MEx common

Common library for MEx python projects.

project

The Metadata Exchange (MEx) project is committed to improve the retrieval of RKI research data and projects. How? By focusing on metadata: instead of providing the actual research data directly, the MEx metadata catalog captures descriptive information about research data and activities. On this basis, we want to make the data FAIR¹ so that it can be shared with others.

Via MEx, metadata will be made findable, accessible and shareable, as well as available for further research. The goal is to get an overview of what research data is available, understand its context, and know what needs to be considered for subsequent use.

After an internal launch, the metadata will also be made publicly available and thus be available to external researchers as well as the interested (professional) public to find research data from the RKI.

For further details, please consult our project page.

package

The mex-common library is a software development toolkit that is used by multiple components within the MEx project. It contains utilities for building pipelines like a common commandline interface, logging and configuration setup. It also provides common auxiliary connectors that can be used to fetch data from external services and a re-usable implementation of the MEx metadata schema as pydantic models.

license

This package is licensed under the MIT license. Other components of the MEx project will be released under the same license in the future.

development

installation

• on unix, consider using pyenv https://github.com/pyenv/pyenv
  • get pyenv curl https://pyenv.run | bash
  • install 3.11 pyenv install 3.11
  • create env pyenv virtualenv 3.11 mex
  • go to repo root
  • use env pyenv local mex
  • run make install
• on windows, see https://python-poetry.org/docs/managing-environments
  • install python3.11 in your preferred way
  • go to repo root
  • run .\mex.bat install

linting and testing

• on unix run make test
• on windows run .\mex.bat test
• or run manually
  • linter checks via pre-commit run --all-files
  • all tests via poetry run pytest

updating dependencies

• update global dependencies in requirements.txt manually
• update git hooks with pre-commit autoupdate
• show outdated dependencies with poetry show --outdated
• update dependencies in poetry using poetry update --lock
• update github actions manually in .github\workflows\default.yml

Footnotes

1. FAIR is referencing the so-called FAIR data principles – guidelines to make data Findable, Accessible, Interoperable and Reusable. ↩