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 miRNA-Seq library (Illumina HiSeq 2000), and it earns a solid B (83/100): base-call quality is excellent, with 93.6% of bases at Q30 and a mean base quality of 36.7, alongside essentially zero adapter contamination and negligible N content, so the reads themselves are clean and trustworthy for downstream small-RNA quantification. The single factor dragging the grade down is an 89.5% duplication rate, which scored 0/100 — though for a miRNA library this is largely expected and benign, since the small, biologically constrained pool of mature miRNAs is sequenced very deeply and naturally produces many identical reads, so you should not treat it as a true library-complexity failure the way you would for standard mRNA-seq. All contributing metrics here were directly measured (evidence_strength=1), so this reading is firm rather than provisional; the only caveats are that total_reads/total_bases are reported rather than independently re-counted. Bottom line: reusable with confidence for miRNA expression analysis, provided you interpret the high duplication in the correct small-RNA context rather than as a quality defect.
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.