Background: Software is at the core of most scientific discoveries today, and the reliability of research results increasingly depends on the quality of the software that underpins them. However, research software is often developed under constraints that prioritize scientific progress over engineering rigor, leaving little to no incentive for maintenance, documentation, or quality assurance. Objective: This study examines whether embedding an existing research software into a software testing course can contribute to improving the quality of the research software and identifies the associated challenges. Method: In an in vivo experiment, we embedded a large-scale network simulation into a university course on software testing at Blekinge Institute of Technology, Sweden, as a group project and observed the effects on the research software. Results: We found that the research software benefited from the embedding through substantially improved documentation and fewer hardware and software dependencies. However, the embedding required significant additional effort from us, and although the student teams produced thoughtful and well-designed test suites, none of their code contributions could be merged into the research software due to uncertainties around intellectual property. Conclusion: We strongly believe that embedding research software engineering activities into teaching can enhance the quality of research software while providing students with exposure to research. However, the uncertainty about the intellectual property of students’ code contributions substantially limits its potential.

Keystroke logging has become a widely adopted method in writing process research and translation studies, offering researchers detailed insights into the development of texts—particularly through the analysis of pauses and revisions. Currently, Inputlog is the most commonly used keystroke logging tool, recording all keystrokes and mouse activity, while adding a timestamp to each of these activities. While Inputlog was mainly designed to log writing in MS Word, we have now developed an add-on for LibreOffice, so called ‘Inputlog-LibreOffice’. This add-on enables unobtrusive observation of digital writing processes. It is a tool for registration of the writing process within a LibreOffice Writer document. The xml output files can be analysed either in the desktop Inputlog application or through other tools. As LibreOffice is an open-source platform, it offers greater control over the logging process compared to Microsoft Word. This enhanced transparency proves particularly valuable for conducting detailed and reliable revision analyses. The source code is publicly available.

Absenteeism among elementary school children has proven useful for early detection of influenza epidemics within a population. This paper presents Detecting Epidemics from School Absenteeism (DESA), an R package developed to: (1) model epidemics using school absenteeism data, (2) raise alerts for potential outbreaks, (3) evaluate the timeliness of alerts using multiple metrics, and (4) simulate household populations, epidemics, and absenteeism patterns to support related research. DESA provides researchers and public health officials with a practical tool for improving early detection of seasonal influenza and other infectious diseases. The package is freely available on CRAN and GitHub for the R community.