Concrete is a material favored by architects and builders alike due to its high structural strength and its ability to take almost any form. However, to shape concrete structures, heavy-duty formwork is usually necessary to support the fresh concrete while curing. To expand geometrical freedom, three-dimensional (3D) printed concrete formwork has emerged as a field of research. This article presents one possible application, a novel fabrication process that combines large-scale robotic fused deposition modeling 3D printing with simultaneous casting of a fast-hardening, set-on-demand concrete. This fabrication process, known as ''Eggshell,'' enables the production of nonstandard concrete structures in a material-efficient process. By casting a fast-hardening concrete in a continuous process, lateral pressure exerted by the fresh concrete is kept to a minimum. In this way, a 1.5-mm-thin thermoplastic shell can be used as a formwork, without any additional support. Geometries of different scales are tested in this article to evaluate the feasibility of the Eggshell fabrication process in an architectural context. An array of printing materials are also tested, and several different reinforcement concepts are analyzed. The findings are used to produce a full-scale architectural demonstrator project. This article shows that a wide range of concrete geometries can be produced in a material-efficient fabrication process, paving the way toward mass customization and structural optimization within concrete architecture.

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News articles covering policy issues are an essential source of information in the social sciences and are also frequently used for other use cases, e.g., to train NLP language models. To derive meaningful insights from the analysis of news, large datasets are required that represent real-world distributions, e.g., with respect to the contained outlets' popularity, topically, or across time. Information on the political leanings of media publishers is often needed, e.g., to study differences in news reporting across the political spectrum, which is one of the prime use cases in the social sciences when studying media bias and related societal issues. Concerning these requirements, existing datasets have major flaws, resulting in redundant and cumbersome effort in the research community for dataset creation. To fill this gap, we present POLUSA, a dataset that represents the online media landscape as perceived by an average US news consumer. The dataset contains 0.9M articles covering policy topics published between Jan. 2017 and Aug. 2019 by 18 news outlets representing the political spectrum. Each outlet is labeled by its political leaning, which we derive using a systematic aggregation of eight data sources. The news dataset is balanced with respect to publication date and outlet popularity. POLUSA enables studying a variety of subjects, e.g., media effects and political partisanship. Due to its size, the dataset allows to utilize data-intense deep learning methods.