The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics
Bibliographic record
Abstract
A genomic database of all Earth's eukaryotic species could contribute to many scientific discoveries; however, only a tiny fraction of species have genomic information available. In 2018, scientists across the world united under the Earth BioGenome Project (EBP), aiming to produce a database of high-quality reference genomes containing all ~1.5 million recognized eukaryotic species. As the European node of the EBP, the European Reference Genome Atlas (ERGA) sought to implement a new decentralised, equitable and inclusive model for producing reference genomes. For this, ERGA launched a Pilot Project establishing the first distributed reference genome production infrastructure and testing it on 98 eukaryotic species from 33 European countries. Here we outline the infrastructure and explore its effectiveness for scaling high-quality reference genome production, whilst considering equity and inclusion. The outcomes and lessons learned provide a solid foundation for ERGA while offering key learnings to other transnational, national genomic resource projects and the EBP.
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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 | 0.083 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".