Harnessing open science practices to teach ecology and evolutionary biology using interactive tutorials
Bibliographic record
Abstract
Open science skills are increasingly important for a career in ecology and evolutionary biology (EEB) as efforts to make data and analyses publicly available continue to become more commonplace. While learning core concepts in EEB, students are also expected to gain skills in conducting open science to prepare for future careers. Core open science skills like programming, data sharing, and practices that promote reproducibility can be taught to undergraduate students alongside core concepts in EEB. Yet, these skills are not always taught in biology undergraduate programs, and a major challenge in developing open science skills and learning EEB concepts simultaneously is the high cognitive load associated with learning multiple disparate concepts at the same time. One solution is to provide students with easily digestible, scaffolded, pre-formatted code in the form of vignettes and interactive tutorials. Here, we present six open source teaching tutorials for undergraduate students in EEB. These tutorials teach fundamental ecological concepts, data literacy, programming (using
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".