Slow improvement to the archiving quality of open datasets shared by researchers in ecology and evolution
Bibliographic record
Abstract
Many leading journals in ecology and evolution now mandate open data upon publication. Yet, there is very little oversight to ensure the completeness and reusability of archived datasets, and we currently have a poor understanding of the factors associated with high-quality data sharing. We assessed 362 open datasets linked to first- or senior-authored papers published by 100 principal investigators (PIs) in the fields of ecology and evolution over a period of 7 years to identify predictors of data completeness and reusability (data archiving quality). Datasets scored low on these metrics: 56.4% were complete and 45.9% were reusable. Data reusability, but not completeness, was slightly higher for more recently archived datasets and PIs with less seniority. Journal open data policy, PI gender and PI corresponding author status were unrelated to data archiving quality. However, PI identity explained a large proportion of the variance in data completeness (27.8%) and reusability (22.0%), indicating consistent inter-individual differences in data sharing practices by PIs across time and contexts. Several PIs consistently shared data of either high or low archiving quality, but most PIs were inconsistent in how well they shared. One explanation for the high intra-individual variation we observed is that PIs often conduct research through students and postdoctoral researchers, who may be responsible for the data collection, curation and archiving. Levels of data literacy vary among trainees and PIs may not regularly perform quality control over archived files. Our findings suggest that research data management training and culture within a PI's group are likely to be more important determinants of data archiving quality than other factors such as a journal's open data policy. Greater incentives and training for individual researchers at all career stages could improve data sharing practices and enhance data transparency and reusability.
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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 | 0.281 | 0.571 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".