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 high-depth, miRNA-targeted small-RNA library (labeled bulk RNA-seq, miRNA-Seq on a HiSeq 2500) for which essentially all quality signals were directly measured, giving a confident reading (evidence_strength=1, so this is not provisional). The verdict is a solid B: base-call quality is the clear strength — 93.2% of bases at Q30 and a mean base quality of 36.7 mean the reads themselves are clean and reliably called, with zero adapter contamination and negligible N content, so trimming and mapping should be unproblematic. The one metric dragging the grade down is a 91.24% duplication rate, which scored 0; for most assays that would scream PCR/optical over-amplification, but for a small-RNA/miRNA library it is partly expected because the transcriptome is low-complexity (a small number of highly abundant mature miRNAs sequenced deeply at 51 bp). Practically, reuse the data with that caveat in mind: the per-base quality is trustworthy, but treat raw read counts cautiously and rely on UMI-style or post-collapse deduplication for any quantitative miRNA-abundance analysis, since the extreme duplication limits confidence in true molecular complexity.
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.