Status of National Open Spatial Data Infrastructures: a Comparison Across Continents
Bibliographic record
Abstract
The increasing need for geospatial information demands for well-organised management among all levels of society. A Spatial Data Infrastructure (SDI) is a multidisciplinary and dynamic instrument that facilitates access and sharing of geospatial information. The current trend towards open data initiatives is influencing the development of these infrastructures. In order to examine this effect, this article addresses the following question: what is the current state of SDI openness of four best practice open data countries Canada, The Netherlands, Australia and Brazil, and how do they compare? The question is answered through a qualitative literature study and the application of a newly developed Open SDI Assessment Framework to the countries. The Netherlands and Canada show a high performance on all assessment dimensions; data discovery, data access and data properties. Australia and Brazil show a poor open SDI performance, as they could not meet the requirements set for the assessed datasets. General conclusions of the assessment are that data is currently fragmented and scattered among the web in all four countries, which strongly negatively influences the user experience. It is crucial that a strict legal framework is embedded in a country, which ensures that current SDI objectives and propositions regarding an user-centred approach and open data availability are achieved.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".