mockrobiota: a public resource for microbiome bioinformatics benchmarking
Bibliographic record
Abstract
Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at https://github.com/caporaso-lab/mockrobiota. The materials contained in mockrobiota include dataset and sample metadata, expected composition data, which are annotated based on one or more reference taxonomies, links to raw data (e.g., raw sequence data) for each mock community dataset, and optional reference sequences for mock community members. mockrobiota does not supply physical sample materials directly, but the dataset metadata included for each mock community indicate whether physical sample materials are available (and associated contact information). At the time of this writing, mockrobiota contains 11 mock community datasets with known species compositions (including bacterial, archaeal, and eukaryotic mock communities), analyzed by high-throughput marker-gene sequencing. The availability of standard, public mock community data will facilitate ongoing methods optimizations; comparisons across studies that share source data; greater transparency and access; and eliminate redundancy. This dynamic resource is intended to expand and evolve to meet the changing needs of the ‘omics community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.033 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".