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. Proceedings of the International Conference on Scientometrics & Informetrics (ISSI 2023), Bloomington, Indiana. Preprint: https://doi.org/10.5281/zenodo.7823626 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.
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 | 0.057 | 0.194 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".