A survey of researchers on rewarding data citation and reuse
Bibliographic record
Abstract
This research-in-progress paper has been accepted to ISSI2023. Please cite as: Ninkov, A., Gregory, K., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (accepted). A survey of researchers on rewarding data citation and reuse. <em>Proceedings of the International Conference on Scientometrics & Informetrics (ISSI 2023)</em>, Bloomington, Indiana. Preprint: https://doi.org/10.5281/zenodo.7823626 <br> This research-in-progress paper presents results from the largest known survey (n=2,492) to explicitly investigate data citation and reuse practices among a representative sample of academic authors across academic disciplines. This analysis focuses on participants’ preferences and attitudes towards rewarding their own data work and that by others. The results indicate that assessing the reach and influence of data is important or extremely important for researchers across disciplines. However, there are significant disciplinary differences identified in preferences for recognition and reward of practices regarding research data management, sharing, citation, reuse and assessment. For example, we observe that assessing the reach and influence of both their own and the data of others, as well as getting credit for reusing data, is more important to researchers in Medical and Health Sciences and Agricultural Sciences than other groups. Additionally, researchers from Social Science found it more important to trace a variety of metrics and information about their own data compared to most other disciplines.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".