MD-Models: Human-Readable, Model-Driven Specifications for FAIR Research Data

Version 1.0
Range, Jan Peter; Pleiss, Jürgen, 2026, "MD-Models: Human-Readable, Model-Driven Specifications for FAIR Research Data", https://doi.org/10.18419/DARUS-5831, DaRUS, V1
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Description

This dataset accompanies MD-Models, a model-driven framework that lets domain experts define structured, machine-readable research data models in plain Markdown. A single human-readable specification serves as the authoritative source, from which technical schemas, programming libraries in five languages, relational and graph database layers, validation logic, and interfaces for LLM-assisted data ingestion are automatically generated. The framework interoperates with existing ecosystems through bidirectional LinkML import/export and has been adopted by two community standards in biocatalysis.

The contents demonstrate the full workflow on a small example domain, which is an enzymatic Experiment model (molecules, enzymes, reaction conditions), and provide a runnable local stack (database, REST API, and MCP server) showing how a single Markdown model powers data storage, programmatic access, and LLM-assisted data entry.

What's inside

  • model.md — the single source of truth: the data model written in plain Markdown.
  • gen.toml — generation config mapping the model to all generated artifacts.
  • code/ — programming libraries auto-generated from the model: Python (dataclass, Pydantic, Pydantic-XML), TypeScript (plain and Zod), Rust, Go, and Julia.
  • schemes/ — technical schemas: JSON Schema, XML Schema (XSD), Protobuf, GraphQL, and LinkML.
  • diagrams/ — a Mermaid class diagram of the model.
  • config.toml, docker-compose.yml, Dockerfile.*, start.sh — the local backend stack: PostgreSQL + pgvector, a REST API, and an MCP server.
  • UploadDataset.ipynb — a Jupyter notebook that creates and lists datasets through the REST API.
  • chats/ — example Claude Desktop conversations using the MCP server to create and query experiments.
  • README.md — full setup and usage instructions.

How to use it

  1. Inspect the model. Open model.md to see the authoritative specification, and browse code/ and schemes/ for the artifacts generated from it.
  2. Regenerate artifacts. With the mdmodels toolkit, run the generator against gen.toml to reproduce every library, schema, and diagram from model.md.
  3. Run the local stack. With Docker installed, run ./start.sh to launch the database, REST API (http://localhost:8800), and MCP server (http://127.0.0.1:7001/mcp).
  4. Add and query data. Use UploadDataset.ipynb to create and list Experiment datasets via the REST API, or connect Claude Desktop to the MCP server for LLM-assisted data entry and querying (see README.md for the configuration).

For complete prerequisites, service verification steps, and example commands, see README.md in the dataset.

Subject Computer and Information Science
Keyword Model-Driven Engineering, Research Data Management, Markdown, Code Generation, Biocatalysis, Large Language Models, Databases, Model Context Protocol, Vector Database
License/Data Use Agreement

MIT License

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model.md
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README.md
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start.sh
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Curation Status History
StatusDateAssigner
2026-06-02 12:35:06Jan Range
Waiting for Feedback from Author2026-06-02 08:35:53Dataverse Admin
In Review by FoKUS2026-06-01 07:46:04Dataverse Admin
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