Crossing the streams: improving data quality and integration across the One Health genomics continuum with data standards and implementation strategies
Bibliographic record
Abstract
The Canadian Genomics Research and Development Initiative for Antimicrobial Resistance (GRDI-AMR) uses a genomics-based approach to understand how health care, food production and the environment contribute to the development of antimicrobial resistance. Integrating genomics contextual data streams across the One Health continuum is challenging because of the diversity in data scope, content and structure. To better enable data harmonization for analyses, a contextual data standard was developed. However, development of standards does not guarantee their use. Implementation strategies are critical for putting standards into practice. This work focuses on the development of implementation strategies to better operationalize data standards across the Canadian federal genomics ecosystem. Results include improved understanding of complex data models that can create challenges for existing systems. Technical implementation strategies included spreadsheet-based solutions, new exchange formats, and direct standards integration into new databases. Data curation exercises highlighted common data collection and sharing issues, which informed improved practices and evaluation procedures. These new practices are contributing to improved data quality and sharing within the GRDI-AMR consortium as evidenced by publicly available datasets. The implementation strategies and lessons learned described in this work are generalizable for other standards and can be applied more broadly within other initiatives.
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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 | 0.405 | 0.415 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.033 | 0.034 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".