Finding the Value in Biobanks: Enhancing the CTRNet Locator
Bibliographic record
Abstract
Biobanks are a critical piece of Research Infrastructure (RI). However, biobanks need to accept the reality of a life cycle for RIs. Until recently, strategies to sustain biobanks have been commonly focused on ways to maintain current operational models. However, sustaining biobanks as they exist today may be increasingly challenging in the face of the disruption in health and research priorities caused by the COVID-19 pandemic. In this opinion article, we review the current and emerging future drivers of biobank value for their researchers, institutions, and funders, highlighting utilization and impact of research performed using the biobank as key measures of future value. While biobanks can only indirectly influence the specific impact of the research performed, they can transform themselves to more actively redefine utilization to their advantage. Utilization means more than the balance of samples and data in versus out. Utilization means redirecting expertise to best support end users, and importantly, closing the operating gap between biobanks and their end users who seek to find the right biospecimens and data to pursue their research. We discuss the specific role of locators (those created by public investment) in closing this gap and the need for additional tools for researchers, before and subsequent to connecting with locators. For the former, we specifically propose that more support is needed to assist researchers in the decision as to how to best obtain biospecimens and navigate the options as to whether finding existing biospecimens and data held by a biobank is the optimal solution for a given project, or whether the optimal solution is either contracting with a biobank to collect samples or creating a new biobank. We believe this type of biospecimen navigator platform will help to maximize utilization of current biobank resources, and also promote the services and expertise in biobanks to better serve researchers' 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 | 0.064 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.024 | 0.034 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.018 |
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".