SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject v2
Bibliographic record
Abstract
PURPOSE: This is a SARS-CoV-2 specific protocol that covers the steps needed to establish a new NCBI submission environment for your laboratory, including the creation of new BioProject(s) and submission groups. Once these are step up, the protocol then walks through the process for submitting raw reads to SRA and sample metadata to BioSample through the Submission portal. 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. If you need a pipeline for frequent or large volume submissions, follow Step 1 in the SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject to get your NCBI submission environment established, then contact gb-admin@ncbi.nlm.nih.gov to set up an account for submitting through the API. These protocols cover submission using NCBI's Submission Portal web-interface. Complete in order (1 then 2): 1. SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject (included protocol) Step-by-step instructions for establishing a new NCBI laboratory submission account and for creating and linking a new BioProject to an existing umbrella effort. SARS-CoV-2 raw data submission to SRA (Sequence Read Archive) and metadata to BioSample. 2. SARS-CoV-2 NCBI consensus submission protocol: GenBank Required: established BioProject and BioSamples Submit SARS-CoV-2 assemblies to NCBI GenBank, linking to existing BioProject, BioSamples, and raw data. Metadata templates, explained! SOP for populating NCBI submission templates
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".