Does interactive animation control improve exploratory data analysis of animated trend visualization?
Bibliographic record
Abstract
OBJECTIVE: Effectively analyzing trends of temporal data becomes a critical task when the amount of data is large. Motion techniques (animation) for scatterplots make it possible to represent lots of data in a single view and make it easy to identify trends and highlight changes. These techniques have recently become very popular and to an extent successful in describing data in presentations. However, compared to static methods of visualization, scatterplot animations may be hard to perceive when the motions are complex. METHODS: This paper studies the effectiveness of interactive scatterplot animation as a visualization technique for data analysis of large data. We compared interactive animations with non-interactive (passive) animations where participants had no control over the animation. Both conditions were evaluated for specific as well as general comprehension of the data. RESULTS: While interactive animation was more effective for specific information analysis, it led to many misunderstandings in the overall comprehension due to the fragmentation of the animation. In general, participants felt that interactivity gave them more confidence and found it more enjoyable and exciting for data exploration. CONCLUSION: Interactive animation of trend visualizations proved to be an effective technique for exploratory data analysis and significantly more accurate than animation alone. With these findings we aim at supporting the use of interactivity to effectively enhance data exploration in animated 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 | 0.003 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".