Data · dataset · 2025
Replication data for: Simulated fields exhibiting varying degrees of spatial structure for research in digital soil mapping and precision agriculture
Listed in REDU - Unicamp Institutional Research Data Repository
This database contains 10,201 spatial observations of five covariates, each simulated through an Unconditional Gaussian Simulation process.
Description
The simulation occurred in a spatial scenario with a grid of 105x105m with one value for every meter. The covariates exhibit distinct spatial structures, defined by different values of the nugget effect (c₀), sill (c₁), and range (r).
The covariate values depend on the simulation process, with only the semivariogram parameters varying. Each complete dataset is referred to as a scenario, numbered from 1 to 24. The semivariogram parameters (sill and range) for each scenario are: Scenario 1: V1 (c₁ = 0.1, r = 50), V2 (c₁ = 0.2, r = 10), V3 (c₁ = 0.5, r = 30), V4 (c₁ = 0.8, r = 80), V5 (c₁ = 0.001, r = 1).
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Scenario 2: V1 (c₁ = 0.8, r = 80), V2 (c₁ = 0.2, r = 50), V3 (c₁ = 0.3, r = 30), V4 (c₁ = 0.5, r = 10), V5 (c₁ = 0.001, r = 1). Scenario 3: V1 (c₁ = 0.5, r = 10), V2 (c₁ = 0.8, r = 80), V3 (c₁ = 0.2, r = 50), V4 (c₁ = 0.1, r = 30), V5 (c₁ = 0.001, r = 1). Scenario 4: V1 (c₁ = 0.5, r = 30), V2 (c₁ = 0.2, r = 10), V3 (c₁ = 0.8, r = 50), V4 (c₁ = 0.1, r = 80), V5 (c₁ = 0.001, r = 1).
Scenario 5: V1 (c₁ = 0.8, r = 50), V2 (c₁ = 0.5, r = 80), V3 (c₁ = 0.1, r = 30), V4 (c₁ = 0.2, r = 10), V5 (c₁ = 0.001, r = 1). Scenario 6: V1 (c₁ = 0.5, r = 10), V2 (c₁ = 0.8, r = 50), V3 (c₁ = 0.1, r = 80), V4 (c₁ = 0.2, r = 30), V5 (c₁ = 0.001, r = 1). Scenario 7: V1 (c₁ = 0.1, r = 30), V2 (c₁ = 0.5, r = 80), V3 (c₁ = 0.8, r = 50), V4 (c₁ = 0.2, r = 10), V5 (c₁ = 0.001, r = 1).
Scenario 8: V1 (c₁ = 0.2, r = 80), V2 (c₁ = 0.1, r = 30), V3 (c₁ = 0.5, r = 50), V4 (c₁ = 0.8, r = 10), V5 (c₁ = 0.001, r = 1). Scenario 9: V1 (c₁ = 0.8, r = 30), V2 (c₁ = 0.1, r = 50), V3 (c₁ = 0.2, r = 80), V4 (c₁ = 0.5, r = 10), V5 (c₁ = 0.001, r = 1). Scenario 10: V1 (c₁ = 0.2, r = 30), V2 (c₁ = 0.1, r = 50), V3 (c₁ = 0.8, r = 10), V4 (c₁ = 0.5, r = 80), V5 (c₁ = 0.001, r = 1).
Scenario 11: V1 (c₁ = 0.8, r = 80), V2 (c₁ = 0.1, r = 30), V3 (c₁ = 0.5, r = 10), V4 (c₁ = 0.2, r = 50), V5 (c₁ = 0.001, r = 1). Scenario 12: V1 (c₁ = 0.2, r = 80), V2 (c₁ = 0.5, r = 10), V3 (c₁ = 0.8, r = 50), V4 (c₁ = 0.1, r = 30), V5 (c₁ = 0.001, r = 1). Scenario 13: V1 (c₁ = 0.1, r = 10), V2 (c₁ = 0.5, r = 30), V3 (c₁ = 0.2, r = 80), V4 (c₁ = 0.8, r = 50), V5 (c₁ = 0.001, r = 1).
Scenario 14: V1 (c₁ = 0.8, r = 50), V2 (c₁ = 0.2, r = 30), V3 (c₁ = 0.5, r = 10), V4 (c₁ = 0.1, r = 80), V5 (c₁ = 0.001, r = 1). Scenario 15: V1 (c₁ = 0.2, r = 10), V2 (c₁ = 0.5, r = 50), V3 (c₁ = 0.1, r = 30), V4 (c₁ = 0.8, r = 80), V5 (c₁ = 0.001, r = 1). Scenario 16: V1 (c₁ = 0.5, r = 80), V2 (c₁ = 0.2, r = 50), V3 (c₁ = 0.1, r = 10), V4 (c₁ = 0.8, r = 30), V5 (c₁ = 0.001, r = 1).
Scenario 17: V1 (c₁ = 0.1, r = 30), V2 (c₁ = 0.8, r = 80), V3 (c₁ = 0.5, r = 10), V4 (c₁ = 0.2, r = 50), V5 (c₁ = 0.001, r = 1). Scenario 18: V1 (c₁ = 0.2, r = 50), V2 (c₁ = 0.8, r = 10), V3 (c₁ = 0.1, r = 80), V4 (c₁ = 0.5, r = 30), V5 (c₁ = 0.001, r = 1). Scenario 19: V1 (c₁ = 0.1, r = 50), V2 (c₁ = 0.2, r = 80), V3 (c₁ = 0.8, r = 10), V4 (c₁ = 0.5, r = 30), V5 (c₁ = 0.001, r = 1).
Scenario 20: V1 (c₁ = 0.2, r = 30), V2 (c₁ = 0.8, r = 10), V3 (c₁ = 0.5, r = 80), V4 (c₁ = 0.1, r = 50), V5 (c₁ = 0.001, r = 1). Scenario 21: V1 (c₁ = 0.5, r = 10), V2 (c₁ = 0.1, r = 80), V3 (c₁ = 0.2, r = 30), V4 (c₁ = 0.8, r = 50), V5 (c₁ = 0.001, r = 1). Scenario 22: V1 (c₁ = 0.5, r = 50), V2 (c₁ = 0.1, r = 30), V3 (c₁ = 0.8, r = 80), V4 (c₁ = 0.2, r = 10), V5 (c₁ = 0.001, r = 1).
Scenario 23: V1 (c₁ = 0.1, r = 10), V2 (c₁ = 0.8, r = 30), V3 (c₁ = 0.2, r = 50), V4 (c₁ = 0.5, r = 80), V5 (c₁ = 0.001, r = 1). Scenario 24: V1 (c₁ = 0.8, r = 80), V2 (c₁ = 0.5, r = 10), V3 (c₁ = 0.2, r = 30), V4 (c₁ = 0.1, r = 50), V5 (c₁ = 0.001, r = 1).
Links
Where it is published
- Dataverse dataset page redu.unicamp.br/dataset.xhtml?persistentId=doi%3A10.25824%2Fredu%2FFI7XIM ↗
landing page · from redu unicamp br
- DOI doi.org/10.25824/redu/fi7xim ↗
DOI / persistent id · from redu unicamp br
Catalogue records · 1
- Dataverse API redu.unicamp.br/api/datasets/:persistentId/?persistentId=doi%3A10.25824%2Fredu… ↗
metadata API · from redu unicamp br
Topics
- Stated by source
- Agricultural Sciences
- From keywords
- Life Sciences
- Inferred from text
- Agriculture, land and farm management 74% · Simulation 75%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| REDU - Unicamp Institutional Research Data Repository | doi:10.25824/redu/FI7XIM | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3002 | enrichment · redu unicamp br | taxonomy-embedding@1.1.0 | title+keywords+description (74%) |
| concepts[field].dataverse_subject:agricultural-sciences | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /subjects |
| concepts[method].local:method:simulation | enrichment · redu unicamp br | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | |
| description | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /description |
| publication_date | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | |
| title | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /name |
| updated_date | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | |
| version_label | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 |