Provenance — who produced it, who reused it
Linked to 2 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.
2 further papers cite this accession but reuse could not be confirmed.
Deep data QC
100/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
This whole-genome shotgun sequencing run of the bacterium Agrilactobacillus composti on an Illumina HiSeq 2000 earns a clean A: the per-base quality is genuinely excellent, with a measured 93.3% of bases at Q30 and a mean base quality of 35.9, meaning the reads carry very few sequencing errors and should support confident assembly and variant calling. The two metrics that most justify the top grade are that high Q30 fraction (the single highest-weighted term, indicating reliable base calls across the run) and the very low adapter content of 0.01%, which means little to no contaminating adapter sequence will distort downstream alignment or assembly; a modest 7.11% duplication rate is well within normal bounds and does not meaningfully inflate coverage. One real caveat for reuse: the most basic integrity and yield figures — checksum, total bases, and total reads — are only reported rather than independently measured (evidence_strength=1), so while the quality-relevant FastQC-style metrics are measured and trustworthy, the headline volume/integrity numbers should be treated as provisional until a deep measured pass confirms them. In short, the read quality is strong enough to reuse with confidence for the bacterium's WGS, with the only asterisk being the unverified yield/checksum claims rather than any data-quality concern.
The A grade is a transparent weighted average. Each metric below scored from 0–100% against the published WGS 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.