The ELIXIR-Greece Galaxy server including best practices tools and workflows for the analysis of SARS-CoV-2 data
Bibliographic record
Abstract
The Galaxy Community and ELIXIR had organised a webinar available here series to demonstrate how open software and public research infrastructures can be used in analysing and publishing SARS-CoV2 data. Best practices for the analysis of this data were presented for Genomics, Proteomics, Evolution, and Cheminformatics. For the Greek life science community, we installed a local Galaxy instance at https://usegalaxy.elixir-greece.org that provides all tools used in these webinars as well as the standard tools of the reference Galaxy distribution. The instance operates on 3 servers with a total of 48 CPUs, 192 GB total memory and 3ΤΒ disk space, initially without quotas per user. Depending on user needs, additional resources (CPU, RAM, storage) and tools can be installed. Within the Genomics/Variant Calling workbench, the initial analysis of the SARS-CoV-2 genome, as published on bioRxiv can be reproduced and adapted to the users data. Comprehensive training material for different workflows in Galaxy is available at https://training.galaxyproject.org
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.087 |
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".