Data Analysis (epsl-data-analysis)

SkillAI & models

Use when reducing and reporting data for an Earth and Planetary Science Letters (EPSL) manuscript, full analytical-uncertainty budgets for isotope and geochronology data, defensible statistics (MSWD, weighted means, Bayesian age models), and inversion/model diagnostics. It guides reduction and reporting norms; it does not fabricate results.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Data Analysis (epsl-data-analysis) skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Earth-and-Planetary-Science-Letters-Skills/skills/epsl-data-analysis/SKILL.md and read by ahel’s review.

EPSL's methods-transparency culture is strictest exactly where the journal is strongest: isotope geochemistry and geochronology. Reviewers expect every headline number to arrive with its complete error budget — internal precision, external reproducibility, standard and blank corrections, and systematic terms like decay-constant uncertainty — and they expect model results to arrive with their resolution limits. Design choices live in epsl-study-design; deposition and supplements in epsl-reporting-and-reproducibility.

When to trigger

  • Reducing isotope, geochronologic, elemental, or geophysical data to reportable results
  • Deciding which uncertainty terms to propagate and how to quote them
  • Computing weighted means, isochrons, concordia intercepts, or Bayesian age–depth models
  • A reviewer asked for the error budget, the MSWD, or a resolution test

Reporting norms EPSL expects

  1. State the full uncertainty ladder. Quote internal (within-run) precision, external reproducibility from repeat standards/replicates, and — when comparing across methods or to other studies — systematic terms (decay constants, tracer calibration, spectrometer bias). Say which ladder rung each quoted ±X σ includes, and whether σ means 1σ, 2σ, or 95% CI.
  2. Anchor to community values. Name the reference materials with the values used, the normalization scheme (e.g., what δ is measured against), and the decay constants adopted, with citations — so the numbers remain comparable when calibrations shift.
  3. Statistics that match geochemical data. Weighted means with MSWD reported and interpreted (MSWD ≫ 1 means scatter beyond analytical error — say why); isochrons with the regression model named; mixtures and outliers handled by a stated rule, not silent deletion.
  4. Age models with honest priors. Bayesian age–depth or eruption-tempo models report priors, convergence, and sensitivity to them; deposit the model input/config.
  5. Inversions come with diagnostics. Resolution/recovery tests, misfit and trade-off curves, and an explicit statement of what the data cannot see; never present a smoothed model as if it were data.
  6. Blanks and detection. Report total procedural blanks and their variability; state how blank-sensitive results (small samples, young ages, low abundances) respond to the blank range.

Uncertainty-reporting table reviewers work down

ElementWhat to reportIf omitted, the reviewer assumes
σ-level and type1σ/2σ/95% CI; internal vs externalnumbers are not comparable
Reference materialsmeasured vs accepted values, per sessionaccuracy unverified
Blanksmagnitude + variability + correctionsmall samples untrustworthy
Decay constants / tracerwhich values, citedages not portable across studies
MSWD / goodness of fitvalue + interpretationscatter hidden in the mean
Model resolutionrecovery tests, trade-offsstructure may be artifact

Worked micro-example (illustrative — a weighted-mean age that survives review)

Twelve single-zircon U-Pb analyses from one ash bed (illustrative numbers):

  • Ten young analyses give a weighted mean of 66.021 ± 0.024 Ma (2σ, analytical only), MSWD = 1.3 — scatter consistent with analytical error, so a single population is defensible.
  • Two older grains are excluded as inherited by a pre-stated criterion (resolvably older than the main cluster), and shown in the figure anyway.
  • Reported as: "66.021 ± 0.024/0.031/0.075 Ma (2σ: analytical / +tracer / +decay constants), MSWD 1.3, n = 10/12" — the three-tier bracket lets a reader compare against Ar-Ar or astrochronologic ages without emailing the authors.

The habit that prevents most queries: every mean is accompanied by its MSWD, its n-of-N, and a plot showing the excluded analyses.

Referee-pushback patterns and the venue-specific fix

  • "Uncertainties are quoted but not defined." → State σ-level, and split analytical vs systematic terms wherever the result is compared to external ages or data.
  • "MSWD indicates overdispersion." → Do not just widen errors; identify the geological or analytical source of scatter and let the interpretation absorb it.
  • "The anomaly is at the edge of resolution." → Show the recovery test at that node; soften or drop claims the test cannot support.

Anti-patterns

  • A headline age or rate with a bare ± and no statement of what it includes
  • Standards measured but never reported against accepted values
  • Outlier analyses removed without a stated rule or a visible plot
  • MSWD omitted, or quoted without interpretation
  • Tomographic/model features discussed where resolution tests show smearing
  • Comparing your ages to literature ages without harmonizing decay constants

Output format

【Headline number】value ± (σ-level; analytical/+systematic) + units + n
【Traceability】standards vs accepted values; blanks; constants cited
【Statistics】weighted mean/isochron/Bayesian model + MSWD/diagnostics
【Exclusions】rule stated + shown in figures? [Y/N]
【Model diagnostics】resolution/sensitivity reported? [Y/N or N/A]
【Next】epsl-figures-and-tables

Supplementary resources

Signals

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Item type
skill
Key
epsl-data-analysis
Source
github.com/brycewang-stanford/awesome-journal-skills