Building Capacity in Your Library for Research Data Management Support (Or What We Learned From Offering to Review DMPs)
Bibliographic record
Abstract
In our evolving effort to build infrastructure and support around research data management needs, we found traction in launching a data management plan review service. In doing so, we have been able to achieve multiple goals: 1) support the research process; 2) create active learning situations for subject liaisons to engage in and learn how to support data management planning; 3) find resonance with campus‐sponsored research officers; 4) collaborate with other campus research support groups including campus IT, the institutional review board, and statistical consulting; 5) and participate in the national dialogue about the tensions of data management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.140 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.047 | 0.063 |
| Open science | 0.011 | 0.058 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.205 | 0.110 |
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".