Wildlife biology, big data, and reproducible research
Bibliographic record
Abstract
ABSTRACT Changes in technology have made it possible to gather vast amounts of data, often of high quality, that in turn can improve the quality of wildlife biology. However, with this growth in data, practices such as data management, exploratory data analysis, data‐sharing, and reproducibility of an analysis have become increasingly complex. These practices often depend heavily on computer scripting languages, and are often hidden from the peer‐review process despite their influence on the final results. Although these issues have been discussed in the literature, they are generally dealt with in a piecemeal fashion, preventing synthesis, and thereby slowing progress. We offer a conceptual framework to illustrate relationships among these practices, and show where wildlife biology as a field has embraced these changes, where awareness is growing, and where it lags behind other fields. We then present several case studies to emphasize the importance of adopting these practices. Any of these case studies could have been conducted with little attention to these practices or employing scripting languages, but there are many disadvantages to this approach including increased chance of errors, inefficiency, and lack of reproducibility. We suggest that a change in the culture of how wildlife biology is conducted is required and that this change will be fostered by integrating these practices into wildlife biology education, implementation, and embracing the idea of open data and open computer code. © 2018 The Wildlife Society.
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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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; both teacher heads agree on what is shown here.
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".