MD-Models

Welcome to Markdown Models (MD-Models), a powerful framework for research data management that prioritizes flexibility and efficiency.

With an adaptable markdown-based schema language, MD-Models automatically generates schemas and programming language representations. This markdown schema forms the foundation for object-oriented models, enabling seamless cross-format compatibility and simplifying modifications to data structures.

Check out the documentation for more information.

Example

The schema syntax uses Markdown to define data models in a clear and structured way. Each object is introduced with a header, followed by its attributes. Attributes are described with their type, a brief explanation, and optional metadata like terms. Nested or related objects are represented using array types or references to other objects.

---
prefixes:
  schema: http://schema.org/
---

### Person

- **name**
  - Type: string
  - Description: The name of the person
  - Term: schema:name
- age
  - Type: integer
  - Description: The age of the person
  - Term: schema:age
- addresses
  - Type: Address[]
  - Description: The address of the person

### Address

- street
  - Type: string
  - Description: The street of the address

Installation

In order to install the command line tool, you can use the following command:

git clone https://github.com/JR-1991/md-models
cd md-models
cargo install --path .

Command line usage

The command line tool can be used to convert markdown files to various formats. The following command will convert a markdown file to Python code:

md-models convert -i model.md -o lib.py -l python-dataclass

This will read the input file model.md and write the output to lib.py using the Python dataclass template. Alternatively, you can also pass a URL as input to fetch the model remotely. For an overview of all available templates, you can use the following command:

md-models --help

Available templates

The following templates are available:

• python-dataclass: Python dataclass implementation with JSON-LD support
• python-pydantic: PyDantic implementation with JSON-LD support
• python-pydantic-xml: PyDantic implementation with XML support
• xml-schema: XML schema definition
• json-schema: JSON schema definition
• shacl: SHACL shapes definition
• shex: ShEx shapes definition

Development

This project uses GitHub Actions for continuous integration. The tests can be run using the following command:

cargo test
cargo clippy

Using pre-commit hooks

This project uses pre-commit to run the rustfmt and clippy commands on every commit. To install the pre-commit hooks, you can use the following command:

pip install pre-commit
pre-commit install

Once the pre-commit hooks are installed, they will run on every commit. This will ensure that the code is formatted and linted correctly. And the clippy CI will not complain about warnings.