Don’t make genetic data disposable: Best practices for genetic and genomic data archiving
Bibliographic record
Abstract
In ecology and evolution, genetic and genomic data are commonly collected for a vast array of scientific and applied purposes. Despite mandates for public archiving, such data are typically used only once by the data-generating authors. The repurposing of genetic and genomic datasets remains uncommon because it is often difficult, if not impossible, due to non-standard archiving practices and lack of contextual metadata. But as the new research field of macrogenetics is demonstrating, if genetic data and their metadata were more accessible, they could be reused for many additional purposes, far beyond their initial intended impact. In this review, we outline the main challenges with existing genetic and genomic data archives, factors underlying the challenges, and current best practices for archiving genetic and genomic data. Recognising that this is a longstanding issue due to an absence of formal data management training within the research field of ecology and evolution, we highlight key steps that universities, funding bodies, and scientific publishers could take to ensure timely change towards good data archiving.
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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 | 0.118 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.028 | 0.044 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".