mex-artificial 0.5.1

Create artificial data for the MEx project.

MEx artificial

Create artificial data for the MEx project.

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 FAIR[^1] 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.

RKI cooperated with D4L data4life gGmbH for a pilot phase where the vision of a FAIR metadata catalog was explored and concepts and prototypes were developed. The partnership has ended with the successful conclusion of the pilot phase.

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.

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

Contact
For more information, please feel free to email us at [e-mail].

Publisher

Robert Koch-Institut
Nordufer 20
13353 Berlin
Germany

Package

Create artificial extracted items, transform them into merged items and write the results into a configured sink.

License

This package is licensed under the MIT license. All other software components of the MEx project are open-sourced under the same license as well.

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
  • switch version pyenv global 3.11
  • run make install
• on windows, consider using pyenv-win https://pyenv-win.github.io/pyenv-win/
  • follow https://pyenv-win.github.io/pyenv-win/#quick-start
  • install 3.11 pyenv install 3.11
  • switch version pyenv global 3.11
  • run .\mex.bat install

Linting and testing

• run all linters with pdm lint
• run only unit tests with pdm unit
• run unit and integration tests with pdm test

Updating dependencies

• update boilerplate files with cruft update
• update global requirements in requirements.txt manually
• update git hooks with pre-commit autoupdate
• update package dependencies using pdm update-all
• update github actions in .github/workflows/*.yml manually

Creating release

• run pdm release RULE to release a new version where RULE determines which part of the version to update and is one of major, minor, patch.

Container workflow

• build image with make image
• run local version using docker make run

Pre-built workflow

• you can run the latest artificial data generator without building it locally
• just pull it from the container registry and configure using cli arguments
• docker run -v $(pwd):/out ghcr.io/robert-koch-institut/mex-artificial:latest --count=100 --chattiness=10
• use -v $(pwd):/out to specify an output directory for the resulting ndjson file
• --count controls the number of items to generate
• --chattiness controls the number of words in textual fields

Commands

• run pdm run artificial --help to print instructions