A Framework for Biobank Sustainability
Bibliographic record
Abstract
Each year funding agencies and academic institutions spend millions of dollars and euros on biobanking. All funding providers assume that after initial investments biobanks should be able to operate sustainably. However the topic of sustainability is challenging for the discipline of biobanking for several major reasons: the diversity in the biobanking landscape, the different purposes of biobanks, the fact that biobanks are dissimilar to other research infrastructures and the absence of universally understood or applicable value metrics for funders and other stakeholders. In this article our aim is to delineate a framework to allow more effective discussion and action around approaches for improving biobank sustainability. The term sustainability is often used to mean fiscally self-sustaining, but this restricted definition is not sufficient for biobanking. Instead we propose that biobank sustainability should be considered within a framework of three dimensions - financial, operational, and social. In each dimension, areas of focus or elements are identified that may allow different types of biobanks to distinguish and evaluate the relevance, likelihood, and impact of each element, as well as the risks to the biobank of failure to address them. Examples of practical solutions, tools and strategies to address biobank sustainability are also discussed.
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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 | 0.033 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".