Gianna-Carina Grün Data Journalism & Information Visualization

Gianna-Carina Grün is a Berlin based data journalist and associated researcher at the Urban Complexity Lab. Her research as a PhD candidate TU Dortmund focuses on how data journalism practitioners can support the interpretability of multidimensional charts.

She holds a Masters degree in Molecular Biomedicine and a Bachelor degree in Life Sciences from the University of Münster. In parallel to her university studies, she participated in a journalistic traineeship program by the Konrad-Adenauer-Foundation. Working as a science and online journalist, Gianna became interested in information visualization and completed online courses at University of Texas and the University of Arts London. The last time she sat in a university classroom was at Columbia Journalism School in New York City, where she completed the post-grad Lede Program that teaches journalists how to code. Since then, she works as a data journalist and editor at DW and spreads her enthusiasm for data-driven journalism as a ddj trainer.

Publications Published Works

Data Stories of Water: Studying the Communicative Role of Data Visualizations within Long-form Journalism

— Computer Graphics Forum (Proc. EuroVis). 42(3), 2023
We present a methodology for making sense of the communicative role of data visualizations in journalistic storytelling and share findings from surveying water-related data stories. Data stories are a genre of long-form journalism that integrate text, data visualization, and other visual expressions (e.g., photographs, illustrations, videos) for the purpose of data-driven storytelling. In the last decade, a considerable number of data stories about a wide range of topics have been published worldwide. Authors use a variety of techniques to make complex phenomena comprehensible and use visualizations as communicative devices that shape the understanding of a given topic. Despite the popularity of data stories, we, as scholars, still lack a methodological framework for assessing the communicative role of visualizations in data stories. To this extent, we draw from data journalism, visual culture, and multimodality studies to propose an interpretative framework in six stages. The process begins with the analysis of content blocks and framing elements and ends with the identification of dimensions, patterns, and relationships between textual and visual elements. The framework is put to the test by analyzing 17 data stories about water-related issues. Our observations from the survey illustrate how data visualizations can shape the framing of complex topics.