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 human miRNA-Seq library (small-RNA fraction, single-end 35 bp reads on a HiSeq 1000), and it earns a solid B (83/100): base quality is excellent, with 96.6% of bases at Q30 and a mean base quality of 37.6, plus zero adapter and N content, so the reads themselves are clean and confidently called. The grade is held back almost entirely by a 91.36% duplication rate, which scored 0 and is the single dominant drag on the score; while very high duplication is expected and largely benign in miRNA-Seq (a small number of mature miRNA species are sequenced very deeply), it does mean the ~47M reads collapse to far fewer unique molecules, limiting effective library complexity and the detection of low-abundance miRNAs. For reuse this dataset is trustworthy for profiling and quantifying moderately-to-highly expressed miRNAs, but treat low-count features and any novel/rare-species claims cautiously. Note that evidence_strength is low (1) because the headline volume figures (total_reads, total_bases, checksum) are reported rather than independently measured, so this reading is provisional pending a full measured QC pass.
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.