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
Bulk small-RNA (miRNA-Seq) data from mouse on an Illumina HiSeq 2000, and overall it's a solid, fully measured library that earns its B: base quality is excellent (Q30 at 89.4% and mean base quality ~33.9, both scoring 97–98/100), adapter contamination is effectively zero, and the QC reading is well-grounded since every quality metric here was actually measured rather than extrapolated. The one factor pulling the grade down is the 86% duplication rate, which scored 0/100 — though for miRNA-Seq this is expected and largely benign, because the small, highly expressed miRNA repertoire is naturally sequenced to redundancy, so much of that "duplication" reflects genuine biological abundance rather than PCR artifact. The main practical caveat is depth: at ~6M reads of 50 bp single-end data, the library is adequate for profiling abundant miRNAs but offers limited sensitivity for low-expression species, so reuse is well-suited to differential-expression of common miRNAs and less so to discovery of rare ones. Treat the quality numbers as reliable rather than provisional, but interpret the duplication metric in the small-RNA context before letting it deter reuse.
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.