An Overview of the University of Alberta Health Research Data Repository (HRDR) Secure Virtual Research Environment
Bibliographic record
Abstract
Located within the Faculty of Nursing at the University of Alberta, Canada, the Health Research Data Repository (HRDR) is a secure virtual research environment (VRE) developed to support the security, confidentiality, access, and management of health related research data. The HRDR's operational phase commenced in January 2013 and at the time of the writing of this abstract thus far has provided support to over forty-five multi-disciplinary and collaborative health related research projects, both quantitative and qualitative in nature, and with an excess of 125 users across local, national, and international institutions accessing these. Project level services provided by the HRDR includes such things as support for grant writing and ethics submissions; data management planning, guidance and training; comprehensive assessments for resource needs including security, project space set-up, access, and analytic software requirements; detailed user orientations; completion of privacy impact assessments; data acquisitions; and secure file transferring (ingests/extracts). Examples of health related research projects that have benefited from these services will be presented. Additionally, a brief overview of the development and current status of the HRDR, including its policies and procedures, technical infrastructure, and cost recovery model will be discussed.
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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".