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MEx template

Cookiecutter template for MEx python projects.

open-code

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.

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.

package

The mex-template is a cookiecutter template to make starting a new python project easier and to keep boilerplate files harmonized using cruft.

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.

usage

installation

usage

new project

  • starting a new project using this template is easy
  • go to your home / code folder
  • run cruft create https://github.com/robert-koch-institut/mex-template
  • fill in the prompted values

update existing

  • there are two tools to help you update the boilerplate of existing projects
  • go to your local checkout of a project using this template
  • cruft diff shows you the difference between the template an your local files
  • cruft update will attempt to update the boilerplate files and content automatically

Footnotes

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