[QGreenland](https://qgreenland.org/) is a free and open-source Greenland-focused QGIS environment for data analysis and visualization. Built using Python and open source geospatial tools like GDAL, QGreenland's software offers automated, reproducible builds to ensure consistent outputs with metadata and provenance for all included datasets.
[QGreenland](https://qgreenland.org/) is a free and open-source Greenland-focused QGIS environment for data analysis and visualization. Originally inspired by [Quantarctica](https://www.npolar.no/en/quantarctica/), a similar QGIS data environment focused on Antarctica, QGreenland formalizes the process of data package creation with a framework built using Python and open source geospatial tools like GDAL. QGreenland's software (available on [GitHub](https://github.com/nsidc/qgreenland/)) automates the process of fetching data from a variety of public sources, transforming those data into optimized formats and projection, and producing a downloadable package with documentation and metadata. Users then access data via a single project file. Because QGreenland's tooling is open source and provenance is maintained for all data operations, QGreenland provides reproducible and transparent outputs suitable for education, field use, and scientific research. Moreover, because the QGreenland data package includes a pre-configured QGIS project file with data organized by discipline (e.g., "Glaciology", "Geophysics", etc), QGreenland is accessible, and is further supported with extensive how-to, tutorial, and curriculum resources. This talk will discuss the idea behind QGreenland, the development process, challenges encountered along the way, lessons-learned, and what the future holds for the project.
Speakers: Trey Stafford