Data and Software Sharing Guidance for Authors Submitting to AGU Journals
Bibliographic record
Abstract
Data and software are the building blocks of the research published in the AGU journals. These digital objects need to be accessible, understandable, and open as possible for reuse to support transparency and replicability. These digital objects include: Data from observations collected in the field; Data from satellites (primarily level 2 or 3); Data from laboratory experiments; Software used for analysis and visualization of the data; Software used to produce model output; All data displayed in the figures of the paper. Data and Software Availability Statements and Citations must satisfy AGU’s Data and Software for Authors requirements before publication. In this document, we offer guidance, templates, and examples to assist authors in meeting these requirements. The final determination of whether a manuscript meets these requirements is made by the journal editors. Author feedback is appreciated to help ensure that the process remains efficient, feasible, and meaningful. AGU recognizes that not all data or software can be fully open. Data or software that are sensitive or restricted must be protected through appropriate access controls. Data or software should be as open as possible, as closed as necessary. For data concerning Indigenous Peoples, authors should consult the CARE Principles for Indigenous Data Governance. This work is supported by Accelerating Open and FAIR Data Practices Across the Earth, Space, and Environmental Sciences: A Pilot with the NSF to Support Public Access to Research Data project funded by the National Science Foundation, Grant 2025364.
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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 | 0.155 | 0.545 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.492 | 0.563 |
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".