Exploring the Design of Animated Transitions between Abstract and Concrete Visualizations in Immersive Environments
Bibliographic record
Abstract
While data visualizations are typically abstract, there is a growing body of work around concrete visualizations, which use familiar objects to convey data. Concrete visualizations can complement abstract ones, especially in immersive analytics, but it is unclear how to design smoothly animated transitions between these two kinds of representations. We investigate a design space of abstract and concrete visualizations, where animated transitions are pathways through the design space. The design space is defined with four axes, each corresponding to a different transformation. We consider different ways to design animated transitions by staging and ordering the transformations along these axes. In a controlled experiment conducted in virtual reality with 16 participants, we compared four types of animated transitions and found quantitative and qualitative evidence of the superiority of a specific staging approach over the simultaneous application of all transformations. Our study pre-registration is available at https://osf.io/8mu73?view_only=f5ed74fd9e2346228214f5dec973b208
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".