GSA Connects 2023 Meeting in Pittsburgh, Pennsylvania

Paper No. 16-2
Presentation Time: 8:20 AM

THE MACROSTRAT PLATFORM FOR THE EARTH’S GEOLOGIC FRAMEWORK


QUINN, Daven, Department of Geoscience, University of Wisconsin – Madison, 1215 W Dayton, Madison, WI 53703

Macrostrat is a geological data platform that integrates stratigraphic columns and geologic maps into a digital description of the crustal rock record. It comprises thousands of stratigraphic columns and 300+ geologic maps, correlated into an integrated, web-accessible dataset. Combined, map and column information create a spatial and temporal “scaffold” that can organize and contextualize information about the rock record. Macrostrat columns can represent stratigraphy at any scale, from regional summaries to single core logs, and can translate between physical and time-stratigraphic representations of the rock record. This capability is particularly powerful when combined with proxy data (e.g., geochemistry, fossils, and paleoenvironmental proxies). Macrostrat exposes a modern, openly accessible software interface (API) that facilitates a range of community uses. By allowing the tabulation of basic rock-record information through Earth’s geologic history, the platform has driven innovative research into the deep-time evolution of the Earth. It also supports the provision of maps and local geologic information to location-aware software, including our own Rockd mobile app, a community-curated field guide with over 100,000 registered users.

An innovative structure and open architecture has allowed Macrostrat to achieve wide community usage. However, the system lacks the breadth needed to be a truly comprehensive geologic data platform. The “Macrostrat v2” effort, now getting underway, centers around building new tools to allow community contribution of maps and stratigraphic data to the system. To build community involvement, we are integrating with other established informatics efforts, such as StraboSpot and the Sedimentary Geochemistry and Paleoenvironments project. And, by engaging the machine-reading capabilities of xDD, we seek to augment these curated data with information automatically extracted from the geologic literature. These enhancements will build a far more flexible and community-integrated data system which can enrich digital geoscience research for years to come.