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Software is essential for scientific research and is applied in research methodology, data analysis, and knowledge dissemination. Scientists believe that software plays a crucial role in their research process, so it is necessary to recognize the contribution of software developers. This study aims to investigate the current situation of software citation in scientific research and explore possible solutions to improve its sustainability via full-text content analysis. There is a gap between the use and the citation of software in scientific publications, and the lack of information in software citations has been a source of both failure and improvement of software in scientific research. We found various deficiencies that hinder the durability of software citations and put forward some suggestions for their stability and development, such as designing software citation standards, promoting free open licensing of scientific software, and implementing strict peer review of software citations. [ABSTRACT FROM AUTHOR]

Science across all disciplines has become increasingly data-driven, leading to additional needs with respect to software for collecting, processing and analysing data. Thus, transparency about software used as part of the scientific process is crucial to understand provenance of individual research data and insights, is a prerequisite for reproducibility and can enable macro-analysis of the evolution of scientific methods over time. However, missing rigor in software citation practices renders the automated detection and disambiguation of software mentions a challenging problem. In this work, we provide a large-scale analysis of software usage and citation practices facilitated through an unprecedented knowledge graph of software mentions and affiliated metadata generated through supervised information extraction models trained on a unique gold standard corpus and applied to more than 3 million scientific articles. Our information extraction approach distinguishes different types of software and mentions, disambiguates mentions and outperforms the state-of-the-art significantly, leading to the most comprehensive corpus of 11.8 M software mentions that are described through a knowledge graph consisting of more than 300 M triples. Our analysis provides insights into the evolution of software usage and citation patterns across various fields, ranks of journals, and impact of publications. Whereas, to the best of our knowledge, this is the most comprehensive analysis of software use and citation at the time, all data and models are shared publicly to facilitate further research into scientific use and citation of software.