gcb-data-analysis
GitHub指导GCB期刊稿件的数据分析，涵盖混合模型、时间序列及元分析。强调尊重数据结构、诚实报告不确定性、量化误差传播，并强制要求通过主脚本和固定种子确保全流程可重复性，以满足审稿与数据归档标准。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill gcb-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "gcb-data-analysis",
"description": "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."
}
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
- 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. - Report uncertainty honestly. Effect sizes with confidence/credible intervals, not just p-values or stars; state the magnitude and its ecological/biogeochemical meaning.
- Quantify, propagate, and partition uncertainty. For models, separate parameter, structural, and scenario uncertainty; prefer ensembles; show measurement error where it matters.
- Meta-analysis discipline. Appropriate effect size (log response ratio, Hedges' g), random/mixed effects, heterogeneity (I^2, tau^2), moderators pre-specified, and a publication-bias check.
- Evaluate models against observations. Report skill metrics and where the model fails, not only where it succeeds.
- Right inference for the unit. Match the analysis to the experimental/sampling unit; avoid pseudoreplication carrying through from design.
Reproducibility while you work (not at the end)
- One master script regenerates every table and figure from raw/constructed data.
- Set and report seeds for any stochastic step (bootstrap, MCMC, simulation, model ensembles).
- Pin software/package versions (
renv.lock,conda/requirements.txt, model version + forcing). - Keep manuscript table/figure numbers matched to script outputs — they will be archived together.
Matching the method to the global-change question
GCB referees expect the analysis to fit the data-generating process. Use this as a routing table from question shape to the inferential machinery a quantitatively literate reviewer will look for.
| Question shape | Expected machinery | What a reviewer checks |
|---|---|---|
| Effect of a manipulated driver across randomized plots | Mixed model with plot/block random effects | Random structure matches the design; no pseudoreplication |
| Trend in a flux time series | Autocorrelation-aware regression / state-space | Residual autocorrelation modelled, not ignored |
| Spatial pattern across a gradient | Spatial random field (INLA/spaMM) |
Spatial dependence handled; CRS and area stated |
| Synthesis across many studies | Random/mixed-effects meta-analysis | Effect-size choice, I^2/tau^2, bias check |
| Future projection from a process model | Multi-model ensemble | Structural + parameter + scenario spread shown |
Worked micro-example (illustrative)
A warming-experiment meta-analysis pools log response ratios (lnRR) of aboveground biomass from 64 studies. A defensible GCB workflow: fit a random-effects model, report the pooled lnRR back-transformed to a percentage with its interval, and quantify heterogeneity. Illustrative output — pooled lnRR 0.12, i.e. a +13% biomass response (95% CI 6–20%), I^2 = 71% with tau^2 = 0.04, and a moderator showing the effect halves in water-limited sites. The funnel plot and trim-and-fill leave the sign unchanged. The 71% heterogeneity is the result, not noise: it motivates the moisture moderator. All numbers illustrative.
Referee pushback patterns and the GCB-appropriate fix
- "Pseudoreplication: chamber treated as replicate" → move the treatment effect to a random-effect or split-plot structure at the true unit of inference.
- "Heterogeneity ignored in the synthesis" → report I^2/tau^2 and pre-specified moderators, not a single pooled mean.
- "Projection has no uncertainty band" → run an ensemble and partition parameter, structural, and scenario spread rather than reporting one trajectory.
- "Skill claimed but never tested out-of-sample" → report validation against held-out observations and the conditions where the model fails.
Anti-patterns
- Treating nested/repeated/spatial data as independent observations
- Stars-only results with no effect sizes, intervals, or ecological magnitude
- A single model run reported as if it had no structural or scenario uncertainty
- A meta-analysis with no heterogeneity or publication-bias assessment
- Code that cannot reproduce the printed tables/figures ("works on my machine")
Output format
【Main estimate】effect size + interval + ecological/biogeochemical meaning
【Data structure】random effects / autocorrelation handled? [Y/N]
【Uncertainty】measurement + parameter + structural + scenario partitioned?
【Model evaluation / heterogeneity】skill metrics or I^2 reported?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】gcb-figures-and-tables
Supplementary resources
../../resources/external_tools.md— mixed-model, meta-analysis, spatial, and modelling packages../../resources/official-source-map.md— data/code archiving policy
Version History
- 1839142 Current 2026-07-05 13:16
