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
Bulk small-RNA (miRNA-Seq) data from mouse on the Illumina HiSeq 2000, this dataset earns a solid B (83/100) on the strength of its base-level quality: 92.5% of bases at Q30 and a mean base quality of 34.8 indicate clean, reliably called reads, and adapter content reading 0% means the libraries are well-trimmed and ready for alignment. The single metric dragging the grade down is an 86.59% duplication rate, which scored 0 and is the main reason this is a B rather than an A — though for a miRNA-Seq library this is partly expected, since a small, highly expressed pool of mature miRNAs naturally produces many identical reads, so treat it as a flag to watch rather than outright contamination. Because the evidence_strength is low (1) and the core size metrics (total_reads, total_bases) are reported rather than independently measured, this reading should be considered provisional and confirmed by a deeper measured QC pass before high-stakes reuse. On balance the data are trustworthy for differential miRNA expression, but verify that your downstream tool handles duplicates appropriately (e.g. UMI-aware or expression-based counting) given the high duplication.
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.