Speakers
Description
Modern research is heavily dependent on software. The landscape of research software engineering is evolving at a high pace, and the effective handling of metadata plays a pivotal role in ensuring software discoverability, reproducibility, and general project quality. Properly curating metadata can, however, become a time-consuming task, while manual curation is error prone at the same time. This poster introduces two new tools for streamlining metadata management: fair-python-cookiecutter and somesy.
The fair-python-cookiecutter is a GitHub repository template which provides a structured foundation for Python projects. The template provides researchers and RSEs with support in meeting the increasing demands for software metadata during development of Python tools and libraries. By cloning and applying the template to their projects, developers can benefit from the incorporated best practices, recommendations for software development, and software project metadata to ensure quality and facilitate citation of their work. The fair-python-cookiecutter is aligned with and inspired by standards like DLR Software Engineering Guidelines, OpenSSF Best Practices, REUSE, CITATION.cff, CodeMeta. Furthermore, it uses somesy to enhance software metadata FAIRness. The template comes with detailed documentation and thus offers an accessible framework for achieving software quality and discoverability within academia.
Somesy (software metadata synchronization) provides a user-friendly command-line interface that assists in synchronization of software project metadata. Somesy supports best-practice metadata standards such as CITATION.cff and CodeMeta and automatically maintains metadata, such as essential project information (names, versions, authors, licenses), consistently across multiple files. This ensures metadata integrity and frees additional time for developers and maintainers to focus on their work.
https://github.com/Materials-Data-Science-and-Informatics/fair-python-cookiecutter
https://pypi.org/project/somesy/
Acknowledgements: The presented content was created by the FAIR data commons & Hub Information of the Helmholtz Metadata Collaboration (HMC) at Forschungszentrum Jülich. HMC is an Incubator platform of the Helmholtz Association within the framework of the Information and Data Science strategic initiative
