Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management
Bibliographic record
Abstract
Abstract The application of new technologies in scientific research, particularly automated sensing of plant phenotypic performance, has resulted in a deluge of data and raised the question of how these data can be efficiently managed and shared. Many studies have examined the benefits and constraints of data sharing in different disciplines. We focus on plant phenotyping due to the increasing volume of digital data generated in multi‐disciplinary plant phenotyping research. Data sharing and reuse practices in plant phenotyping research have not been widely explored. Study results show that data sharing in plant phenotyping research occurs mostly through direct personal requests based on trust relationships and technical supplements (appendices) to publications, and researchers are willing to share data if incentives and policies are aligned to overcome the barriers. This paper provides empirical evidence to guide the establishment of incentive systems and policy frameworks that support FAIR (findability, accessibility, interoperability, and reusability) data, promote behavioral change, and enhance data sharing for the advancement of science and innovation by research communities, institutions, policymakers, and funders.
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 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 | 0.235 | 0.341 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".