Protocol for Systematic Analysis Data Elements v1
Bibliographic record
Abstract
This protocol provides a detailed framework for the systematic annotation and analysis of the semantic interoperability of data elements in data shared through repositories. We applied this protocol for the analysis of spinal cord injury (SCI) research data shared through the Open Data Commons for Spinal Cord Injury (odc-sci.org), centering on the role and harmonization potential of Community-based Data Elements (CoDEs). It underscores the critical need for systematic analysis to achieve consistent, high-quality data standardization, integral to the reproducibility and evolution of SCI research. The protocol navigates researchers through the adoption and interpretation of data elements in publicly available datasets, focusing on their semantic interoperability and harmonization capabilities. By delineating a clear method for evaluating changes in data reporting practices and the efficacy of data elements, this protocol not only bolsters data transparency and reusability but also contributes significantly to the credibility and collaborative progress of the SCI research field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.173 | 0.308 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.385 | 0.128 |
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".