Bibliographic record
Abstract
Research data management (RDM) has become an increasingly pressing issue for academic libraries as they strive to assist researchers in addressing new public funding requirements surrounding data dissemination and preservation. Briney, Goben, & Zilinski (2015) reviewed several characteristics of RDM service provision efforts by 206 American research universities. Following a similar methodology, the author reviewed RDM service development within Canadian research universities and compared the results to the American efforts. The main area requiring development in Canada is the provision of RDM services. Therefore, some current best practices for implementing RDM services were gathered through a literature review. The successful approaches highlighted in the literature include awareness of funder and institutional data policies, reaching out to data service providers on campus and beyond, understanding researcher data management needs and finding RDM champions, implementing research data services strategically, planning for growth in RDM services, marketing the RDM services, and creating incentives to create data management plans and utilize RDM services. Third Place DJIM Best Article Award.
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 | 0.030 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".