FAIR principles are a set of best practices aimed at making data findable, accessible, interoperable, and reusable to both humans and machines. The growth of spatial data infrastructures and discovery catalogs (aka ‘geoportals’) have highlighted the importance of geospatial data management, metadata, and systems architecture. These data are invaluable to public, private, and academic sectors for use in decision-making, policy development, research, as well as subsequent data production. However, the costs and overall effort associated with the curation of data throughout their lifecycle can be substantial. The Stanford Spatial Data Infrastructure implements FAIR principles to its collections of geospatial data in order to better meet the needs of its researchers and worldwide user community. These principles are applied to data, metadata, and infrastructure, and serve as a guide for collecting, organizing, and managing data that are produced through research endeavors, published by public entities for open consumption, or created by vendors for commercial purposes.

In this presentation, the design and implementation of a geospatial data curation strategy utilizing FAIR principles will be described. Additionally. we will discuss our efforts around automation in data wrangling and metadata, as well as access, licensing, and digital preservation.