RESUMEN
In this paper, we investigate progress toward improved software citation by examining current software citation practices. We first introduce our machine learning based data pipeline that extracts software mentions from the CORD-19 corpus, a regularly updated collection of more than 280,000 scholarly articles on COVID-19 and related historical coronaviruses. We then closely examine a stratified sample of extracted software mentions from recent CORD-19 publications to understand the status of software citation. We also searched online for the mentioned software projects and their citation requests. We evaluate both practices of referencing software in publications and making software citable in comparison with earlier findings and recent advocacy recommendations. We found increased mentions of software versions, increased open source practices, and improved software accessibility. Yet, we also found a continuation of high numbers of informal mentions that did not sufficiently credit software authors. Existing software citation requests were diverse but did not match with software citation advocacy recommendations nor were they frequently followed by researchers authoring papers. Finally, we discuss implications for software citation advocacy and standard making efforts seeking to improve the situation. Our results show the diversity of software citation practices and how they differ from advocacy recommendations, provide a baseline for assessing the progress of software citation implementation, and enrich the understanding of existing challenges.
RESUMEN
Background: Software is now ubiquitous within research. In addition to the general challenges common to all software development projects, research software must also represent, manipulate, and provide data for complex theoretical constructs. Ensuring this process of theory-software translation is robust is essential to maintaining the integrity of the science resulting from it, and yet there has been little formal recognition or exploration of the challenges associated with it. Methods: We thematically analyse the outputs of the discussion sessions at the Theory-Software Translation Workshop 2019, where academic researchers and research software engineers from a variety of domains, and with particular expertise in high performance computing, explored the process of translating between scientific theory and software. Results: We identify a wide range of challenges to implementing scientific theory in research software and using the resulting data and models for the advancement of knowledge. We categorise these within the emergent themes of design, infrastructure, and culture, and map them to associated research questions. Conclusions: Systematically investigating how software is constructed and its outputs used within science has the potential to improve the robustness of research software and accelerate progress in its development. We propose that this issue be examined within a new research area of theory-software translation, which would aim to significantly advance both knowledge and scientific practice.
