An infrastructure for qualified data sharing and team science in late-stage translational spinal cord injury research
Bibliographic record
Abstract
The complex and heterogeneous nature of spinal cord injury has limited translational bench-to-bedside results. The wide variety of data, including injury parameters, biochemical, histological, and behavioral outcome measures represent a 'big data' problem, calling for modern data science solutions. There are some instances in which SCI researchers collect sensitive data that needs to remain private, such as datasets designed to meet regulatory approval, sensitive intellectual property, and non-human primate studies. For these types of data, we have developed a Private Data Commons for SCI (PDC-SCI). Our objective is to give an overview of this novel data commons, describing how this type of commons works, how it can benefit the research community, and the cases in which it would be most useful. This private infrastructure is ideal for multi-lab transdisciplinary studies that require a well-organized, scalable data commons for rapid data sharing within a closed, distributed team. As a use-case for the PDC-SCI, we demonstrate the VA Gordon Mansfield SCI Consortium, in which multimodal data from behavior, biomechanics of injury, hospital records, imaging, and histology are integrated, shared, and analyzed to facilitate insights and knowledge discovery.
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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 | 0.065 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.008 | 0.045 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.021 |
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".