Supporting successful data sharing practices in earthquake engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Prior studies identified a need for further comparison of data-sharing practices across different disciplines and communities. Toward addressing this need, the purpose of this paper is to examine the data-sharing practices of the earthquake engineering (EE) community, which could help inform data-sharing policies in EE and provide different stakeholders of the EE community with suggestions regarding data management and curation. Design/methodology/approach This study conducted qualitative semi-structured interviews with 16 EE researchers to gain an understanding of which data might be shared, with whom, under what conditions and why; and their perceptions of data ownership. Findings This study identified 29 data-sharing factors categorized into five groups. Requirements from funding agencies and academic genealogy were frequent impacts on EE researchers’ data-sharing practices. EE researchers were uncertain of data ownership and their perceptions varied. Originality/value Based on the findings, this study provides funding agencies, research institutions, data repositories and other stakeholders of the EE community with suggestions, such as allowing researchers to adjust the timeframe they can withhold data based on project size and the amount of experimental data generated; expanding the types and states of data required to share; defining data ownership in grant requirements; integrating data sharing and curation into curriculum; and collaborating with library and information schools for curriculum development.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.181 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it