Incorporating data sharing to the reward system of science
Bibliographic record
Abstract
Purpose It is widely recognized that sharing data is beneficial not only for science but also for the common good, and researchers are increasingly expected to share their data. However, many researchers are still not making their data available, one of the reasons being that this activity is not adequately recognized in the current reward system of science. Since the attribution of data sets to individual researchers is necessary if we are to include them in research evaluation processes, the purpose of this paper is to explore the feasibility of linking data set records from DataCite to the authors of articles indexed in the Web of Science. Design/methodology/approach DataCite and WoS records are linked together based on the similarity between the names of the data sets’ creators and the articles’ authors, as well as the similarity between the noun phrases in the titles of the data sets and the titles and abstract of the articles. Findings The authors report that a large number of DataCite records can be attributed to specific authors in WoS, and the authors demonstrate that the prevalence of data sharing varies greatly depending on the research discipline. Originality/value It is yet unclear how data sharing can provide adequate recognition for individual researchers. Bibliometric indicators are commonly used for research evaluation, but to date no large-scale assessment of individual researchers’ data sharing activities has been carried out.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchBibliometricsOpen science Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.301 | 0.598 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.022 | 0.037 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".