The Elephant in the Room: Expert Experiences Designing, Developing and Evaluating Data Visualizations on Large Displays
Bibliographic record
Abstract
Large displays can provide the necessary space and resolution for comprehensive explorations of data visualizations. However, designing and developing visualizations for such displays pose distinct challenges. Identifying these challenges is essential for data visualization designers and developers creating data visualizations on large displays. In this study, we aim to identify the challenges designers and developers encounter when creating data visualizations for large displays. We conducted semi-structured interviews with 13 experts experienced in creating data visualizations for large displays and, through affinity diagramming, categorized the challenges. We identified several challenges in designing, developing, and evaluating data visualizations on large displays, as well as building infrastructure for large displays. Design challenges included scaling visual encodings, limited design tools, and adopting design guidelines for large displays. In the development phase, developers faced difficulties working away from large displays and dealing with insufficient tools and resources. During the evaluation phase, researchers encountered issues with individuals' unfamiliarity with large display technology, interaction interruptions by technical limitations such as cursor visibility issues, and limitations in feedback gathering. Infrastructure challenges involved environmental constraints, technical issues, and difficulties in relocating large display setups. We share the lessons learned from our study and provide future directions along with research project examples to address these challenges.
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".