Funding Sources for Canadian Biorepositories: The Role of User Fees and Strategies to Help Fill the Gap
Bibliographic record
Abstract
Biorepositories, the coordinating hubs for the collection and annotation of biospecimens, are under increasing financial pressure and are challenged to remain sustainable. To gain a better understanding of the current funding situation for Canadian biorepositories and the relative contributions they receive from different funding sources, the Canadian Tumour Repository Network (CTRNet) conducted two surveys. The first survey targeted CTRNet's six main nodes to ascertain the relative funding sources and levels of user fees. The second survey was targeted to a broader range of biorepositories (n=45) to ascertain business practices in application of user fees. The results show that >70% of Canadian biorepositories apply user fees and that the majority apply differential fees to different user groups (academic vs. industry, local vs. international). However, user fees typically comprise only 6% of overall operational budgets. We conclude that while strategies to drive up user fee levels need to be implemented, it is essential for the many stakeholders in the biomedical health research sector to consider this issue in order to ensure the ongoing availability of research biospecimens and data that are standardized, high-quality, and that are therefore capable of meeting research needs.
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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 | 0.047 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".