EPSCoR Research Fellows: NSF: Incorporating Knowledge Graphs and Reasoning Capabilities for Enhanced Visual Data Exploration in Mineral Evolution

The goal of this fellowship project is to enhance the lead researcher's career trajectory in data science and geoinformatics through a six-month visit to the Carnegie Institution for Science in Washington, DC (CIW), a leading laboratory in the field. The fellowship will establish a reusable methodology for efficiently deploying data science in geoscience studies to accelerate scientific discoveries.

Focusing on mineralogy, the project will implement an empirical approach to integrate existing software and data elements for data exploration. The lead researcher, who has developed software packages for knowledge graphs and data visualization, will collaborate with scientists from the Deep-time Data Driven Discovery (4D) initiative at CIW. This established long-term collaboration with 4D and CIW will enhance the researcher’s research capabilities at the University of Idaho (UI), including software development, data curation, and leadership in cross-disciplinary research.

The resulting data science methodology will have a sustained impact on the researcher’s future career. As researchers across many disciplines seek methods to efficiently deploy data science in their work, this fellowship will open numerous opportunities for the researcher to collaborate with researchers both within and outside UI.

This fellowship will provide an innovative approach to improving the interconnection and usability of existing software building blocks in open data and open science, facilitating data-driven scientific discoveries within the 4D initiative, a community of over 220 earth, space, life, and data scientists from several countries. The project will significantly enhance the researcher's research capacity, progressing from the development of data and software elements to constructing comprehensive data science frameworks.

These efforts will ultimately create workflows that bridge the gap between data scientists and geoscientists. Through the established long-term connection with the 4D network, the researcher will explore cutting-edge topics in data science applications, contribute to the international research community, and cultivate a pioneering professional path that impacts various cross-disciplinary studies.

More significantly, the project will provide a foundation for the researcher to contribute to the theoretical underpinnings of data science and develop methods that improve the efficiency of data science application and education. This project will enable the researcher to incorporate new examples and methods into the data science curriculum at UI. Additionally, the software tools and workflows developed will be broadly applicable to researchers and students in geosciences, agricultural science, physics, and chemistry at UI and beyond.

The planned conference sessions and presentations aim to promote a shift towards more open practices in data and code across geoscience and other disciplines, fostering a growing acceptance of open data and open code as standard practices in data-intensive research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.