What Do Biomedical Researchers Want from Biobanks? Results of an Online Survey
Bibliographic record
Abstract
Aims: The purpose of biobanking is to provide biospecimens and associated data to researchers, yet the perspectives of biobank research users have been under-investigated. This study aimed to ascertain biobank research users' needs and opinions about biobanking services. Methods: An online survey was developed, which requested information about researcher demographics, localities of biobanks accessed, methods of sourcing biospecimens, and opinions on topics including but not limited to, application processes, data availability, access fees, and return of research results. There were 27 multiple choice/check box questions, 4 questions with a 10-point Likert scale, and 8 questions with provision for further comment. A web link for the survey was distributed to researchers in late 2019/early 2020 in four Australian states: New South Wales, Victoria, Western Australia, and South Australia. Results: Respondents were generally satisfied with biobank application processes and the fit for purpose of received biospecimens/data. Nonetheless, most researchers (n = 61/99, 62%) reported creating their own collections owing to gaps in sample availability and a perceived increase in efficiency. Most accessed biobanks (n = 58/74, 78%) were in close proximity (local or intrastate) to the researcher. Most researchers had limited the scope of their research owing to difficulty of obtaining biospecimens (n = 55/86, 64%) and/or data (n = 52/85, 60%), with the top three responses for additional types of data required being “more long term follow up data,” “more clinical data,” and “more linked government data.” The top influence to use a particular biobank was cost, and the most frequently suggested improvement was reduced direct “cost of obtaining biospecimens.” Conclusion: Biobanks that do not meet the needs of their end-users are unlikely to be optimally utilized or sustainable. This survey provides valuable insights to guide biobanks and other stakeholders, such as developing marketing and client engagement plans to encourage local research users and discouraging the creation of unnecessary new collections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".