An Exploratory Study of Data Sketching for Visual Representation
Bibliographic record
Abstract
Abstract Hand‐drawn sketching on napkins or whiteboards is a common, accessible method for generating visual representations. This practice is shared by experts and non‐experts and is probably one of the faster and more expressive ways to draft a visual representation of data. In order to better understand the types of and variations in what people produce when sketching data, we conducted a qualitative study. We asked people with varying degrees of visualization expertise, from novices to experts, to manually sketch representations of a small, easily understandable dataset using pencils and paper and to report on what they learned or found interesting about the data. From this study, we extract a data sketching representation continuum from numeracy to abstraction; a data report spectrum from individual data items to speculative data hypothesis; and show the correspondence between the representation types and the data reports from our results set. From these observations we discuss the participants’ representations in relation to their data reports, indicating implications for design and potentially fruitful directions for research.
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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 | 0.042 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".