The problem — A pipeline that computes a correct result and packages it without QC evidence has done half the job. The consumer — a biologist, a project manager, a regulatory reviewer — sees the answer but not the evidence that it should be believed. This creates the exact failure mode Varoquaux & Cheplygina document for medical imaging: methodologically weak results that propagate because the evidence of their weakness was never surfaced.
What it is / how it works — The report stage is the accountability layer. It packages three things together: the biological readout (profiles, hit list, slide prediction, dynamics summary), the QC evidence at each of the four nested quality levels (image-space, run/sample, readout, decision — per the model in Image Quality Is Not One Number), and the provenance lineage that traces the result back to its acquisition. In practice this means: a Cell Painting run ships profiles plus a per-plate reproducibility table plus the image-QC flags that were applied; a digital pathology slide ships a grade plus per-tile QC plus the normalization and embedding versions used; a spatial omics dataset ships an AnnData export plus registration quality metrics. The delivery form — PDF report, structured JSON, API endpoint, interactive notebook — is secondary to the completeness of the evidence chain. This is the concrete form of The QC-Aware Report.
Where it breaks — The most common failure is reporting only the endpoint — the profile, the score, the count matrix — with no attached evidence of the QC checks that cleared it. This makes a result that should be flagged look identical to a result that is solid. A secondary failure is provenance detachment: a report that does not trace back to its acquisition manifest cannot be reproduced or compared across runs — the failure described in the metadata perspective (Huisman et al.). The discipline: the report is not done until it includes the decision ("reportable / needs-review / reject") and the evidence chain that supports it.