Legal Agreements and the Governance of Research Commons: Lessons from Materials Sharing in Mouse Genomics
Bibliographic record
Abstract
Omics research infrastructure such as databases and bio-repositories requires effective governance to support pre-competitive research. Governance includes the use of legal agreements, such as Material Transfer Agreements (MTAs). We analyze the use of such agreements in the mouse research commons, including by two large-scale resource development projects: the International Knockout Mouse Consortium (IKMC) and International Mouse Phenotyping Consortium (IMPC). We combine an analysis of legal agreements and semi-structured interviews with 87 members of the mouse model research community to examine legal agreements in four contexts: (1) between researchers; (2) deposit into repositories; (3) distribution by repositories; and (4) exchanges between repositories, especially those that are consortium members of the IKMC and IMPC. We conclude that legal agreements for the deposit and distribution of research reagents should be kept as simple and standard as possible, especially when minimal enforcement capacity and resources exist. Simple and standardized legal agreements reduce transactional bottlenecks and facilitate the creation of a vibrant and sustainable research commons, supported by repositories and databases.
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 | 0.090 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".