A Maturity Model for Urban Dataset Metadata
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid increase in published datasets has intensified challenges in sourcing and integrating relevant data for analysis. Persistent obstacles include poor metadata, ineffective presentation, and difficulties in locating and integrating datasets. This paper delves into the intricacies of dataset retrieval, emphasising the pivotal role of metadata in aligning datasets with user queries. Through an exploration of existing literature, it highlights prevailing issues, such as identifying valuable metadata and developing tools to maintain and annotate them effectively. The paper proposes a dataset metadata maturity model, inspired by software engineering frameworks, to guide dataset creators from basic to advanced documentation. The model encompasses seven pivotal dimensions, spanning content to quality information, each stratified across five maturity levels to guide the optimal documentation of datasets, ensuring ease of discovery, accurate relevance assessment, and comprehensive understanding of datasets. This paper also incorporates the maturity model into a data cataloguing tool called CKAN through a custom plugin, CKANext-udc. The plugin introduces custom fields based on different maturity levels, allows for user interface customisation, and integrates with a graph database, converting catalogue data into a knowledge graph based on the Maturity Model ontology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it