Preparing data managers to support open ocean science: Required competencies, assessed gaps, and the role of experiential learning
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
Ocean science is experiencing an explosion of data as researchers employ a widening variety of sensors, operating at higher fidelity and frequency, to inform our understanding of the global ocean. This is further complicated by the increasing integration of open science data from other disciplines to analyze complex systems, like climate change, animal migration, and sea/air interaction. This shift has been unplanned, chaotic, and emergent, and has placed the onus on researchers to stay current with best practices for managing, analyzing, and sharing data. Ocean scientists who do not have the technical skill to manage this data are turning to technologists, on the assumption they have the expertise required to help. To test this assumption, we examined an experiential learning program that placed technologists at ocean data centres in Canada, conducting interviews with students and employers to identify the competencies they believed were required to manage ocean data, which were missing in students' education up to that point, and which students gained during the work term placement.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.019 | 0.061 |
| Open science | 0.027 | 0.057 |
| 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