Barriers to Data Sharing: New Evidence from a US Survey
Bibliographic record
Abstract
Recent studies demonstrate that the majority of social science data is not preserved or shared through social science data archives and other formal archival arrangements. This motivates further investigation about the various ways researchers share their data (including more "informal" data sharing) and the factors that underlie their data sharing behavior. We developed a survey to collect information from principal investigators (PIs) of federally funded research grants in the US about their experiences with data sharing (n=1,021). We also collected information about various factors that might be related to data sharing behavior including: normative data sharing practices in their discipline, perceived barriers to data sharing, rank/tenure, institutional type, gender and so on. We find that while only 12% of the PIs have archived their data, 45% have shared their data outside the immediate research team. Being in a discipline that favors data sharing is positively associated with the likelihood that a PI shares his or her research data. Perceived barriers to data sharing reduce the likelihood one shares data. Other factors associated with data sharing include rank/tenure status and duration of the grant. Implications for data archives are also discussed.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".