Before building anything, we need a principled way to manage scientific data — datasets that are large, heterogeneous, version-sensitive, and expensive to recompute. The data foundations layer establishes the abstractions that every subsequent lesson builds on.

Lessons Covered

Lesson 00 — Foundations

Datasets, lazy evaluation, and the shape of scientific data management. Introduces the @evaluate_datasets decorator pattern: scientific functions declare what data they need, and the framework resolves, caches, and validates dependencies automatically.

Lesson 01 — Parcellation

The fly brain’s geography: 78 neuropil regions organised in a spatial hierarchy. This lesson builds the anatomical coordinate system that all subsequent analyses reference.

Lesson 02 — Composition

Cell type counts and neurotransmitter profiles per brain region. Statistical description of circuit heterogeneity: how many neurons of each type, what neurotransmitter they release, where they project.

Lesson 03 — Factology

Structured scientific measurements: every number earns a name. The @fact and @structural decorators create reproducible, versioned factsheets for any circuit or brain region.

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