Spreading the Knowledge: Overviewing the University of Alberta Libraries' Research Data Management Services
Bibliographic record
Abstract
In June 2016 the Tri-Council Agencies, a major source of research funding for post-secondary institutions in Canada, released a Statement of Principles on Digital Data Management which identifies research data management (RDM) as being an essential and shared responsibility between researchers, research communities, research institutions, and research funders. As a major international research library, University of Alberta Libraries (UAL) offers expertise, resources, and services for supporting sound RDM throughout the research lifecycle. In alignment with the Tri-Council statement, UAL has adopted a holistic approach to education and delivering of RDM knowledge and resources across campus, focusing upon a variety of stakeholders. Examples of this include a running series of applied RDM training sessions for liaison librarians, customized information sessions both for Research Services Office and Research Ethics Office staff, and collaborative RDM events and training sessions delivered to researchers and students across campus. Some specific services and platforms offered by UAL include the Portage Data Management Planning (DMP) Assistant, Dataverse, and an open access Education and Research Archive for promoting research discovery, archival, and preservation. This session will provide a brief overview of UAL's RDM services, methods employed for their delivery and uptake, and both current and emerging RDM initiatives.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.036 | 0.064 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".