What’s My Line? Exploring the Expressive Capacity of Lines in Scientific Visualization
Bibliographic record
Abstract
Data is moving beyond the scientific community, flooding communication channels and addressing issues of importance to all aspects of daily life. This highlights the need for rich and expressive data representations to communicate the science on which society rests and must act. However, current visualization techniques often lack the broad visual vocabulary needed to accommodate the explosion in data scale, diversity, and audience perspectives. While previous work has mined artistic and design knowledge for color maps and shape affordances (glyphs) in visualization, line encoding has received little attention. In this paper, we report on an exploration of visual properties that extend the vocabulary of the line, particularly for categorical encoding. We describe the creation of a corpus of lines motivated by artistic practice, Gestalt theory, and design principles, and present initial results from a study of how different visual properties influence how people associate these into sets of similar lines. While very preliminary, the findings suggest that a rich set of line attributes will support both association and categorical hierarchies, as well as provoke further inquiry into how and why line encoding can be more expressive in encoding multivariate, multidimensional data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".