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 mouse small-RNA (miRNA-Seq) library sequenced on a HiSeq 2000, and overall it is a solid, trustworthy dataset (grade B, 83/100) whose score rests almost entirely on directly measured QC metrics. Base quality is excellent — pct_q30_bases of 100% and a mean base quality well above any concern, with zero adapter contamination and negligible N content — so per-base accuracy is not a limiting factor for downstream mapping or miRNA quantification. The one metric that pulled the grade down is the duplication_rate_pct of 96.28%, which scored 0; for most assays this signals a low-complexity library, but in short small-RNA sequencing such high duplication is expected and partly artefactual, since mature miRNAs are short, highly abundant, and identical reads are biologically genuine rather than purely PCR duplicates — so do not naively deduplicate before counting. With evidence_strength at the maximum and the key quality metrics all measured rather than extrapolated, this reading is firm and not provisional; the dataset is reusable for miRNA expression work provided the duplication is interpreted in that small-RNA context.
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.