Data and Software Sharing Guidance for Authors Submitting to AGU Journals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it