Interaction Techniques for Stacked-Dimension Visualizations
Bibliographic record
Abstract
Stacked-dimension tables (SDTs) represent multidimensional data with nested tables, where each table shows two of the dataset’s dimensions. SDTs provide a comprehensive overview of all of the data and all of its dimensions – but overviews are just the starting point for exploration, and there is little information available to designers about how to support further interactions with SDTs. We worked with a crop-breeding research group to develop a stacked-dimension system that suited their complex multidimensional datasets, and to identify requirements for their analyses of differential gene expression across multiple genomes. Based on the requirements, we developed a new SDT system and several new interaction techniques that support the researchers’ needs to filter the data, reconfigure the visualization, provide data context, revisit previous configurations, and integrate findings into a broader workflow. To test the generalizability of our designs, we then extended the requirements and techniques in a second SDT system for a new domain (outcomes from a retirement-planning model) and carried out a small usability study with this system. Our evaluations show that the techniques are easily learned and understood by both domain experts and everyday users, and that they provide support for real-world exploration in stacked-dimension visualizations.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".