A Design Language for Prototyping and Storyboarding Data-Driven Stories
Bibliographic record
Abstract
Data-driven stories (DDS) are digital forms of storytelling that arrange data and visualizations to communicate a narrative of information to an audience. They have been growing rapidly over the past decades. As a result, a great degree of versatility appears in the forms of published DDS. The recent structures of DDS are more complex, respecting their arrangement, composition, features, and inner parts. In the current academic research, neither storytelling techniques nor any taxonomies suggest visual mechanisms to distinguish between different layouts, compositions, and arrangements. The lack of an expressive visual solution that integrates different parts of DDS under one structure prevents the authors from trying more alternative design paths in the story design process. In this proposed work, we unify all the constructing parts of DDS to define the narrative structure as a visually structured representation of the DDS narrative, which is formed and designed by their constructing elements. This solution proposes a design language consisting of a set of design rules that integrate the visual elements to represent the DDS narrative structure. Our evaluation of the audit process out of 100 DDS examples confirms that the design language is comprehensive, expressive, and versatile. Additionally, we developed DataStoryDesign, a system that incorporates this visual solution to facilitate prototyping and storyboarding DDS for a team of DDS authors. The preliminary result of the exploratory evaluation indicates that such a solution is effective in prototyping and storyboarding DDS. In addition, our findings confirmed that the existence of our design language improves the visual communication between different personas in the DDS production workflow.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".