Everything needed to teach each module: lesson flow, activities, slides, and worksheets.
This is delivery material: written for whoever is running a session, not for whoever is learning. It is the hosted-workshop mode of the same curriculum that self-study learners read directly.
Nothing here is a separate body of content. Session kits are generated from the module pages, and the depth behind every activity lives in the core — the content library, dictionary, journal club, atlas and hidden curriculum. If a learner asks a question a kit does not answer, the core is where you look.
Running a session? Go to the session kits. Each is one page holding the prep checklist, run of show, materials, misconceptions to target, and rubric for a single module — assembled from pieces that used to live in five different places.
Designing a course, or wondering why the sessions are shaped this way? Start with the Facilitator Guide. It covers why at least half of contact time has to be learner judgment rather than explanation, how to differentiate across the four learner personas, and how to assess calibration rather than raw accuracy.
Each card links to the lesson page, rendered slide deck, editable source, and activity worksheet.
Turn broad interest in brain mapping into concrete, testable connectomics questions with explicit scope and measurable outcomes.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module01.marp.md | Worksheet
Make implicit research expectations explicit: lab norms, communication scripts, dataset responsibilities, and building a personal support network.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module02.marp.md | Worksheet
Hands-on Python and Jupyter skills for reproducible connectomics data exploration, from environment setup through documented analysis workflows.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module03.marp.md | Worksheet
Neuroanatomical fluency for interpreting EM structures across cortical layers and brain regions, with attention to uncertainty and misclassification risks.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module04.marp.md | Worksheet
How EM produces the raw data of connectomics: acquisition principles, common artifacts, and image quality screening for segmentation readiness.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module05.marp.md | Worksheet
Core segmentation error taxonomy—merges, splits, boundary errors—and a practical correction workflow with documented quality impact.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module06.marp.md | Worksheet
Proofreading strategies that prioritize scientifically high-impact corrections and maintain reproducible, documented QC standards.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module07.marp.md | Worksheet
Designing testable connectomics hypotheses with measurable structural outcomes, appropriate null models, and explicit uncertainty limits.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module08.marp.md | Worksheet
Extracting and interpreting skeleton representations and morphology descriptors from segmented neurons for cell-type reasoning.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module09.marp.md | Worksheet
Representing connectomes as graphs, computing core network metrics, and interpreting results with biological and statistical caution.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module10.marp.md | Worksheet
Interpreting synaptic organization and local circuit motifs from connectomics data, differentiating robust patterns from reconstruction artifacts.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module11.marp.md | Worksheet
Scalable data architecture, query planning, and provenance tracking for petascale connectomics datasets like MICrONS and H01.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module12.marp.md | Worksheet
ML workflows for connectomics with controls for data leakage, spatial correlation bias, and biologically meaningful evaluation metrics.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module13.marp.md | Worksheet
Computer vision methods—from classical filters to deep learning—applied to EM imagery for segmentation support, morphology extraction, and quality diagnostics.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module14.marp.md | Worksheet
LLM-assisted patch triage and annotation support with human-in-the-loop verification gates to prevent hallucination and unsupported scientific inference.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module15.marp.md | Worksheet
Principled visualization of connectomics structures and analysis results: encoding uncertainty, avoiding misleading representations, and producing publication-ready figures.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module16.marp.md | Worksheet
Writing evidence-grounded connectomics manuscripts, clear figure legends, and effective reviewer responses for neuroscience audiences.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module17.marp.md | Worksheet
Reproducible preprocessing workflows from raw connectomics data through analysis-ready releases with integrity checks, QC metrics, and full provenance.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module18.marp.md | Worksheet
Applying peer-review criteria and research-ethics frameworks to connectomics manuscripts, workflows, and collaborative decisions.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module19.marp.md | Worksheet
Defensible statistical inference for connectomics: choosing null models, controlling multiplicity in high-dimensional tests, and reporting with explicit assumptions.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module20.marp.md | Worksheet
Operationalizing FAIR principles and reproducibility standards for connectomics datasets, analysis code, and public releases.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module21.marp.md | Worksheet
Delivering clear scientific talks for technical and mixed audiences without oversimplifying structural evidence, with explicit question-handling norms.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module22.marp.md | Worksheet
Conference-ready abstracts and posters with explicit hidden-curriculum support for networking, Q&A, and navigating scientific meetings.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module23.marp.md | Worksheet
Evidence-based career strategy for connectomics: evaluating graduate programs, drafting targeted mentor outreach, and navigating admissions hidden curriculum.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module24.marp.md | Worksheet
Capstone portfolio assembly demonstrating end-to-end connectomics competencies with curated artifacts, reflective commentary, and mentor feedback.
Lesson: Module page
Deck: Slide page | Rendered HTML | Markdown source
Source: course/decks/marp/modules/module25.marp.md | Worksheet