Biobanking in the COVID-19 Era and Beyond: Part 1. How Early Experiences Can Translate into Actionable Wisdom
Bibliographic record
Abstract
The era of COVID-19 has brought about a number of novel challenges for the global biobanking community. To better position the biobanking community to cope with current and future challenges, the International Society for Biological and Environmental Repositories (ISBER) COVID-19 Response Task Force was convened to identify needs and gaps in biobanking tools (existing resources that support good practice), for example, standards, best practices, business, etc. and to make recommendations to benefit the community. Toward these goals, the Task Force assembled a set of questions to explore individual biobanks' experiences, with emphasis on identification of key challenges and approaches, including tools employed. A survey was designed with the use of these questions and administered by ISBER. This article presents a summary of the aggregated data obtained from the survey responses, illustrating some of the major issues encountered and identifying which tools the survey respondents found most useful. In particular, this article focuses on the challenges identified during the early months of the COVID-19 era. Recommendations are provided to support biobank emergency preparedness for the future, address lessons learned, and propose solutions to bridge identified gaps. The analysis and the complete survey dataset will also inform the larger Task Force goal to develop specific tool recommendations.
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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 | 0.071 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.024 | 0.034 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".