Open Data: Nice People Can'T Share!
Bibliographic record
Abstract
Talk for FORCE2018 meeting in Montreal, Qc. One of the most meaningful ways by which scientists can engage with other scientists and with the broader community is by sharing one of their most treasured possessions: data. Indeed, publishers and funders increasingly acknowledge the importance of data as a scientific output. As such, many journals and funders now require that scientists make their data openly accessible, both to promote transparency and to accelerate the pace of scientific discovery. To date, journal policies that mandate data sharing have successfully increased the accessibility to datasets underlying scientific publications. However, is the quality of these data sufficient to allow reuse and reanalysis? Even when journals mandate data sharing, our survey of the ecological and evolutionary literature found that 56% of open datasets were incomplete, and 64% were shared in a way that partially or entirely prevented reuse. Given the highly competitive nature of academia, authors might be wary of openly sharing their data for fear of criticism or of others benefiting from their work at their expense. As such, ‘scholarly altruism’ is often cited as a key reason for why some authors are willing to share high quality data that are complete and readily reusable. In this talk, I will discuss our latest (unpublished) work to test this hypothesis by assessing how researchers’ psychological motivations and level of cooperation in real-world situations relate to the quality of their shared datasets.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.247 | 0.106 |
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".