A roadmap for equitable reuse of public microbiome data
Bibliographic record
Abstract
Science benefits from rapid open data sharing, but current guidelines for data reuse were established two decades ago, when databases were several million times smaller than they are today. These guidelines are largely unfamiliar to the scientific community, and, owing to the rapid increase in biological data generated in the past decade, they are also outdated. As a result, there is a lack of community standards suited to the current landscape and inconsistent implementation of data sharing policies across institutions. Here we discuss current sequence data sharing policies and their benefits and drawbacks, and present a roadmap to establish guidelines for equitable sequence data reuse, developed in consultation with a data consortium of 167 microbiome scientists. We propose the use of a Data Reuse Information (DRI) tag for public sequence data, which will be associated with at least one Open Researcher and Contributor ID (ORCID) account. The machine-readable DRI tag indicates that the data creators prefer to be contacted before data reuse, and simultaneously provides data consumers with a mechanism to get in touch with the data creators. The DRI aims to facilitate and foster collaborations, and serve as a guideline that can be expanded to other data types.
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.
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 | 0.098 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".