Bibliographic record
Abstract
Good data management requires support for researchers at all stages of the data lifecycle, from policy and planning development to infrastructure that ensures data is findable, accessible, interoperable, and reusable (FAIR). While several excellent institutional, domain-specific, and general repositories currently exist both within Canada and abroad, Canada lacks nationally coordinated solutions for managing research data, and the question of where to deposit data for discovery, reuse, and preservation remains pervasive. Developed through a partnership between the Canadian Association of Research Libraries (CARL), the Portage Network, and Compute Canada, the Federated Research Data Repository (FRDR) seeks to address a longstanding gap in Canada’s research infrastructure by providing a single platform from which research data can be ingested, curated, preserved, discovered, cited, and shared. The platform’s federated search tool will provide a focal point to discover and access Canadian research data, while the range of services provided by FRDR will help researchers store and manage their data, preserve their research for future use, and comply with institutional and funding agency data management requirements. In this presentation, participants will learn about the development of the new system, current and planned functionality, the timeline for service launch, the proposed distributed service model to support institutions both locally and nationally, and a brief overview of research projects we will be supporting as the platform moves toward launch. Researchers will gain an understanding of how they can use FRDR to make their research data discoverable and accessible, as well as comply with increasing funder expectations for the management of research data.
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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".