Bibliographic record
Abstract
Background: Medical science data hold significant value, and open platforms play a crucial role in unlocking this potential. While relevant platforms are being developed, the overall usage of these data values remains limited. Objective: This study aims to propose a set of practical and effective data value evaluation processes and methods for medical science data open platforms, enabling them to manage and unlock the value of these data. Methods: Integrating the information system success model, technology acceptance model, and consumer perceived value theory, a set of medical science data value assessment index systems was developed by adopting the literature review and expert survey methods. Data from 10 domestic and international open platforms were collected and empirically analyzed using the entropy-weighted Technique for Order Preference by Similarity to Ideal Solution technique. Results: Based on the scores of each indicator, the intragroup correlation coefficient was calculated to be 0.489, indicating consistency in the evaluation. The highest information entropy values and weights determined using the entropy weighting method were the number of datasets (0.70, 17.68%), data timeliness (0.77, 13.44%), search comprehensiveness (0.78, 12.92%), and system responsiveness (0.80, 11.55%), respectively. Based on the weighted analysis, the platform with the highest overall score was the National Population Health Sciences Data Center, with a score of 62.32. Conclusions: The evaluation index system and model developed can be used not only to optimize the platform's data value evaluation processes, but also to enhance the platform's overall data value and encourage users to reuse data.
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 | 0.115 | 0.152 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".