Portaging Along: Developing a Collaborative National Research Data Management Network in Canada
Bibliographic record
Abstract
This presentation at the annual BCNET 2018 conference describes the work towards developing Portage, a national network of sustainable, shared services for research data management (RDM) in Canada. A description of the RDM context in Canada will be provided, including an overview of major Portage initiatives. We will also cover the heightened expectations around the Government of Canada’s Open Science plans, Tri-Council work towards the Research Data Management Policy. After providing this introductory background, we will specifically focus on the Federated Research Data Repository (FRDR) — a scalable, federated platform for digital research data management and discovery of Canadian research data, collaboratively developed by Compute Canada and Portage. We will discuss the discovery and harvesting mechanisms for FRDR, metadata standards used, storage options, and digital preservation pipelines to Archivematica software. Moreover, we will review the strengths and limitations of the current platform and cover expected future developments.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".