Provenance — who produced it, who reused it
Linked to 1 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
1 further paper cites this accession but reuse could not be confirmed.
Deep data QC
18/100 · FStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
This is a small-RNA (miRNA-Seq) library from mouse, sequenced on a HiSeq 2500, and it fails QC decisively at 18/100 (grade F). The grade is driven down overwhelmingly by base-call quality: only 31.4% of bases reach Q30 and the mean base quality is 14.3 (roughly a 1-in-25 error rate), both scoring 0/100, which means base calls are unreliable enough to compromise the precise sequence matching that miRNA quantification depends on, and the 70% duplication rate further erodes library complexity. The one bright spot is essentially zero adapter contamination, but for a miRNA library — where short inserts normally produce heavy, expected adapter read-through — a 0% adapter figure is itself suspicious and may indicate the small-RNA inserts were not properly captured or the metric was computed on already-trimmed reads. With an evidence_strength of 1, all four scoring metrics were directly measured, so this poor verdict is not provisional — though I'd recommend reusing this dataset only with strong caution, if at all, given that low base quality fundamentally limits confident miRNA identification and expression estimates.
The F grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.