Harnessing open science practices to teach ecology and evolution using interactive tutorials
Bibliographic record
Abstract
Open science skills are increasingly important for a career in ecology and evolution as efforts to make data and analyses publicly available and transparent continue to become more commonplace. However, open science skills are not typically taught in biology undergraduate programs. In learning core concepts in ecology and evolutionary biology (EEB), students must also gain skills in open science or they will miss out on opportunities to prepare for future careers. Core open science skills, like programming and practices that promote reproducibility and data sharing, can be taught to undergraduate students alongside core concepts in EEB. Yet, a major challenge in teaching open science skills and EEB concepts simultaneously is the high cognitive load associated with teaching 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 were developed through a graduate student based working group entitled Data Bytes in Ecology and Evolutionary Biology. These tutorials combine teaching data literacy and programming (using R) with analyzing publicly available data sets to teach fundamental ecological and evolutionary concepts and by doing so, introduce students to open science concepts and tools. Spanning a variety of EEB topics and skill levels, these tutorials serve as examples of how educators can integrate open science tools, programming, and data literacy into undergraduate teachings in topics of ecology and evolution.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.016 |
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".