Results of community surveys organized by the members of the BioImaging North America – Quality Control and Data Management Working Group to understand the microscopy reporting and reproducibility needs of the bioimaging community
Bibliographic record
Abstract
In January 2022, members of the BioImaging North America (BINA) Quality Control and Data Management Working Group, held a Community Conversation to introduce a series of articles that had been featured in the FOCUS on Microscopy Reporting and Reproducibility published in the December 2021 issue of Nature Methods. During this event, the authors of the papers featured on the FOCUS issue were invited to present their work and interact with members of the BINA community. A series of community surveys were conducted during this Community Conversation to better understand the audience, their current reporting and reproducibility practices, and their interest in tools and resources to help them better take advantage of these practices. While the results of these polls are limited by the small sample size, this document is published in the hope that these results could be useful to the community to guide the future development of Research Data Management metadata specifications and software tools.
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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 | 0.084 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".