Fundamental Considerations for Biobank Legacy Planning
Bibliographic record
Abstract
Biobanking in its various forms is an activity involving the collection of biospecimens and associated data and their storage for differing lengths of time before use. In some cases, biospecimens are immediately used, but in others, they are stored typically for the term of a specified project or in perpetuity until the materials are used up or declared to be of little scientific value. Legacy planning involves preparing for the phase that follows either biobank closure or a significant change at an operational level. In the case of a classical finite collection, this may be brought about by the completion of the initial scientific goals of a project, a loss of funding, or loss of or change in leadership. Ultimately, this may require making a decision about when and where to transfer materials or whether to destroy them. Because biobanking in its entirety is a complex endeavour, legacy planning touches on biobank operations as well as ethical, legal, financial, and governance parameters. Given the expense and time that goes into setting up and maintaining biobanks, coupled with the ethical imperative to appropriately utilize precious resources donated to research, legacy planning is an activity that every biobanking entity should think about. This article describes some of the fundamental considerations for preparing and executing a legacy plan, and we envisage that this article will facilitate dialogue to help inform best practices and policy development in the future.
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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 | 0.110 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 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".