Open science and community norms
Bibliographic record
Abstract
While modern genomics research often adheres to community norms emphasizing open data sharing, many genomics institutes and projects have recently nuanced such norms with a corpus of data release policies. In particular, publication moratoria and data retention policies have been enacted to ‘reward’ data producers and ensure data quality control. Given the novelty of these policies, this article seeks to identify and analyse the main features of data retention and publication moratoria policies of major genomics institutes and projects around the world. We find that as more collaborative genomics projects are created, and further genomic research discoveries are announced, the need for more sophisticated yet practical and effective policies will increase. Reward systems should be implemented that recognize contributions from data producers and acknowledge the need to remain dedicated to the goals of open data sharing. To this end, in addition to the current choices of employing data retention or publication moratoria policies, alternative models that would be easier to implement or less demanding on open science should also be considered.
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 | 0.142 | 0.189 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".