SARS-CoV-2 ENA submission workflow + guidance for structuring and releasing metadata v1
Bibliographic record
Abstract
PURPOSE: This workflow provides an overview on the metadata specification recommended for SARS-CoV-2 sequence data and a series of protocols outlining the steps for ENA submission. PHA4GE Contextual Metadata SOP This protocol provides step-by-step instructions for populating the template, and also addresses a number of ethical, privacy and practical considerations that should be discussed with your data steward prior to any type of data sharing. The appendices provide additional instructions and examples of how to curate sample type descriptions, and how to identify additional standardized terms should you need them. SOP for populating EBI submission templates (ENA) Guidance for populating the ENA metadata template using PHA4GE fields and terms. SARS-CoV-2 EBI submission protocol: ENA, BioSample, and BioProject 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. SARS-CoV2 EBI assembly submission protocol Required: established BioProject and BioSamples Submit SARS-CoV-2 assemblies (consensus sequences) to ENA 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 | 0.031 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.194 | 0.366 |
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".