The Tao of Open Science for Ecology
Bibliographic record
Abstract
The field of ecology is poised to take advantage of emerging technologies that facilitate the gathering, analyzing, and sharing of data, methods, and results. The concept of transparency at all stages of the research process, coupled with free and open access to data, code, and papers, constitutes "open science." Despite the many benefits of an open approach to science, a number of barriers to entry exist that may prevent researchers from embracing openness in their own work. Here we describe several key shifts in mindset that underpin the transition to more open science. These shifts in mindset include thinking about data stewardship rather than data ownership, embracing transparency throughout the data life-cycle and project duration, and accepting critique in public. Though foreign and perhaps frightening at first, these changes in thinking stand to benefit the field of ecology by fostering collegiality and broadening access to data and findings. We present an overview of tools and best practices that can enable these shifts in mindset at each stage of the research process, including tools to support data management planning and reproducible analyses, strategies for soliciting constructive feedback throughout the research process, and methods of broadening access to final research products.
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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 | 0.073 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.064 |
| Scholarly communication | 0.029 | 0.044 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".