Transforming Biospecimen Management: A Roadmap for Integrated Sample Traceability in the Era of Global Research
Bibliographic record
Abstract
INTRODUCTION: Advancements in biomedical research depend on the quality and availability of biological samples. Despite their sophisticated storage capabilities, biobanks face significant challenges in sample management, with stored specimens often remaining unused and researchers struggling to access the required samples. OBJECTIVES: To analyze the challenges in biospecimen access and traceability, evaluate existing solutions, and propose a framework for integrated sample management in global research collaboration. METHODS: A scoping review was conducted across PubMed, Scopus, and Web of Science databases, supplemented by grey literature (2004-2024). The analysis included an examination of Biobank Information Management Systems and an evaluation of sample management systems, tracking technologies, and governance frameworks. RESULTS: The analysis revealed fragmented management systems, with at least 38 different biobanking software solutions offering limited interoperability. Proprietary systems and vendor lock-ins create significant barriers to data sharing. Sample tracking shows the evolution from manual to digital systems; however, cross-institutional tracking remains challenging. Reproducibility issues account for significant challenges in research, whereas inefficient resource utilization persists, with 67% of biobanks citing underutilization as a major concern. CONCLUSIONS: Addressing biobank sample access and traceability requires a shift from an institution-centric to an ecosystem-wide approach. Its success depends on integrating technological solutions such as Blockchain, the Internet of Things, and artificial intelligence with governance frameworks while ensuring alignment with stakeholder needs. Future developments should focus on implementing integrated traceability systems that support transparent and accountable sample management across the global research ecosystem.
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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 | 0.195 | 0.182 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.032 | 0.060 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".