The systematic assessment of completeness of public metadata accompanying omics studies in the Gene Expression Omnibus data repository.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough -> clean 1:1 reproduction. This is a meta-research paper (metadata-completeness of GEO); the repo (github.com/Mangul-Lab-USC/metadata-completeness @08887ab, authors' own code) ships both its GEO-metadata snapshot (Data/*.csv) and the figure notebooks. I re-ran the authors' own analysis logic (Notebooks/Main_Figures_metadata_project.ipynb cells 1-29 and 45-55) on their shipped D1 data via a «our HPC» SLURM job (2176087, conda base, pandas 3.0.2). 12/12 checked D1 numbers reproduced: 11 EXACT, 1 within-tolerance (publication-side mean 58.54% vs reported 59.0%, 0.46pp). Notably the Figure-1 bar percentages [4.7,33.2,21.3,29.2,11.5] and pie counts [12,84,54,74,29] are HARDCODED literals in the notebook; recomputing them from the shipped study-wide CSVs reproduces them to the digit (incl. the human/non-human split) -> fabrication check PASS, the headline figures are faithfully backed by the shipped data. NOT attempted (the hard ~20%): (a) re-running the GEO XML collection scripts over all of GEO -- non-reproducible by construction (GEO grows; a fresh pull cannot match the frozen snapshot), so I used the authors' shipped snapshot, which is standard for meta-research; (b) the D2 large-scale headline numbers (63.2% overall, human 47.4%, non-human 63.5%, age 19%/sex 13.8%/race 4.2%) -- D2 export CSVs are shipped but the notebook path to those figures is less cleanly pinned, so per 80/20 I stopped at the fully-clean D1 results; (c) Figure-2 per-phenotype sample-level percentages and the '74.8% phenotypes available' aggregate (more ambiguous PR+OP-additional metric).
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 99assessed: 2026-06-14 ⛓ e96a66b50508
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusHow complete is the public metadata accompanying omics studies shared in the textual content of publications versus public repositories such as the Gene Expression Omnibus (GEO), and what gaps exist in metadata sharing across human and non-human studies?
- ★ Over 25% of critical metadata are omitted, with only 74.8% of relevant phenotypes available in publications or public repositories. finding
- ★ Public repositories contain more complete metadata (62% of phenotypes) than the textual content of publications, surpassing publications by ~3.5%. finding
- ★ Only 11.5% of studies completely shared all phenotypes, while 37.9% shared less than 40% of phenotypes. finding
- ★ Studies with non-human samples are more likely to include complete metadata than human studies. finding
- ★ Metadata availability has increased substantially over time, with marked improvement in studies released after 2018/2021. finding
- ★ Metadata completeness is operationally defined as the presence of six phenotypic attributes: race/ethnicity/ancestry, age, sex, tissue/cell type, organism, and strain information. method
- ★ A custom Python/automated XML parsing pipeline retrieves repository metadata, complemented by manual audit of 253 studies to confirm parser accuracy. method
- Race/ethnicity/ancestry is the least frequently reported phenotype, with only up to 20% of human studies reporting it. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Manual metadata curation/audit of publication text (main text + supplementary materials) | 253 randomly selected human and non-human mammalian omics studies (D1 dataset, >164,000 samples) | none | presence/availability of six phenotypic attributes (organism, sex, age, tissue, race/ethnicity/ancestry, strain) per study and per sample | — |
| Automated metadata extraction via custom Python scripts / XML parsing from public repositories | Public repository entries for the 253 D1 studies | none | availability of six phenotypes in repository records | custom Python scripts |
| Large-scale automated XML parsing of repository metadata | 61,312 genomics/transcriptomics GEO records (D2 dataset, >2.1 million samples, deposited 2008-2024) | none | metadata availability across phenotypes by organism type and over time | GEO repository |
- – Average phenotype availability across publications and repositories 74.8%
- – Proportion of studies sharing all six phenotypes (100%) 11.5%
- – Proportion of studies sharing less than 40% of phenotypes 37.9%
- ▲ Metadata availability in public repositories vs publications 62.0% in repositories, >3% (3.5%) higher than publications
- – Overall metadata availability of 2.1 million GEO samples (D2) 63.2%
- ▲ Non-human studies share more phenotypes than human studies in GEO (D2) 16.1% more phenotypes
- ▲ Age information more available in repositories than publications 17.9% more samples
- ▲ Studies released after 2021 incorporating metadata vs <1% before 2011 up to 50% (vs <1% before 2011)
- count 253 studies (randomly selected studies in D1 dataset)
- count 164,909 samples (total samples in D1 dataset (153 human, 100 non-human studies))
- count 61,312 records (GEO genomics/transcriptomics records in D2 dataset (2008-2024))
- count 2.1 million samples (total GEO samples in D2 dataset)
- other 74.8% (average phenotypes shared across publications/repositories)
- other 62.0% (metadata availability in public repositories)
- other 63.2% (overall metadata availability of 2.1 million GEO samples)
- other 42% (attributes disclosed in one source but absent from its counterpart)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This cross-sectional descriptive study assessed metadata completeness across 253 manually curated omics studies (D1, >164,000 samples) and an extended automated survey of 61,312 GEO records (D2, >2.1 million samples). Results were summarized as proportions and percentages of studies or samples reporting each of six phenotypic attributes, stratified by organism type (human vs. non-human) and year of publication. No formal inferential hypothesis tests were named in the available text; all group comparisons (human vs. non-human, repository vs. publication) were expressed as percentage-point differences without p-values or confidence intervals.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Descriptive proportions and percentage-point comparisons | Overall metadata availability; human vs. non-human studies; repository vs. textual content of publications; per-phenotype reporting rates across 253 studies and 61,312 GEO studies | 253 studies / >164,000 samples (D1); 61,312 studies / >2.1 million samples (D2) | not stated |
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Human vs. non-human differences in metadata completeness were reported as raw percentage-point differences (e.g., non-human studies had 16.1% more phenotypes shared)↳ Could also: A two-proportion z-test or chi-square test could also have been applied to each binary completeness outcome (attribute reported yes/no per study) to accompany the descriptive percentages — Formal tests would provide p-values and confidence intervals for the human/non-human gap, allowing readers to assess whether observed differences are likely to exceed chance variation given the sample sizes available
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Temporal trends in metadata availability were described qualitatively (e.g., 'less than 1% before 2011', 'drastic increase after 2018', 'as many as 50% after 2021')↳ Could also: Logistic regression of study-level binary completeness (or a fractional outcome model for the 0–1 proportion score) on year of publication could also have been used — Regression would quantify the rate of change per year, control for potential confounders such as organism type or disease domain, and yield an odds ratio or slope with a confidence interval rather than a qualitative description
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Metadata completeness per study was summarized by binning studies into discrete thresholds (20%, 40%, 60%, 80%, 100% of six attributes reported)↳ Could also: A continuous completeness score (count of 0–6 attributes, or 0–100% proportion) could also have been summarized with median and interquartile range, and compared between groups with a Mann-Whitney U test — Continuous or ordinal scoring preserves more information than discrete bins and enables distributional comparisons without imposing arbitrary cut-points, while the non-parametric test avoids distributional assumptions on the bounded score
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Availability of individual phenotypes was compared between repositories and the textual content of publications as percentage-point differences, treating the two sources independently↳ Could also: McNemar's test could also have been applied to each paired (publication, repository) binary attribute observation within the same study — Because the same study contributes a data point to both the publication and the repository columns, the proportions are paired; McNemar's test accounts for this within-study dependency and would yield a formal p-value for each attribute's source-level discordance
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All proportions and percentages are reported as point estimates without any measure of uncertainty↳ Could also: Wilson or Clopper-Pearson confidence intervals for proportions could also have been reported alongside each point estimate — Confidence intervals communicate the precision of each estimate given the finite number of studies sampled, making it easier to judge whether observed differences between groups or sources are practically distinguishable from sampling variability
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The curated 253-study D1 dataset and the broader 61,312-study D2 dataset were analyzed separately and findings compared narratively↳ Could also: A multilevel or stratified model pooling both datasets (with dataset as a fixed covariate or sampling-frame stratum) could also have been considered — Pooling with an explicit dataset indicator would allow a formal test of whether trends observed in the manually curated sample generalize to the broader GEO population while accounting for the different sampling frames of the two datasets
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Reproduction scope — pmid-40926267
Paper: The systematic assessment of completeness of public metadata
accompanying omics studies in the Gene Expression Omnibus data repository.
Genome Biol 2025. DOI 10.1186/s13059-025-03725-0.
Repo: github.com/Mangul-Lab-USC/metadata-completeness @ 08887ab5e8f1 (own code, P16 N/A).
Zenodo: 10.5281/zenodo.16734885.
Nature of the study
A meta-research / data-curation study. There is no wet lab. The "pipeline" is:
- Collection (
Script/{get_xml_files,dl_recs_Final,pull_metadata}.py): download GEO MINiML XML per GEO series over FTP, parse sample-level fields into metadata CSVs. → ships asData/*.csv. - Analysis (
Notebooks/*.ipynb): from those CSVs, score per-sample and per-study metadata completeness (presence of Age, Organism, Sex, Tissue, Ethnicity/Strain in the public repository [PR] vs the original publication [OP]) and produce the figures/headline numbers.
The repo ships two datasets:
- D1 — 253 manually-curated studies (153 human + 100 non-human), 164,909 samples. Both the collected sample-level CSVs AND the study-wide completeness CSVs are shipped → fully self-contained.
- D2 — ~61,950 GEO studies / 2.1M samples, large-scale automated pull
(
Data/Geo_Metadata_Project_D2/export_*.csv).
IN SCOPE (attempted — clearly specified, self-contained = the 80%)
All from the D1 shipped data, re-running the authors' own notebook logic:
- D1 dataset composition counts (studies, human/non-human split, sample counts).
- Per-sample mean metadata availability, public repository (PR) vs original publication (OP), overall and by organism category (Main_Figures cells 1-29).
- Per-study phenotype-sharing distribution over the 5 phenotypes
(Main_Figures cells 45-55) — i.e. Fig 1 bar
[4.7,33.2,21.3,29.2,11.5]and the "11.5% complete / 37.9% < 40%" abstract claims. This doubles as a fabrication check: those plot numbers are hardcoded in the notebook, so we recompute them from the shipped study-wide CSVs and check they agree.
OUT OF SCOPE (not attempted — the hard ~20%, with reasons)
- Re-running the GEO collection (Script/*.py over all of GEO): non-reproducible by construction — GEO grows daily, so a fresh pull would not match the frozen D1/D2 snapshots. We use the authors' shipped snapshot instead (standard for meta-research reproduction).
- D2 headline numbers (63.2% overall, human 47.4%, non-human 63.5%, age 19% / sex 13.8% / race 4.2%): D2 export CSVs are shipped but the notebook path that turns them into those figures is less cleanly pinned; per 80/20 we stopped at the fully-clean D1 results. Not a drop — simply not attempted.
- Figure 2 per-phenotype sample-level percentages & the "74.8% phenotypes available" / repository-vs-publication +3% age-17.9% details: derivable but the exact "PR + OP-additional" metric is more ambiguous; not attempted.
- Wet-lab / manual curation steps: none exist.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Clean 1:1 reproduction of the D1 pipeline: 12/12 checked numbers agree (11 exact, 1 within 0.46pp), all derived by re-running the authors' own notebook on their own shipped GEO-metadata snapshot. The only deviation is a sub-percent rounding gap on the publication-side mean (58.54% vs 59.0%) — technical, on neither the authors' nor our methodological side. A genuine fabrication check (independently recomputing the hardcoded Fig.1 bar/pie literals from the shipped CSVs) passed to the digit, including the human/non-human split. Note the reproduction scoped to D1 and did not attempt the D2 large-scale numbers or Fig.2, but for every claim actually checked the central conclusion of metadata incompleteness is fully confirmed.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.