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
83/100 · BStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
This is a human small-RNA (miRNA-Seq) library on the HiSeq 2000, and its B/83 grade reflects genuinely excellent base-level quality tempered by one expected anomaly. The grade was driven up by pristine base calling — pct_q30_bases and mean_base_quality both maxed out, with zero adapter and zero N content, meaning trimmed reads should map cleanly — and the ~22 nt mean read length is exactly right for mature miRNAs, so the assay looks on-target. The single metric pulling the score down is the 97.65% duplication rate, which scored 0; this would be alarming in mRNA-seq, but for miRNA-Seq it is largely an artifact of a small, naturally low-complexity target space where the same handful of abundant miRNA sequences recur, so it should not by itself disqualify reuse for differential-expression work. Because all contributing metrics were actually measured (evidence_strength = 1), this reading is solid rather than provisional, though you should still confirm library complexity and unique-miRNA yield before trusting it for novel-miRNA discovery.
The B 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.