Coding Earth in practice: A fully reproducible Python workflow for 1-km geodiversity hotspot mapping in Yellowstone National Park

Le résumé fourni par la source

Description This archive provides the v7.1.7 replication package for the manuscript “Coding Earth in practice: A fully reproducible Python workflow for 1-km geodiversity hotspot mapping in Yellowstone National Park.” The package contains the open-source Python workflow, processed data artifacts, provenance manifests, integrity checksums, run logs, diagnostic CSVs and figure exports used to reproduce the Objective 1 geodiversity analysis reported in the manuscript. The workflow builds a 1-km grid for Yellowstone National Park, derives three abiotic components—thermal footprint, lithological richness and terrain variability—and combines them into a min-max normalized composite Geodiversity Index. Hotspots are defined as cells at or above the 90th percentile of the composite distribution. The archive also includes reproducibility and sensitivity outputs for threshold sensitivity at p85, p90 and p95; grid-size / MAUP sensitivity at 500 m, 1 km and 2 km; normalization sensitivity; component correlations; dominant-component structure; sliver diagnostics; and spatial autocorrelation using Global Moran’s I and Local Moran’s I. Current integrity records are provided in SHA256SUMS_CURRENT.txt and FILE_INVENTORY_SHA256_CURRENT.csv. Users should begin with the README/run-guide documentation in the archive, verify checksums from the unzipped bundle root, create the documented Python environment, and then run the core workflow and diagnostic workflow as described in the supplementary workflow guide. Keywords geodiversity; Yellowstone National Park; Coding Earth; reproducible workflow; Python; open-source GIS; hotspot mapping; geospatial reproducibility; sensitivity analysis; Modifiable Areal Unit Problem; spatial autocorrelation; research compendium License Creative Commons Attribution 4.0 International (CC BY 4.0) for the archived research compendium and data artifacts. Code included in the archive is additionally released under the MIT License, as documented in the package license files. Creators Aditya Narayan Rai Related work / manuscript This archive supports the manuscript:“Coding Earth in practice: A fully reproducible Python workflow for 1-km geodiversity hotspot mapping in Yellowstone National Park”Manuscript ID: PPG-26-052 Notes This release corresponds to the revised manuscript submission and should be cited as the replication package for the reported Yellowstone Objective 1 geodiversity workflow. The archive includes current checksum records, provenance files, run logs, diagnostic outputs and figure exports required to verify the reported results.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.