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Record W4366550150 · doi:10.1145/3544548.3581119

Showing Flow: Comparing Usability of Chord and Sankey Diagrams

2023· article· en· W4366550150 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsChord (peer-to-peer)UsabilityComputer scienceVisualizationInterpretabilityEngineering drawingHuman–computer interactionInformation retrievalData miningEngineeringArtificial intelligenceDatabase

Abstract

fetched live from OpenAlex

Chord and Sankey diagrams are two common techniques for visualizing flows. Chord diagrams use a radial layout with a single circular axis, and Sankey diagrams use a left-to-right layout with two vertical axes. Previous work suggests both strengths and weaknesses of the radial approach, but little is known about the usability and interpretability of these two layout styles for showing flow. We carried out a study where participants answered questions using equivalent Chord and Sankey diagrams. We measured completion time, errors, perceived effort, and preference. Our results show that participants took substantially longer to answer questions with Chord diagrams and made more errors; participants also rated Chord as requiring more effort, and strongly preferred Sankey diagrams. Our study identifies and explains limitations of the popular Chord layout, provides new understanding about radial vs. linear layouts that can help guide visualization designers, and identifies possible design improvements for both visualization types.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.318
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations13
Published2023
Admission routes1
Has abstractyes

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