ConnectedCharts: Explicit Visualization of Relationships between Data Graphics
Bibliographic record
Abstract
Abstract Multidimensional multivariate data can be visualized using many different well‐known charts, such as bar charts, stacked bar charts, grouped bar charts, scatterplots, or pivot tables, or also using more advanced high‐dimensional techniques such as scatterplot matrices (SPLOMs) or parallel coordinate plots (PCPs). These many techniques have different advantages, and users may wish to use several charts or data graphics to understand a dataset from different perspectives. We present ConnectedCharts, a technique for displaying relationships between multiple charts. ConnectedCharts allow for hybrid combinations of bar charts, scatterplots, and parallel coordinates, with curves drawn to show the conceptual links between charts. The charts can be thought of as coordinated views, where linking is achieved not only through interactive brushing, but also with explicitly drawn curves that connect corresponding data tuples or axes. We present a formal description of a design space of many simple charts, and also identify different kinds of connections that can be displayed between related charts. Our prototype implementation demonstrates how the connections between multiple charts can make relationships clearer and can serve to document the history of a user's analytical process, leading to potential applications in visual analytics and dashboard design.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".