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 bulk small-RNA library (miRNA-Seq on Illumina HiSeq 2000, mouse), and overall it earns a solid B (83/100) as a reusable dataset whose base-level chemistry is excellent but whose complexity is questionable. The grade is held up almost entirely by quality scores — 93.4% of bases at Q30 and a mean base quality of 35.1, with zero adapter contamination and negligible N content — meaning the reads themselves are clean, accurately called, and ready to align without aggressive trimming. The one metric dragging the score down is the duplication rate of 89.16% (scored 0/100): while extreme duplication is partly expected and even normal for miRNA-Seq, where a small repertoire of short, highly expressed mature miRNAs is sequenced deeply, this level still flags limited library complexity and means effective unique coverage is far lower than the 7.65M raw reads suggest, so quantification of low-abundance miRNAs will be noise-limited. Reassuringly, the metrics that mattered most here were all directly measured rather than extrapolated, so this reading is firm rather than provisional — though note that several descriptive fields (total reads/bases, checksum) are reported-not-measured, which only affects bookkeeping, not the quality verdict.
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.