SARS-CoV-2 EBI submission protocol: ENA, BioSample, and BioProject v1
Bibliographic record
Abstract
This protocol provides the steps needed to establish a new EBI submission environment for your laboratory, including BioProject(s). Once established, this protocol covers raw read submission to EBI and sample metadata to BioSample. For new submitters, there's quite a bit of groundwork that needs to be established before a laboratory can start its first data submission. We recommend that one person in the laboratory take a few days to get everything set up in advance of when you expect to do your first data submission. Two protocols cover the PHA4GE guidance for SARS-CoV-2 submission to EBI Complete in order (1 then 2): 1. SARS-CoV-2 EBI submission protocol: ENA, BioSample, and BioProject (included protocol) Step-by-step instructions for establishing a new EBI (Webin) submission account and for creating and linking a new BioProject to an existing umbrella effort. SARS-CoV-2 raw data submission to ENA (European Nucleotide Archive) and metadata to BioSample. 2. SARS-CoV-2 EBI assembly submission protocol Required: established BioProject and BioSamples Submit SARS-CoV-2 assemblies to EBI, linking to existing BioProject, BioSamples, and raw data.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.222 | 0.347 |
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".