Quantifying the impact of workshops promoting microbiome data standards and data stewardship
Bibliographic record
Abstract
The field of microbiome research continues to grow at a rapid pace, with multi-omics approaches becoming widely used to interrogate diverse microbiome samples. However, due to lagging awareness and implementation of standards and data stewardship, many datasets are produced that are not comparable, reproducible, or reusable. In 2021, the National Microbiome Data Collaborative launched its Ambassador Program, which utilizes a community-learning model to annually train a cohort of early-career researchers in microbiome data stewardship best practices. These Ambassadors then host workshops and other events to communicate these themes to their respective microbiome research communities. To quantify the impact of this learning model for promoting awareness of and experience with microbiome data, we conducted a survey of workshop participants from events hosted by the 2023 Ambassador cohort. The 2023 cohort of 13 National Microbiome Data Collaborative Ambassadors collectively hosted 21 events, reaching over 550 researchers. The Ambassadors distributed an anonymous post-workshop survey to their event participants to quantify the effectiveness of the training materials, the workshop format, and the thematic content. From the 21 events, survey results were successfully collected for 15 of those events from a total of 122 researchers. Overall, 122 participants working with a range of microbiome types and from a variety of institutions responded to the survey and reported overwhelmingly positive experiences with the workshop content and materials, with 98% of respondents reporting that they gained knowledge from the event. Participants across the events also reported an increase in their post-workshop understanding of metadata standards, principles for microbiome data management and reporting, and the importance of standardization in microbiome data processing. Participants also expressed a willingness to apply what they learned about microbiome data stewardship to their own research. The results of this study demonstrate the effectiveness of hands-on workshops and community-learning for communicating data stewardship best practices to microbiome researchers. The lessons learned and details about the implementation of this cohort-based learning model contained herein are intended to assist other groups in their efforts to create or improve similar learning strategies.
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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 | 0.040 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".