Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› est-data-analysis
est-data-analysis
GitHub用于ES&T稿件的数据分析与报告，涵盖QA/QC、不确定度、统计方法及质量平衡。确保分析严谨性，指导结果呈现以通过专家审查，不伪造数据。
Trigger Scenarios
将原始仪器或现场数据转化为结果
构建结果部分及QA/QC报告
回复审稿人关于检测限、回收率或统计的问题
完成质量/能量平衡或拟合动力学/剂量-反应模型
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill est-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "est-data-analysis",
"description": "Use when executing and reporting the analysis for an Environmental Science & Technology (ES&T) manuscript so it survives expert review — analytical QA\/QC, honest uncertainty, statistics appropriate to environmental data, and closed mass balances. It guides analysis and reporting norms; it does not fabricate results."
}
Data Analysis (est-data-analysis)
ES&T reviewers scrutinize the analytical chain: blanks, recoveries, detection limits, replicates, and
whether the numbers add up. This skill covers execution and reporting; design decisions live in
est-study-design, and deposit/reproducibility in est-reporting-and-reproducibility.
When to trigger
- Reducing raw instrument/field data into results
- Building the results section and the QA/QC reporting
- A reviewer asked about detection limits, recoveries, replicates, or statistics
- Closing a mass/energy balance or fitting kinetics/dose–response
Analysis norms ES&T expects
- Report QA/QC explicitly. Method/field blanks, matrix-spike recoveries, CRM results, LOD/LOQ, calibration range and R², surrogate/internal-standard recoveries, and how non-detects were handled.
- Honest uncertainty. Report replicates with measures of dispersion (SD/SE/CI), not single values; propagate uncertainty through derived quantities; state n every time.
- Right statistics for environmental data. Handle left-censored (below-LOD) data correctly (e.g., substitution caveats, MLE/ROS, Kaplan–Meier); check distributional assumptions; use nonparametric or transformed analyses for skewed/heteroscedastic data; correct for multiple comparisons.
- Mass / energy balance. Account for products, sorbed and volatilized fractions, and losses; report closure (%) and explain gaps.
- Kinetics / dose–response. Report rate constants/half-lives with CIs and goodness of fit; EC/IC/LC values with confidence bounds; state the model fitted.
- Effect size & significance. Give magnitudes and intervals, not p-values alone; relate results to environmentally meaningful thresholds.
Reproducibility while you work (not at the end)
- A master script/workflow regenerates every figure, table, and SI exhibit from processed data.
- Set and report seeds for any stochastic step (bootstrap, Monte Carlo, simulation).
- Pin software/package versions; record instrument settings and integration parameters.
- Keep figure/table numbers matched to script outputs (see
est-reporting-and-reproducibility).
QA/QC reporting table reviewers expect to see
ES&T referees often work down a mental analytical checklist. Reporting each item pre-empts the most common "rigor not established" objection. The minimum set, with what a reviewer reads when it is absent:
| Element | What to report | If omitted, the reviewer assumes |
|---|---|---|
| Method/field blanks | blank levels vs. sample levels; subtraction approach | contamination is uncontrolled |
| Recoveries | matrix-spike % and CRM agreement | quantitation is biased/unknown |
| LOD/LOQ | derivation (e.g., 3σ/10σ or S/N) and per-analyte values | "detections" may be noise |
| Calibration | range, R², whether samples fall in range | extrapolation beyond standards |
| Surrogate/IS recoveries | per-sample recovery correction | run-to-run drift hidden |
| Non-detects | censoring method (ROS/MLE/KM), not bare substitution | summary statistics distorted |
Worked micro-example (illustrative — PFAS quantitation with censored data)
A river-PFAS dataset (illustrative numbers) shows how the rules combine into a reportable result:
- 24 samples, triplicate injection; LOQ for PFHxA = 0.5 ng/L (illustrative, derived at 10×S/N).
- Matrix-spike recovery 92% (RSD 7%, n=6); field blank < LOQ; surrogate-corrected.
- 9 of 24 below LOQ — left-censored. Naive half-LOQ substitution would report a mean of 3.1 ng/L; regression-on-order-statistics (ROS) gives 2.4 ng/L (illustrative), because substitution inflated the low tail. Report the ROS mean with its CI and state the method.
- Reported result: "PFHxA = 2.4 ng/L (95% CI 1.7–3.3, n=24, 38% < LOQ; ROS), recovery 92±7%." That single line carries magnitude, uncertainty, n, censoring handling, and recovery — the form a reviewer can sign off without a query.
Referee-pushback patterns and the venue-specific fix
- "Detection limits and QA/QC are not reported." → Add the blank/recovery/LOD/LOQ table to the SI and cite it from Methods; never leave it implicit.
- "Below-detect data handled by substitution." → Re-analyze with ROS/MLE/Kaplan–Meier; show the result is robust to the censoring choice.
- "The mass balance does not close." → Report closure %, name the unaccounted fraction (sorbed, volatilized, mineralized), and bound it rather than ignoring the gap.
Anti-patterns
- Results with no blanks, recoveries, or detection limits reported
- Single measurements with no replication or dispersion
- Naive zero/half-LOD substitution for heavily censored data with no caveat
- A transformation/treatment study whose mass balance never closes (or is never reported)
- p-values without effect sizes or environmental thresholds
- Over-fitting kinetics/dose–response with too few points
Output format
【Main result】magnitude + uncertainty (n, SD/CI) + units
【QA/QC】blanks / recoveries / CRM / LOD-LOQ / calibration reported? [Y/N]
【Censored data】handled how
【Mass/energy balance】closure % + explanation
【Statistics】appropriate test + assumptions checked? [Y/N]
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】est-figures-and-tables
Supplementary resources
../../resources/external_tools.md— analytical QA/QC, statistics, and modeling packages../../resources/official-source-map.md— data-availability and reproducibility expectations
Version History
- 1839142 Current 2026-07-05 13:11
