Bibliographic record
Abstract
Introduction: The surge in open data platforms such as CKAN and Dataserve has expanded the urban data landscape, yet data scarcity persists due to inadequate metadata, poorly tailored data presentation, and localization challenges (Ojo et al., 2016). Decentralization of repositories further complicates data discovery and metadata inconsistencies and obstructs dataset identification, comparison, and deduplication. The Canadian Urban Data Catalogue (CUDC) addresses these issues by providing a comprehensive catalogue of both accessible and restricted Canadian urban datasets and web services. It incorporates a dataset metadata maturity model that ranks datasets by metadata completeness, where higher maturity denotes greater detail. Following Fox et al. (2024), the levels assess search-relevant attributes, extending to licensing, governance, and compliance with FAIR and indigenous data principles, ensuring a structured and mature metadata framework for catalogue entries.Methodology: The development of CUDC involves a user-centric approach, focusing on its users' practical needs and behaviours. The architecture integrates the maturity model with an advanced knowledge graph database for metadata analysis, developed as an open-source CKAN plugin that provides:1. Cataloguing: a metamodel, extension support, upload capabilities, and API access points, ensuring accessible and transparent data access policies.2. Search Functionality: a wide range of searchable metadata organized for easy data entry and retrieval.3. Dataset Usage Quality: encourages comprehensive metadata provision for determining dataset applicability and relevance.4. Search Behaviour Analysis: offers insights into dataset search models and tools, identifying key metadata across domains. References Ojo, A., Porwol, L., Waqar, M., Stasiewicz, A., Osagie, E., Hogan, M., Harney, O., and Zeleti, F. A. (2016, October). Realizing the innovation potentials from open data: Stakeholders’ perspectives on the desired affordances of open data environment. In Working Conference on Virtual Enterprises (pp. 48-59). Springer, Cham. Fox, M., Gajderowicz ,B., Lyu, D. (2024), A Maturity Model for Urban Dataset Meta-data. Manuscript under review.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.049 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.055 |
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".