An analysis of the FAIRness of water related datasets on the Government of Canada's “Open Maps” platform
Bibliographic record
Abstract
The Canada Centre for Mapping and Earth Observation (CCMEO) branch of Natural Resources Canada is supporting geographic analysis and evidence-based decision making by delivering core geospatial data that is accurate, dynamic, authoritative and accessible. One role of CCMEO is in the dissemination of spatial datasets to Canadian citizens and the broader public. Over time, the volume of data, contributors, file formats, types and frequency of data updates has increased. CCMEO is working diligently to ensure that the datasets it provides are Findable, Accessible, Interoperable and Reusable (FAIR) to support the Government of Canada’s commitment to ‘openness and expansion of data resources”. In this study, we undertake an environmental scan of the Government of Canada collection of geospatial datasets related to the theme of “Water” to evaluate how FAIR the data is and assess how well the available geospatial data is to meeting the federal Governments’ stated commitment. The data were tested with the online FAIR tool Self-Assessment Tool (SATIFYD) from Data Archiving and Networked Services (DANS). The computed average over the collection is 70% (or 3.5/5), respectively, with datasets from Statistics Canada achieving the highest average rating. Key areas to improve the FAIR score include adding a globally unique and persistent identifier, domain standardized vocabulary (where possible), improving the metadata by adding provenance information and data/workflow process descriptions, linked data, and citation information. Other considerations to improve the ability to identify relevant search results include a new hierarchical categorization scheme and an improved method to filter results per a feature based location search.
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 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 | 0.061 | 0.279 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".