Skills Development Achieving Visual Analytics Reproducibility Standards

Achieving Visual Analytics Reproducibility Standards

v20260724
vis-reproducibility
This guide details best practices for achieving high standards of reproducibility in visual analytics papers, focusing on open materials, verifiable code, and rigorous documentation. It covers mapping figures to data sources, preregistering studies, documenting data provenance, and defining the level of reproducibility achieved (e.g., turnkey, scripted).
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Overview

VIS Reproducibility

Use this before submission and again before camera-ready. IEEE VIS's Open Practices posture and the Graphics Replicability Stamp make reproducibility a visible dimension, not a courtesy: reviewers routinely open the supplemental code, data, and video, and the TVCG camera-ready collects open-practices disclosures. The goal is that a competent reader could rebuild your figures, rerun your study analysis, and reach your conclusions.

Evidence map

  • Map each figure, quantitative result, and study finding to a verifiable location — a section, a figure generated from logged data, or a script in the supplemental archive.
  • For techniques and rendering, give enough of the algorithm, parameters, and environment (including GPU/driver assumptions and tolerances) that a reader could re-implement or re-run.
  • For empirical and perceptual studies, report participants and recruitment, apparatus/stimuli, the task, the design (within/between), measures, statistics, and the analysis scripts.
  • Keep the open-materials statement truthful and specific: what is shared, where it lives, and — if something cannot be shared — exactly why.
  • Keep the paper and the archive consistent: a number in the PDF that no script reproduces is the contradiction reviewers read as carelessness.

Open-materials statement audit

Claim in the paper Weak availability answer VIS-ready answer
"We render/lay out X" "Code available on request" Public, licensed repo with a build path that regenerates the teaser figure
"Our system supports task Y" "Demo will be released" Runnable build (or Docker) with bundled sample data and a demo script
"N participants judged Z" Nothing (privacy cited vaguely) Anonymized responses, stimuli, the analysis notebook, and the ethics/consent note
"We evaluated on dataset D" Named but not shared The dataset or documented access + the preprocessing scripts

"Available on request" reads as not available; convert every such line into a concrete, anonymized archive or an explicit, justified exception.

Preregistration for studies (a distinctly VIS-valued move)

Perceptual experiments and controlled user studies benefit from preregistration (e.g., on OSF): locking hypotheses, design, sample size, and the analysis plan before data collection separates confirmatory from exploratory findings and blunts the "you fished for that result" objection.

[Preregister]  hypotheses, conditions, planned N + power analysis, primary DV, analysis plan
[Cite it]      reference the (anonymized) preregistration in the paper; report deviations honestly
[Separate]     label confirmatory vs. exploratory results; do not present post-hoc as planned

Provenance pinning

[Datasets]   record source and version; archive the actual data or stimuli, not just a query/URL;
             document any cleaning/filtering with the script
[Rendering]  pin toolchain and library versions; provide reference images and a comparison
             tolerance for GPU-dependent or non-deterministic output
[Studies]    store raw per-participant responses (anonymized), the exact stimuli, and timing
[Compute]    state hardware and runtime so a reader can size a reproduction
[Randomness] log seeds; say what is and is not deterministic

Degrees of reproducibility (state the one you achieved)

  • Turnkey: one documented command regenerates each figure/result from logged data.
  • Scripted: scripts exist but need documented manual steps, large data, or specific hardware.
  • Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.

For VIS, aim turnkey for anything an evaluator might rerun quickly (a figure from logged benchmark data, a study's statistics from anonymized responses); a large rendering benchmark or a proprietary dataset may stay scripted with access clearly documented. Stating the achieved level honestly beats promising turnkey behavior that fails on a clean machine — the GRSI stamp is decided exactly there.

Vignette: a technique-plus-study paper

A paper contributing a new encoding and a controlled study evaluating it. Its reproducibility spine: the encoding code with a script that regenerates each figure; the study's stimuli and anonymized per-participant responses; the preregistration for the confirmatory hypotheses; the analysis notebook that turns responses into the reported effect sizes and CIs; and one honest sentence about anything (identifiable video, proprietary data) that cannot be shared and why.

Consistency and camera-ready pass

  • Before submission: every scored number and figure traces to the archive; the open-materials statement matches reality; if double-blind, the archive is anonymized (no owner strings, lab names, or institutional URLs).
  • Before camera-ready: swap anonymized links for permanent, DOI-issuing archives, complete the Open Practices form, and align the package with what you submit to GRSI (vis-artifact-evaluation).

Output format

[Claim inventory] <figure/result/finding -> evidence location>
[Open materials] concrete / vague / missing
[Preregistration] present / not applicable / should have (for studies)
[Provenance gaps] <dataset versions / rendering references / study raw data / seeds>
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Paper fixes] <must appear in the PDF>
[Archive fixes] <additions before upload>
Info
Category Development
Name vis-reproducibility
Version v20260724
Size 5.86KB
Updated At 2026-07-29
Language