跳到主要内容
知仓学习社ZHICANG

vis-reproducibility

Use when strengthening IEEE VIS reproducibility and open-practices evidence, covering the open-materials statement, anonymized-but-runnable code and…

不碰外部(只输出文字)无严重或高危命中brycewang-stanford/Awesome-Journal-Skills

它会碰到什么

扫了多少1 个文本文件,6 KB
它会碰到什么不碰外部(只输出文字)
命中总数0 处
命中统计严重 0 · 高 0 · 中 0 · 低 0

这一栏是扫描器报的事实,不是结论。命中多不等于有毒(安全工具、规则库、示例脚本本来就会包含危险写法),命中少也不等于干净。它和你手上的凭据、文件、网络有什么关系,需要你自己看。

技能内容

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>

想直接用这个技能?

本站把开放许可(MIT / Apache 等)的技能按仓库打包整理到网盘,点一下转存到你自己的网盘,不用一个个从 GitHub 拉。许可未声明的技能只给原始仓库链接,不打包。