ijoc-data-analysis
Use when running and reporting the computational experiments — and assembling the reproducible code/data deposit — for an INFORMS Journal on Computing (IJOC) manuscript. Turns a designed protocol (see ijoc-methods) into defensible, reproducible results; it does not redesign the experiment.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijoc-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Computational Experiments & Reproducibility (ijoc-data-analysis) ## When to trigger - The protocol is designed and you are now **running experiments** and interpreting results - A referee questions whether a performance win is **real** versus tuning, seed luck, or benchmark selection - You need to turn raw outputs into the **statistical comparison** IJOC expects (not just a table of best times) - You are assembling the **IJOC GitHub Software and Data Repository** deposit and want it to reproduce the paper ## Making the computational claim defensible IJOC's distinctive risk is a result that is real on the page but an artifact underneath. Pre-empt the three ways a referee will attack it. - **Tuning artifact.** Show the win holds with tuning done symmetrically on a disjoint set. Report performance at default *and* tuned for both your method and the baselines, so the gain is not hidden in hyperparameters. - **Seed/variance artifact.** For any stochastic component, run **multiple seeds** (commonly ≥10) and report mean, dispersion (sd/IQR), and the distribution — a box/violin plot or a table of quartiles — not a single best run. State the seeds; they go in the deposit. - **Benchmark-se
- When to trigger
- Making the computational claim defensible
- The statistics IJOC reviewers expect
- Assembling the IJOC reproducibility deposit
- Execution bridge (StatsPAI / Stata MCP)
- Checklist
- Anti-patterns
- Output format
What does the ijoc-data-analysis skill do?
Use when running and reporting the computational experiments — and assembling the reproducible code/data deposit — for an INFORMS Journal on Computing (IJOC) manuscript. Turns a designed protocol (see ijoc-methods) into defensible, reproducible results; it does not redesign the experiment.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijoc-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.