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Contributing

First, thank you for taking the time to contribute to the PEtab-SciML Benchmark Collection. Contributions help advance SciML method development and benchmarking for the wider community.

Ways to contribute

We welcome contributions of new benchmark models.

Adding a new benchmark model

To add a new benchmark model, please make sure that the following checklist is fulfilled.

  • The PEtab-SciML problem is based on a peer-reviewed and published model.
  • The problem ID follows the format {LAST_NAME_OF_FIRST_AUTHOR}_{ABBREVIATED_JOURNAL_NAME}{YEAR_OF_PUBLICATION}.
  • The problem ID is included in the pull request title.
  • There is a GitHub issue for the problem.
    • The problem ID is included in the issue title.
    • The issue contains a brief model description.
    • The issue and pull request are linked to each other.
    • Differences between the implementation and the original publication are described.
    • The source of the nominal parameters is stated, for example whether they were taken from the original publication or obtained by fitting.
  • The SBML file is included.
    • The SBML file is annotated with a reference to the original publication.
    • The SBML model id and name attributes match the problem ID.
  • The PEtab-SciML files are included.
    • The PEtab-SciML problem is valid according to the PEtab-SciML linter.
    • An expected.yaml file is provided with the expected likelihood/objective function value for the provided nominal parameter values.
  • The main repository README has been updated.
    • The model is included in the overview table.
    • The overview table includes the model ID, hybridization form, number of estimated parameters, number of mechanistic parameters, number of ML parameters to estimate, and number of ODE states.
  • The benchmark model directory contains a brief README with:
    • the reference to the original publication,
    • a short model description,
    • notes on relevant differences from the original publication, if any.

Although PEtab-SciML builds on PEtab v2 and supports model formats beyond SBML, this benchmark collection currently accepts only SBML-based models, as SBML is the most widely used format among tools supporting PEtab-SciML.