ome-iris 0.0.3

A tiny open bioimage dataset catalog with fetch and verify workflows.

OME-IRIS

OME-IRIS is an open bioimage dataset catalog for benchmarking image input/output (IO), transformations, metadata management, and bioimage-linked workflows.

We also provide a small Python package by the same name (ome_iris) to help fetch and validate the datasets in the catalog.

Inspired by both the classic iris.csv dataset and the iris of the eye that brings images into focus, OME-IRIS aims to provide a collection of reference datasets for evaluating interoperable bioimage data formats, tools, and workflows.

What this is

• A lightweight manifest catalog for small benchmark datasets
• A fetch + verify workflow with a single CLI
• LinkML-based schema definitions for dataset manifests

What this is not

• Not a data portal
• Not DVC-based
• Not a large-file git storage approach
• Not a full ontology or end-to-end benchmark system yet

Quick start

uv run ome-iris fetch --tier small
uv run ome-iris verify
uv run ome-iris export-rocrate --dataset nf1-cellpainting-shrunken

Fetch output modes:

uv run ome-iris fetch --tier small --verbose  # show per-file labels + downloader progress
uv run ome-iris fetch --tier small --silent   # suppress downloader progress output

What fetch does

High-level flow when you run ome-iris fetch:

1. Loads dataset manifests from --manifests-dir.
2. Applies optional filters (--dataset, --tier).
3. Creates local dataset roots under --data-dir/<source_identifier>/.
4. Writes ro-crate-metadata.json into each dataset root.
5. Iterates over each files entry:
   • for kind: file: downloads the file URL (or skips if already present)
   • for kind: directory: traverses/downloads directory contents (or extracts archive sources)
6. Reports a summary:
   • downloaded count + item list
   • skipped count + item list
   • missing URLs
   • failed downloads

Output layout example:

data/
  NF1_cellpainting_data_shrunken/
    ro-crate-metadata.json
    profiles.parquet
    images/
    masks/

Local files are stored under ./data/ by default. Each dataset directory also gets ro-crate-metadata.json with source/provenance metadata from the manifest.

To use another data directory:

uv run ome-iris fetch --data-dir /tmp/ome-iris-data
uv run ome-iris verify --data-dir /tmp/ome-iris-data

Add a dataset

1. Add or update a dataset manifest and catalog metadata.
2. Include source, formats, and file-level metadata.
3. Run:

uv run ome-iris verify

Starter scaffolding command:

uv run ome-iris scaffold --source-path /path/to/JUMP_plate_BR00117006
uv run ome-iris scaffold --source-path /path/to/JUMP_plate_BR00117006 --append-csv
uv run ome-iris scaffold --source-path /path/to/JUMP_plate_BR00117006 --include-directory-entry --directory-path images --archive-format zip

The command guesses a dataset id/name/formats, writes a starter YAML manifest, and prints a suggested datasets.csv row.

File entry patterns

• source_identifier is required at the top level of each manifest.
• All files[].path values are relative to data/<source_identifier>/.
• sha256 is optional for file entries.
• Use kind: directory to fetch everything under a directory source.
  • For GitHub tree URLs (https://github.com/<owner>/<repo>/tree/<ref>/<path>), OME-IRIS traverses files under that subtree.
  • For local directory paths, OME-IRIS recursively copies files.
  • For archive URLs, set archive_format (zip or tar) to extract an archive into the destination directory.

Relationships

Use an optional top-level relationships list to describe links between dataset components.

• from: source file path (must match a files[].path)
• to: target file path (must match a files[].path)
• type: relationship label (for example links_to_images_by, links_to_masks_by, references_metadata)
• rocrate_predicate: explicit RO-Crate/JSON-LD predicate URI for export (required)
• via_columns (optional): explicit table columns used for linking
• filename_patterns (optional): standardized filename templates used by the relationship
• derived_from_columns (optional): columns used when deriving one component from another (for example images -> masks)

Example:

files:
  - path: profiles.parquet
  - path: images
    kind: directory

relationships:
  - from: profiles.parquet
    to: images
    type: links_to_images_by
    rocrate_predicate: http://schema.org/associatedMedia

Example directory entry:

files:
  - path: jump-plate/images
    kind: directory
    archive_format: zip
    url: https://example.org/jump-plate-images.zip
    sha256: ""  # optional

Custom metadata (first-class)

OME-IRIS supports custom metadata as a first-class field via custom_metadata objects at manifest, source, and file levels.

Rules:

• custom_metadata must be an object/map.
• Keys must be strings.
• Values may be strings, numbers, booleans, null, lists, or nested objects.

Example:

id: jump-plate
source_identifier: JUMP_plate_BR00117006
name: JUMP plate BR00117006 (JUMP_plate_BR00117006) example
description: Plate-level cell painting benchmark subset.
tier: small
license: CC-BY-4.0
custom_metadata:
  study: jump-cp
  species: human
source:
  repository: https://example.org/repo
  path: datasets/JUMP_plate_BR00117006
  url: https://example.org/repo/tree/main/datasets/JUMP_plate_BR00117006
formats: [csv, tiff]
files:
  - path: profiles.csv
    url: https://example.org/files/profiles.csv
    sha256: "..."
    custom_metadata:
      role: profile_table

Why large files are not committed

Large image/profile files make repositories slow and fragile for contributors and CI. OME-IRIS tracks metadata and download locations, while actual data is fetched locally when needed.

Documentation

Build docs locally:

uv sync --group docs
uv run --frozen sphinx-build docs/src docs/build