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
82/100 · 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, with read lengths (~50 bp) and quality already typical of microRNA profiling. Overall it is a solid, fully measured assessment (evidence_strength=1) earning a B: base quality is excellent — pct_q30 of 89.3% and a mean base quality of 34 mean the reads are accurate enough to trust for confident miRNA quantification, and adapter content scored a clean 0%, which is reassuring for short-insert libraries where adapter read-through is a common failure. The grade was held back almost entirely by an 86.67% duplication rate, which zeroed out that metric; however, in a miRNA-Seq context very high duplication is largely expected and partly biological (a small repertoire of highly abundant mature miRNAs sequenced deeply), so it should be interpreted as a caution about library complexity rather than disqualifying contamination. For reuse, these data are dependable for relative miRNA abundance and differential-expression analysis, but the duplication level means you should rely on UMIs or collapsed-read counting if available and be cautious treating raw read depth as independent observations.
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.