Towards a Collaborative National Research Data Management Network
Bibliographic record
Abstract
This paper describes the plans and strategies to develop Portage, a national network of sustainable, shared services for research data management (RDM) in Canada. A description of the RDM context in Canada is provided. This environment has heightened expectations around the Government of Canada’s Open Science plans and includes deliverables aimed at improving access to publications and data resulting from federally funded scientific activities. At the same time, a recent environmental scan published by Canada’s three federal research granting councils reveals significant gaps in services, infrastructure, and funding mechanisms to support RDM. In addition, Canada’s RDM environment consists of stakeholders from a variety of communities with minimal ongoing coordination or cooperation. The Portage network was conceived as a collaborative network model based on libraries’ strong connections with researchers across the disciplines, an ethos of curation and preservation, and experience with systems for managing data in all its forms. A pilot project provided Portage with a vision and set of principles, and identified several objectives as the small wins that would build the trust and shared understanding required for a successful network. Current services and activities of Portage, including a data management planning tool and an infrastructure project, are described in this paper. Portage now faces the challenge of moving from project to operational network, and the challenge of establishing a sustainable governance model. CARL appointed a Steering Committee that will be proposing a full governance model at the conclusion of this transition period. Using a framework of factors identified in the literature, several relevant collaborative and network governance models are being explored.This paper outlines experience to date with Portage and matters under consideration for long-term sustainability, with a goal of engaging international colleagues in discussion and furthering the concepts for the benefit of RDM networks everywhere.
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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 | 0.150 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.009 | 0.028 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".