The Time Has Come… To Talk About Why Research Data Management Isn’t Easy
Bibliographic record
Abstract
For the last decade, academic libraries have talked with each other and with potential partners about their roles in helping to manage research data and their plans to expand or initiate research data services (RDS). Libraries have the capacity to provide these services, but the range and maturity of research data services from libraries vary considerably. In summer 2019, our team surveyed a sample of academic libraries of all sizes who are members of the Association of College and Research Libraries (ACRL) to find out about their current RDS and plans for the future. This study is a follow-up to surveys of this same group in 2012 and 2015. Our findings include the types of RDS currently being offered in academic libraries, the barriers that hinder RDS implementation, and staff capacity for creating RDS.
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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 | 0.092 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.027 | 0.038 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.029 |
| Insufficient payload (model declined to judge) | 0.029 | 0.020 |
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".