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Record W2543266858 · doi:10.2218/ijdc.v11i1.411

Towards a Collaborative National Research Data Management Network

2016· article· en· W2543266858 on OpenAlexaffabout
Chuck Humphrey, Kathleen Shearer, Martha Whitehead

Bibliographic record

VenueInternational Journal of Digital Curation · 2016
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsQueen's UniversityCanadian Association of Research LibrariesUniversity of Alberta
Fundersnot available
KeywordsRDMContext (archaeology)DeliverableKnowledge managementComputer scienceCorporate governanceProcess managementData governanceGovernment (linguistics)Open dataData sharingBusinessWorld Wide WebData qualityEngineeringService (business)Systems engineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.150
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.991
Threshold uncertainty score0.791

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1500.097
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.009
Science and technology studies0.0130.007
Scholarly communication0.0220.032
Open science0.0090.028
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0080.004

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.

Opus teacher head0.266
GPT teacher head0.479
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainReproducibility
GenreMethods

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".

Quick stats

Citations3
Published2016
Admission routes2
Has abstractyes

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