Implementing Research Data Management Services in a Canadian Context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.025 |
| Open science | 0.015 | 0.037 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it