Willing or unwilling to share primary biodiversity data: results and implications of an international survey
Bibliographic record
Abstract
Abstract Biodiversity science and conservation increasingly depend on the sharing and integration of large amounts of data, but many researchers resist sharing their primary biodiversity data. We recently conducted an international survey to ascertain the attitudes, experiences, and expectations regarding biodiversity data sharing and archiving of researchers. The results show that whereas most respondents are willing to share article‐related biodiversity data, more than 60% of respondents are unwilling to share primary data before publishing. Results indicate an underdeveloped culture of data sharing and several major technological and operational barriers. A major concern for researchers is appropriate benefits from data sharing. Most respondents would accept data archiving policies of journals. Researchers also express concerns about how to easily and efficiently deal with data and data quality in public databases. Expectations for biodiversity databases include standardization of data format, user‐friendly data submission tools, formats for different types of data, and coordination among databases. The survey results provide suggestions for improving data sharing and archiving by individual scientists, organizations, journals, and databases.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".