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
81/100 · BStandardized, 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 single-end at 50 bp on a HiSeq 2000, and its B/81 grade rests on metrics that were actually measured rather than extrapolated (evidence_strength = 1), so the reading is firm rather than provisional. Base quality is the dataset's strength: 89.1% of bases at Q30 and a mean base quality of 33.9 mean reads are accurate enough for confident miRNA identification and quantification, and the 0% adapter content indicates clean, properly trimmed inserts—important because adapter readthrough is a common artifact in short small-RNA fragments. The one metric pulling the score down hard is the 82.95% duplication rate (scored 0/100), but this should not be read as library failure: miRNA-Seq targets a small, finite repertoire of short mature sequences, so very high duplication is biologically expected and largely reflects the assay rather than a degraded or over-amplified sample. For reuse, the data are trustworthy for miRNA expression analysis; just treat raw duplication-based filters with caution and rely on the strong per-base quality and absent adapter contamination as the real indicators of fitness.
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.