Orchestrating a community-developed computational workshop and accompanying training materials
Bibliographic record
Abstract
The importance of bioinformatics, computational biology, and data science in biomedical research continues to grow, driving a need for effective instruction and education. A workshop setting, with lectures and guided hands-on tutorials, is a common approach to teaching practical computational and analytical methods. Here, we detail the process we used to produce high-quality, community-authored educational materials that are available for public consumption and reuse. The coordinated efforts of 17 authors over 10 weeks resulted in 15 workshops available as a website and as a 388-page electronic book. We describe how we utilized cloud infrastructure, GitHub, and a literate programming approach to robustly deliver hands-on tutorials to participants of the annual Bioconductor conference. The scripts, raw and published workshop materials, and cloud machine image are all openly available. Our approach uses free services and software and can be adapted by workshop organizers and authors in other contests with appropriate technical backgrounds.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".