To share or not to share in the emerging era of big data: perspectives from fish telemetry researchers on data sharing
Bibliographic record
Abstract
The potential for telemetry data to answer complex questions about aquatic animals and their interactions with the environment is limited by the capacity to store, manage, and access data across the research community. Large telemetry networks and databases exist, but are limited by the actions of researchers to share their telemetry data. Promoting data sharing and understanding researchers’ views on open practices is a major step toward enhancing the role of big data in ecology and resources management. We surveyed 307 fish telemetry researchers to understand their perspectives and experiences on data sharing. A logistic regression revealed that data sharing was positively related to researchers with collaborative tendencies, who belong to a telemetry network, who are prolific publishers, and who express altruistic motives for their research. Researchers were less likely to have shared telemetry data if they engage in radio and (or) acoustic telemetry, work for regional government, and value the time it takes to complete a research project. We identify and provide examples of both benefits and concerns that respondents have about sharing telemetry data.
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.
How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".