gcb-data-analysis
Use when executing and reporting the analysis for a Global Change Biology (GCB) manuscript — mixed/hierarchical models, time-series and spatial analysis, meta-analysis, and model evaluation with honest uncertainty. GCB reviewers and data archiving demand reproducible, well-quantified inference. Guides analysis norms; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill gcb-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.
# Data Analysis (gcb-data-analysis) GCB reviewers are quantitatively sophisticated, and because **data and code are archived publicly with a DOI** (see `gcb-reporting-and-data-policy`), the analysis must be reproducible by a third party. Analyze as if both are true — because they are. This skill covers execution and reporting norms; design decisions live in `gcb-study-design`. ## When to trigger - Running main and supporting analyses; building the results - Choosing the right model for nested/repeated/spatial ecological data - Synthesizing effect sizes for a meta-analysis or evaluating a process model - Making the analysis reproducible before deposit ## Analysis norms GCB expects 1. **Respect the data structure.** Use mixed / hierarchical models (`lme4`, `glmmTMB`, `brms`, `INLA`) for nested, repeated-measures, and spatially/temporally autocorrelated data; do not ignore random effects or autocorrelation. 2. **Report uncertainty honestly.** Effect sizes with confidence/credible intervals, not just p-values or stars; state the magnitude and its ecological/biogeochemical meaning. 3. **Quantify, propagate, and partition uncertainty.** For models, separate **parameter, structural, and s
- When to trigger
- Analysis norms GCB expects
- Reproducibility while you work (not at the end)
- Matching the method to the global-change question
- Worked micro-example (illustrative)
- Referee pushback patterns and the GCB-appropriate fix
- Anti-patterns
- Output format
- Supplementary resources
What does the gcb-data-analysis skill do?
Use when executing and reporting the analysis for a Global Change Biology (GCB) manuscript — mixed/hierarchical models, time-series and spatial analysis, meta-analysis, and model evaluation with honest uncertainty. GCB reviewers and data archiving demand reproducible, well-quantified inference. Guides analysis norms; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill gcb-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.