Metaseed
Metaseed creates, edits, and validates scientific metadata against a standard, from a YAML specification of that standard.
Documentation · Introduction slides · Try it hosted · Changelog
What it does
A metadata standard such as MIAPPE or ISA is written as a profile: a YAML file that names the entity types, their fields, the parent–child hierarchy, and the validation rules. From that file, Metaseed:
- Generates Pydantic models for every entity type at runtime.
- Validates a dataset with composable rules: required fields, patterns, ranges, uniqueness, referential integrity, and conditions.
- Serializes datasets to JSON, YAML, and Excel, and back.
- Exports to the formats repositories take: ISA-Tab, ENA XML, PRIDE
submission.pxwith SDRF, DCAT, and SEEK's ISA RDF. - Pushes a dataset into a running FAIRDOM-SEEK instance, or to a shared metaseed-hub.
You work with it from a command line, a web interface, a Python API, or an MCP server for an AI agent. All four reach the same library functions, and a test fails when one of them falls behind the others.
Install
Metaseed requires Python 3.11 or later.
uv tool install metaseed
To include every integration, install metaseed[all]. To pick individual integrations, name their extras: for example, metaseed[seek,dcat] adds the FAIRDOM-SEEK and DCAT adapters. The Plugins page in the web interface lists which adapters are available.
For development:
git clone https://github.com/sorenwacker/metaseed.git
cd metaseed
make setup
Profiles
The package ships these profiles. Counts refer to each profile's latest version.
| Profile | --profile |
Versions | Entities | Fields | Domain |
|---|---|---|---|---|---|
| MIAPPE | miappe |
1.1, 1.2 | 14 | 163 | Plant phenotyping |
| ISA | isa |
1.0 | 22 | 139 | Life science investigations |
| Darwin Core | darwin-core |
1.0 | 10 | 189 | Biodiversity |
| DiSSCo | dissco |
0.4 | 16 | 261 | Digital specimens |
| ENA | ena |
1.0 | 12 | 113 | Nucleotide archive submissions |
| MetaboLights | metabolights |
1.0 | 13 | 71 | Metabolomics |
| PRIDE | pride |
1.0, 2.0 | 9 | 61 | Proteomics |
| REMBI | rembi |
1.5 | 14 | 101 | Biological imaging |
| Health-RI core | health-ri-core |
2.0 | 13 | 156 | Health data catalogues (DCAT-AP) |
| SEEK | seek |
1.0 | 24 | 229 | The FAIRDOM-SEEK data model |
| SEEK-ready template | seek-ready-template |
1.0, 2.0, 3.0 | 6 | 33 | Minimal ISA shape for a SEEK upload |
Profiles you write yourself go under ~/.local/share/metaseed/specs/. The web interface's spec builder and the metaseed spec commands author them; the explorer compares them; metaseed merge combines them.
Integrations
An adapter is a route in or out of a dataset. A profile is a standard; an adapter is a service or file format. Each adapter is a pip extra of the same name.
| Adapter | Direction | What it does |
|---|---|---|
| FAIRDOM-SEEK | push, export | Creates Sample Types and Extended Metadata on a SEEK instance and pushes a dataset as ISA content; exports SEEK-importable ISA RDF (guide) |
| Metaseed Hub | push, pull | Pushes datasets and profiles to a metaseed-hub and pulls them back, never overwriting without being asked (guide) |
| DCAT | export | Exports a dataset's catalogue record as DCAT, in JSON-LD and Turtle |
| ENA | import, export | Imports the metadata of a European Nucleotide Archive accession; exports ENA XML |
| PRIDE | import, export | Imports a PRIDE Archive project; exports submission.px and SDRF |
| MetaboLights | import | Imports a MetaboLights study document |
| BrAPI | import | Imports a BrAPI v2 server's studies into the MIAPPE profile |
Use it
Command line
The CLI is grouped by what you act on. Every group prints its own help, for example metaseed dataset --help.
metaseed profiles # the profiles and their versions
metaseed profile schema --profile miappe -v 1.2 # entity types and fields
metaseed dataset create test-drought --profile miappe -v 1.2
metaseed entity create test-drought Investigation --set unique_id=INV001 --set title="Drought trial"
metaseed dataset validate test-drought
metaseed dataset export test-drought --format dcat -o out/
metaseed ui # the web interface
metaseed mcp --transport stdio # the MCP server, for Claude Desktop
Output is JSON, so a script reads what a person reads. The CLI reference lists every command; Capability parity records which command, MCP tool, and web route serve each capability.
Python
from metaseed import MetaseedClient
client = MetaseedClient("miappe", "1.2")
investigation = client.create_entity(
"Investigation",
{"unique_id": "INV001", "title": "Drought tolerance trial"},
)
client.create_entity(
"Study",
{"unique_id": "STU001", "title": "Field trial 2024", "start_date": "2024-03-01"},
parent_id=investigation.id,
)
result = client.validate()
print(result.valid, [issue.message for issue in result.issues])
The Python API reference covers the client; the public API contract lists what is stable.
Web interface
metaseed ui serves the datasets overview, entity forms and tables, validation, the graph view, the profile explorer, the spec builder, and the Plugins page for the adapters.
MCP server
metaseed mcp exposes the same capabilities as tools for an AI agent: profile discovery, dataset and entity editing, extraction from source files, validation, ontology lookup, and specification authoring. See the MCP setup guide.
Validation
Rules are part of the profile, in YAML:
validation_rules:
- name: study_unique_within_investigation
type: uniqueness
applies_to: [Study]
field: unique_id
unique_within: parent
- name: observation_unit_names_a_study
type: referential_integrity
applies_to: [ObservationUnit]
field: study_id
reference: Study.unique_id
Rule types cover required fields, patterns, numeric and date ranges, coordinate pairs, uniqueness within a parent or globally, referential integrity, and conditions.
Architecture
graph LR
subgraph interfaces["Interfaces"]
direction RL
CLI["CLI"]
UI["Web interface"]
MCP["MCP server"]
end
subgraph core["Core"]
Client["MetaseedClient"]
Facade["ProfileFacade"]
Factory["Model factory"]
Validators["Validation engine"]
end
subgraph data["Data"]
Specs["YAML profiles"]
Repo["Entity repository"]
Storage["JSON and YAML files"]
end
interfaces --> Client
Client --> Facade
Facade --> Factory
Facade --> Validators
Factory --> Specs
Validators --> Repo
Repo --> Storage
The architecture overview describes each layer.
Development
make setup # dependencies and pre-commit hooks
make dev # the web interface with reload
make test # the test suite
make lint # ruff and mypy
make docs # the documentation site with reload
The project follows document-driven and test-driven development: a change starts in docs/, gets a test, then an implementation. Rules are enforced by tests rather than by review; the contributing guide lists them.
Data sources and attribution
Ontology lookup and validation use the EMBL-EBI Ontology Lookup Service (OLS4). Term data comes from the public OLS4 API and stays the property of the source ontologies; use of OLS is subject to the EMBL-EBI terms of use. Metaseed caches results, limits its request rate, and identifies itself with a descriptive User-Agent. For bulk term resolution, download the source ontologies or run a local OLS instance instead of using the public API.
License
1 maintainer
Metadata
1 maintainer
Release files for metaseed 0.62.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| metaseed-0.62.0.tar.gz | 946.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| metaseed-0.62.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.1 MB
Release files / metaseed-0.62.0.tar.gz
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| Uploaded via |
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Provenance
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