The PHA4GE Microbial Data-Sharing Accord: establishing baseline consensus microbial data-sharing norms to facilitate cross-sectoral collaboration
Bibliographic record
Abstract
Microbial data sharing underlies evidence-based microbial research, as well as pathogen surveillance and analysis essential to public health. While the need for data sharing was highlighted during the SARS-CoV-2 pandemic, some concerns regarding secondary data use have also surfaced. Although general guidelines are available for data sharing, we note the absence of a set of established, universal, unambiguous and accessible principles to guide the secondary use of microbial data. Here, we propose the Public Health Alliance for Genomic Epidemiology (PHA4GE) Microbial Data-Sharing Accord to consolidate consensus norms and accepted practices for the secondary use of microbial data. The Accord provides a set of seven simple, baseline principles to address key concerns that may arise for researchers providing microbial datasets for secondary use and to guide responsible use by data users. By providing clear rules for secondary use of microbial data, the Accord can increase confidence in sharing by data providers and protect against data mis-use during secondary analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".