are not weakness; they are reproducibility signals. A paper that acknowledges its limits is more credible than one that ignores them.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Assuming readers know which dataset version you used. Even within the same project (e.g., MICrONS), different materialization timestamps produce different connectivity tables.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Linking to a GitHub repository is not sufficient if the repository has no tagged release and the methods do not specify which commit was used.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Uncertainty statements are not weakness; they are reproducibility signals.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Assuming that citing the original EM paper covers all required attributions. Segmentation, proofreading, and annotation are separate contributions that deserve separate citations.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
- They may believe: Defensive tone weakens technical credibility. Never characterize a reviewer’s comment as “wrong” — instead, provide the evidence that supports your position.
- Surface it by asking: “What would have to be true for that to hold? What would change your mind?”
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
- Minimum pass
- Strong performance
- Common failure modes
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
Write one results paragraph from a connectomics figure and include:
- one quantitative claim with effect size and confidence interval,
- one explicit caveat tied to a known data limitation,
- one sentence on reproducibility assumptions (dataset version, materialization, code),
- one figure legend sentence that specifies sample size and uncertainty indicator.
If this session goes wrong
- Nobody talks in the debrief. You asked “any questions?” Ask instead: “Which cue would you drop first if the data got worse?”
- Everyone finishes early. They are pattern-matching, not judging. Give an ambiguous case where the answer is “uncertain” and see what happens.
- Nobody finishes. The scaffolding came off too fast. Work the next case together rather than pressing on.
- A learner is silently lost. The most likely cause is unstated vocabulary. Point them at the dictionary and check back.