Preparing data managers to support open ocean science: Required competencies, assessed gaps, and the role of experiential learning
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.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".