R wrapper around the EasyNMT python library, providing "Easy to use, state-of-the-art Neural Machine Translation for 100+ languages" locally. - thieled/easieRnmt

The Weizenbaum Journal of the Digital Society (WJDS), launched in 2021, has been accepted for inclusion in Scopus and will receive its first CiteScore in 2026. This marks an important step in the jour...

Abstract. When assessing media effects, we seldom consider how they change over time. Especially with the prevalence of smartphone use as short-lived, frag

As smartphones become increasingly integral to daily life, their importance for understanding human behavior will only continue to grow. Recognizing the potential of objective data on smartphone usage...

Smartphones have become popular tools for data collection in the social sciences due to their high prevalence and mobility. Surveys, experience sampling (ESM) and tracking/logging are among the most used smartphone data-collection methods. However, existing apps are either commercial solutions, require programming skills, collect sensitive data, or do not handle all three methods simultaneously. When two or more data collection methods are used simultaneously, it further burdens both researchers and participants. This paper introduces the app MART (Mobile Assessment Research Tool) that solves these problems and is available for Android and iOS devices. Content and data collection settings can be customized dynamically via a web interface without the need to compile a new version of the app when changes are made. While the logging functionality is only supported on Android devices, data donation via the app Screen Time is requested on iOS devices. MART is already functional, and the source code is open-source and available on GitHub. The necessary long-term revisions for its use in custom projects without reprogramming are currently under development.