The atlas page answers four questions: which public dataset can answer my question; what tool I need at this pipeline stage; how to evaluate a segmentation method; how to add something to the atlas. Specifications there are as published and approximate where releases have been revised — always confirm current figures and access terms against the primary source before citing, because dataset sizes change as proofreading continues.
Instructor script: "Before proposing new acquisition, check the atlas. A large fraction of good connectomics questions can be answered by re-analysing existing public data. Acquiring a new mm-scale volume is a multi-year, multi-million-dollar program; re-analysing one is a compute bill and your time." Quick map from the page's choosing table: whole-brain graph structure -> FlyWire or larval Drosophila; clean cell-type connectivity -> hemibrain; structure–function -> MICrONS; human-specific features -> H01; development -> the C. elegans developmental series; what dense reconstruction requires -> Kasthuri 2015.
The atlas page is explicit that this panel is deck context and nothing in it should be cited. Use it to rehearse the habit the atlas asks for: volume sizes, cell counts and synapse counts in this field are release-dependent, so confirm every specification against the primary source (Unit 04 §2).
The known_limits field is the one most often left blank and the one most worth filling. Instructor script: "An atlas of tools without limits is advertising." Pick one resource from the page's software table — CloudVolume, caveclient, CATMAID, DotMotif — and write its limit sentence together before the activity.
Use the panel to motivate the modality and effective_resolution fields: a dataset that pairs functional imaging with EM reconstruction (MICrONS is the reference case today) needs its entry to say which product a user is being pointed at — the calcium traces, the connectivity matrix, or the volume — because access, licensing and maturity differ between them.
The page's field names, for learners who will contribute: citation (with DOI), workflow_stage, species and brain_region, modality and effective_resolution, dataset_or_code_access (open / registration / request), maturity (concept / validated prototype / production-validated), known_limits, mapped_units.
Two principles from the page worth stressing. Mark superseded methods as historical rather than deleting them when they remain pedagogically useful — the history of a method often explains its assumptions. And every entry links to at least one unit, so the atlas stays connected to teaching rather than becoming an orphaned bibliography.
A pairing that works: a benchmark leaderboard entry against a released dataset with a client library. Benchmarks on small, well-prepared volumes systematically overstate performance on production data; a released dataset with a pinned version and a client is something a learner can act on tomorrow.
"Strong" on the page's rubric: the limitation sentence is specific enough that a reader could predict a failure case from it, and the unit mapping says where in that unit's workflow the resource fits.