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
31/100 · FStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
This is a bulk RNA-seq run (Illumina Genome Analyzer IIx, human) that fails QC at 31/100 (grade F), and the verdict is driven overwhelmingly by base-call quality rather than library artifacts. The decisive negatives are pct_q30_bases at 31.1% (only ~31% of bases meet Q30) and a mean base quality of just 15.6 — both scored 0/100 — meaning the majority of base calls carry a high error probability, which directly undermines variant calling and confident read mapping and would inflate spurious mismatches in expression quantification; the very short 36 bp reads further compound multi-mapping ambiguity. On the credit side, adapter content is effectively zero (100/100) and the 36.45% duplication rate is acceptable for RNA-seq (86/100), so the problem is intrinsic sequencing quality, not contamination or over-amplification, consistent with this being an older first-generation Illumina platform. Notably, evidence_strength is 1, so while the QC-driving quality metrics are genuinely measured, the read/base totals are only reported — the headline grade is trustworthy for the quality call but a fuller measured pass is still warranted; overall I would not reuse this dataset where base accuracy matters (variant detection, allele-specific or precise quantitative analysis).
The F 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.