Software is an integral part in much of today's research which naturally poses the question how to publish and archive software as an important part of research results. Often research software is developed by researchers. The software itself may even be the subject of the research. The reproducibility of results obtained with such software (as an integral part of the scientific process) can be more efficiently achieved--researchers never have enough time! --by following a few basic rules during development and publishing. Discussing best practices, I will argue, that following guidelines has the added benefit of aiding the robustness of the research process itself. Moreover, I will try to make the point that for practical reasons, thinking about reusability of developed research software, rather than reproducibility of results, may be a more natural way to create research software that advances the scientific field of interest. Finally, I will discuss several "modes" of publishing software and how they may create a sustainable development cycle. This discussion includes some experience we gained in a recently completed project in collaboration with an interdisciplinary team of researchers from the University of Stuttgart.
The sustainability of research in the domain of data-integrated simulation science is intricately linked to a viable interactive archival infrastructure for research data. Establishing a framework (namely a dataset) by linking and referencing research data and its sources within widely distributed archival ecosystems, and then implementing persistent identifiers to that framework, strengthens the foundation of reproducible research. Precise and descriptive metadata, coupled with domain-specific ontologies, functions as a guiding light in this context. Persistently accessible data is not only a key factor towards achieving reproducible research but also facilitates collaboration, promoting data integrity, enabling peer review, and fostering knowledge preservation. During this talk, a comprehensive collection of guidelines and best practices will be outlined, which provides a methodical and organized approach for archiving, thereby enhancing crucial aspects of achieving FAIR (Findable, Accessible, Interoperable, and Reusable) research standards. We will also provide a recommendation on selecting a data repository that aligns with these guidelines. Additionally, we will emphasize how standardized metadata improves the findability and understanding of research outcomes, underscoring its importance within this context.