Advances in global change research require open science by individual researchers
Bibliographic record
Abstract
Abstract Understanding how species and ecosystems respond to climate change requires spatially and temporally rich data for a diverse set of species and habitats, combined with models that test and predict responses. Yet current study is hampered by the long‐known problems of inadequate management of data and insufficient description of analytical procedures, especially in the field of ecology. Despite recent institutional incentives to share data and new data archiving infrastructure, many ecologists do not archive and publish their data and code. Given current rapid rates of global change, the consequences of this are extreme: because an ecological dataset collected at a certain place and time represents an irreproducible set of observations, ecologists doing local, independent research possess, in their file cabinets and spreadsheets, a wealth of information about the natural world and how it is changing. Although large‐scale initiatives will increasingly enable and reward open science, we believe that change demands action and personal commitment by individuals – from students and PIs. Herein, we outline the major benefits of sharing data and analytical procedures in the context of global change ecology, and provide guidelines for overcoming common obstacles and concerns. If individual scientists and laboratories can embrace a culture of archiving and sharing we can accelerate the pace of the scientific method and redefine how local science can most robustly scale up to globally relevant questions.
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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 | 0.283 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.029 | 0.039 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".