XDGGS: Xarray Extension for Discrete Global Grid Systems (DGGS)
Bibliographic record
Abstract
Traditional geospatial representations of the globe on a 2-dimensional plane often introduce distortions in area, distance, and angles. Discrete Global Grid Systems (DGGS) mitigate these distortions and introduce a hierarchical structure of global grids. Defined by ISO standards, DGGSs serve as spatial reference systems facilitating data cube construction, enabling integration and aggregation of multi-resolution data sources. Various tessellation schemes such as hexagons and triangles cater to different needs - equal area, optimal neighborhoods, congruent parent-child relationships, ease of use, or vector field representation in modeling flows.The fusion of Discrete Global Grid Systems (DGGS) and Datacubes represents a promising synergy for integrated handling of planetary-scale data.The recent Pangeo community initiative at the ESA BiDS'23 conference has led to significant advancements in supporting Discrete Global Grid Systems (DGGS) within the widely used Xarray package. This collaboration resulted in the development of the Xarray extension XDGGS (https://github.com/xarray-contrib/xdggs). The aim of xdggs is to provide a unified, high-level, and user-friendly API that simplifies working with various DGGS types and their respective backend libraries, seamlessly integrating with Xarray and the Pangeo scientific computing ecosystem. Executable notebooks demonstrating the use of the xdggs package are also developed to showcase its capabilities.This development represents a significant step forward, though continuous efforts are necessary to broaden the accessibility of DGGS for scientific and operational applications, especially in handling gridded data such as global climate and ocean modeling, satellite imagery, raster data, and maps.Keywords: Discrete Global Grid Systems, Xarray Extension, Geospatial Data Integration, Earth Observation, Data Cube, Scientific Collaboration
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.060 |
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".