Audit the OCx25 dataset

The 2025 release (experiments, xrf, dashboard) is the basis of every model built in this Campaign. A second pair of eyes on a dataset of this size is useful to its authors and to every solver: check it row by row and report what you find.

What to examine

Work from experiments and xrf; use dashboard only to cross-check the team's own cleaning (what it dropped, and whether it should have).

1. Integrity. Duplicated rows or sample ids; rep labels that do not match the ids; summary rows (avg, std) that disagree with the replicates they summarise; missing current densities in a CO₂RR series; Faradaic efficiencies that do not sum sensibly (fe_gas_total, fe_liquid as the electron balance); impossible values (negative efficiencies, voltages with the wrong sign or magnitude for the reaction); HER rows carrying product columns, and the reverse.
2. Replicate reproducibility. For each source and reaction, the spread between replicates of the same synthesis target (HER voltage at 50 mA/cm²; CO₂RR efficiencies and production rates at each current density and at 3.3 V after prepare_targets.py), overall and by batch. Compare with the noise floors the paper reports (reference.md).
3. Composition. Target versus XRF composition: how far they differ by source and element, samples with no XRF, elements in xrf that never appear in a target (contamination or substrate signal), and the dashboard's per-element columns against xrf.
4. Batch effects and drift. Whether a batch or a date is predictive of outcome beyond composition; whether identical targets printed in different batches agree.
5. Outliers. Samples whose outcome is far from every neighbour in composition space, with a judgment on whether they look like measurement problems or real chemistry.

What to submit

• document: a JSON file satisfying document_schema: counts, one replicate_reproducibility row per (source, reaction, quantity), findings numbered F001… with severity, category, the affected sample ids, the evidence (a number, a rule, a query) and a suggested action, and recommended_exclusions that each cite a finding. Be specific enough that the team can act on each line without re-deriving it.
• report: Markdown, under 4,000 words, written for the experimental team: what is solid, what needs a look, what you would change in the release.
• code: an archive that regenerates every number in the document from the bound inputs; include the per-replicate tables as CSV.

How it is judged

Schema check, then a campaign reviewer who knows the data. Verified when the findings are correct (the reviewer spot-checks every blocking and major finding against the data, and a sample of the rest), the reproducibility statistics re-run, and the recommended exclusions are each justified by a finding. Speculation about mechanisms is not a finding; a finding is something a row in the data shows. Bound inputs: experiments, xrf, dashboard, document_schema.