Don’t make genetic data disposable: Best practices for genetic and genomic data archiving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.036 | 0.183 |
| Research integrity | 0.000 | 0.001 |
| 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 it