uncertainty4ddj: Visualizing Uncertainty in Data Journalism (2024–2026)

Data journalism frequently relies on data that is incomplete, estimated, or model-based, yet uncertainty is rarely communicated explicitly in published visualizations. This project, uncertainty4ddj, investigates how uncertainty can be more effectively represented in data journalism to improve transparency, trust, and audience understanding. We combine a systematic analysis of existing journalistic visualizations with qualitative insights from a full-day co-creation workshop involving data journalists. From this, we derive a structured set of seven recurring uncertainty scenarios, including forecasts, sampling, spatial uncertainty, and missing data. Each scenario highlights distinct challenges in representation and interpretation. Our findings reveal key barriers to uncertainty communication, including editorial constraints, perceived audience limitations, and a lack of established visual conventions. Based on these insights, we identify design opportunities and propose a set of guidelines for integrating uncertainty into journalistic storytelling. The project contributes both an empirical foundation and practical recommendations for advancing uncertainty-aware visualization in real-world newsroom contexts.