freeform-summary-to-galaxy-interface

---
type: research
title: "Open-requirements ledger"
tags:
  - target/galaxy
status: draft
created: 2026-06-16
revised: 2026-08-29
revision: 3
related_notes:
  - "[[galaxy-workflow-draft-format]]"
related_molds:
  - "[[advance-galaxy-draft-step]]"
  - "[[repair-galaxy-draft-topology]]"
  - "[[implement-galaxy-tool-step]]"
summary: "Carried unresolved-requirements artifact the source→Galaxy pipeline discharges or explicitly surrenders, autonomously."
---

# Open-requirements ledger

The `open-requirements-ledger` is a single artifact threaded through the source→Galaxy pipeline that records **obligations the pipeline has taken on but not yet met** — a declared output with no producer, a parameter whose value the source never pinned, a tool with no corpus exemplar — and **source work it decided not to carry**. Each Mold that surfaces one **appends** it; each Mold whose decision closes one **marks it resolved**; the terminal path **surrenders** whatever remains open, explicitly, into the final artifact.

## Framing: obligations the pipeline discharges, not questions a human answers

This is deliberately *not* an "open questions for the user" list. The pipeline is autonomous — no human-in-the-loop gate is assumed. The ledger's consumers are **Molds and the loop's convergence gate**, with human readout a secondary affordance. An entry is closed by a downstream Mold doing work (wiring a producer, picking a wrapper, settling a value), or — when nothing can close it — surrendered: written into the final draft as a known, labelled gap rather than silently dropped or fabricated around.

The distinction matters because a "questions for a human" framing leaks an operator's personal interaction style into a tool meant to run inside anyone's harness. The ledger must behave identically
...

---
type: research
title: "Galaxy collection semantics"
tags:
  - target/galaxy
status: draft
created: 2026-04-30
revised: 2026-05-05
revision: 3
license: MIT
license_file: LICENSES/galaxy.LICENSE
related_notes:
  - "[[galaxy-xsd]]"
  - "[[galaxy-collection-tools]]"
  - "[[galaxy-apply-rules-dsl]]"
  - "[[nextflow-to-galaxy-channel-shape-mapping]]"
  - "[[nextflow-operators-to-galaxy-collection-recipes]]"
  - "[[galaxy-tool-job-failure-reference]]"
  - "[[galaxy-workflow-invocation-failure-reference]]"
  - "[[iwc-transformations-survey]]"
  - "[[galaxy-discover-datasets]]"
sources:
  - "https://github.com/galaxyproject/galaxy/blob/7765fae934fbfdee77e3be5f5b235e43735273ae/lib/galaxy/model/dataset_collections/types/collection_semantics.yml"
companions:
  - "galaxy-collection-semantics.yml"
summary: "Vendored formal spec of Galaxy dataset-collection mapping/reduction semantics, with labeled examples and pinned test references."
---

> **Vendored from upstream**, pinned at SHA `7765fae`. One file travels with this note:
>
> - `galaxy-collection-semantics.yml` — the structured source. **Agents and casting should consume this.** It carries the `tests:` blocks that pin concrete Galaxy test names; the rendered upstream view drops them.
>
> The Foundry also vendors Galaxy's auto-generated MyST/LaTeX rendering of the same YAML, which
> is what the human view below renders from. It is a site asset: casting does not carry it, so
> do not look for it beside this note in a skill bundle. Sync is manual.
>
> **When to consult:** authoring or reasoning about Molds and patterns that touch `data_collection` inputs, map-over / reduction shape changes, sub-collection mapping, `paired_or_unpaired`, or `sample_sheet`.

```vendored-myst
file: galaxy-collection-semantics.upstream.myst
source: h
...

- doc: |
    # Collection Semantics

    This document describes the semantics around working with Galaxy dataset collections.
    In particular it describes how they operate within Galaxy tools and workflows.

    :::{admonition} You Probably Don't Need to Read This
    :class: caution

    Any significantly sophisticated workflow language will have ways to collect data
    into arrays or vectors or dictionaries and apply operations across this data (mapping)
    or reduce the dimensionality of this data (reductions). Typically, this is explicitly
    annotated with map functions or for loops. Galaxy however is designed to be a point
    and click interface for connecting steps and running tools. It is important that steps
    just connect and just do the most natural thing - and this is what Galaxy does.
    This document just provides a mathematical formalism to that "what should just
    intuitively work" that can be used to document test cases and help with implementation.
    This is reference documentation not user documentation, Galaxy should just work.
    :::

    ## Mapping

    If a tool consumes a simple dataset parameter and produces a simple dataset parameter,
    then any collection type may be "mapped over" the data input to that tool. The result of
    that is the tool being applied to each element of the collection and "implicit collections"
    being created from the outputs that are produced from those operations. Those implicit
    collections have the same element identifiers in the same order as the input collection that is
    mapped over. Each element of the implicit collections correspond to their own job and
    Galaxy very naturally and intuitively parallelizes jobs without extra work from the user
    and without any knowledge of the tool.

...

---
type: research
title: "Galaxy sample_sheet collection types"
tags:
  - target/galaxy
status: draft
created: 2026-05-05
revised: 2026-05-06
revision: 2
related_notes:
  - "[[galaxy-collection-semantics]]"
  - "[[galaxy-collection-tools]]"
  - "[[nextflow-workflow-io-semantics]]"
  - "[[nextflow-params-to-galaxy-inputs]]"
  - "[[nextflow-path-glob-to-galaxy-datatype]]"
  - "[[nextflow-to-galaxy-channel-shape-mapping]]"
  - "[[nextflow-to-galaxy-reference-data-mapping]]"
related_molds:
  - "[[nextflow-summary-to-galaxy-interface]]"
  - "[[nextflow-summary-to-galaxy-data-flow]]"
sources:
  - "Galaxy PR #19305 (Implement Sample Sheets), merged 2025-07-30"
  - "lib/galaxy/model/dataset_collections/types/sample_sheet.py"
  - "lib/galaxy/model/dataset_collections/types/sample_sheet_util.py"
  - "lib/galaxy/model/dataset_collections/type_description.py"
  - "lib/galaxy/schema/schema.py (SampleSheetColumnDefinition, SampleSheetRow)"
  - "lib/galaxy/tools/wrappers.py (DatasetCollectionWrapper.sample_sheet_row)"
  - "lib/galaxy/tools/sample_sheet_to_tabular.xml"
  - "lib/galaxy/webapps/galaxy/api/dataset_collections.py (sample_sheet_workbook endpoints)"
  - "lib/galaxy/model/migrations/alembic/versions_gxy/3af58c192752_implement_sample_sheets.py"
summary: "Galaxy's sample_sheet collection family: typed column metadata, four variants, mapping rules, validator allowlist."
---

# Galaxy sample_sheet collection types

Reference for the Galaxy backend shape that targets structured per-row metadata — the natural landing zone for Nextflow `samplesheetToList` parameters and for any source-side idiom that pairs typed metadata columns with dataset references.

## Shape

A `sample_sheet` is a list-shaped collection where each element carries a typed `columns` row, and the parent collectio
...

---
type: research
tags:
  - target/galaxy
status: draft
created: 2026-05-03
revised: 2026-09-23
revision: 3
related_notes:
  - "[[iwc-workflow-testability-survey]]"
  - "[[iwc-test-data-conventions]]"
  - "[[planemo-asserts-idioms]]"
  - "[[iwc-shortcuts-anti-patterns]]"
  - "[[planemo-workflow-test-architecture]]"
  - "[[implement-galaxy-workflow-test]]"
  - "[[gxformat2-schema]]"
  - "[[gxformat2-workflow-inputs]]"
  - "[[galaxy-datatypes-conf]]"
summary: "Design guidance for Galaxy workflow inputs, outputs, and checkpoints that make IWC-style workflow tests possible."
---

# Design a Galaxy workflow that can be tested

Choose the workflow's public inputs and outputs while designing the analysis, before writing its `-tests.yml`. A workflow test supplies `job:` values by input name and checks `outputs:` by output name. It can only check results the workflow exposes. A good interface gives the test a reproducible input, an addressable result, and at least one assertion that would catch a meaningful wrong result.

This note concerns workflow design. [[iwc-test-data-conventions]] covers the test file's input fixtures, [[planemo-asserts-idioms]] covers assertion syntax, and [[iwc-shortcuts-anti-patterns]] explains when a smoke check is enough. [[iwc-workflow-testability-survey]] holds the broader corpus evidence.

## Name the public interface before writing tests

Give each input and promoted output a stable, descriptive public name. In an exported gxformat2 workflow this is commonly the entry's `id`. The structural [[gxformat2-schema]] also permits `label`. Check the **effective name in the workflow being tested** instead of assuming a step label, generated dataset name, or array position will become the test key. In the pinned [Scanpy workflow](https://github.com/galaxy
...