Our Research Coordination Network

The FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network (FARR RCN) aims to build better practices (via a roadmap, community practices, and advice on tooling) for both the members of and the larger CS, GEO, and other communities they represent. This will lead to products (e.g., data, models) that are more FAIR, which in turn will lead to greater reproducibility where these products are used, and increased reuse of the products.

This RCN concentrates on three themes:

FAIR in ML
AI readiness
AI reproducibility

FARR will partner with the new NSF CSSI Democratized Cyberinfrastructure for Open Discovery to Enable Research (DeCODER) project and the Council of Data Facilities to expand and extend the successful EarthCube GeoCODES framework and community to unify data and tool description and reuse across geoscience domains.

Existing networks will be used to build the RCN, creating a network of networks. Experts and affinity groups related to ML will be engaged to understand emerging best practices, resources to leverage, and how to stimulate experimentation that quantifies the relationship between the FAIRness of data and how easily and efficiently ML algorithms can be applied, as well as need for awareness and new research in ML reproducibility. Different stakeholder types will be engaged, for example, data repositories will be supported to make their data more FAIR and AI-ready.

The FARR RCN is hosted by the San Diego Supercomputer Center (SDSC), University of California San Diego. (See award announcement.) SDSC is involved in other related and high profile research data efforts, including the US National Science Foundation-funded EarthCube Office, West Big Data Innovation Hub and the US National Data Service (NDS).

The Team

The FARR RCN brings together researchers from the San Diego Supercomputer Center (SDSC), Scripps Institution of Oceanography (SIO), North Carolina Institute for Climate Studies (NCICS), and National Center for Supercomputing Applications (NCSA).

Christine R. Kirkpatrick

Principal Investigator

Christine Kirkpatrick leads the San Diego Supercomputer Center’s (SDSC) Research Data Services division, which manages large-scale infrastructure, networking, and services for research projects of regional and national scope. In addition to being the PI of the EarthCube Office (ECO) and West Big Data Innovation Hub, Kirkpatrick founded the GO FAIR US Office. Christine's research interest is in Compute Science and data-centric AI, working at the intersection of ML and FAIR, with a focus on making AI more efficient to save on power consumption and 'time to science'.

Karen Stocks

Co-Investigator

Karen Stocks is the Director of the Geological Data Center at Scripps Institution of Oceanography. She is an oceanographer and data scientist specializing in the management and dissemination of diverse oceanographic data. Stocks’ expertise includes information systems for vessel-based sensors, scientific ocean drilling, biodiversity and biogeography, metagenomics, and ocean observing systems. Within FARR, she serves as liaison to geosciences data facilities, ensuring that FARR understands the needs of this community around AI/ML readiness, and that FARR outcomes and guidance are widely communicated.

Yuhan (Douglas) Rao

Co-Investigator

Douglas Rao is a Research Scientist at the North Carolina Institute for Climate Studies, North Carolina State University. He is interested in improving data readiness for AI and working with data facilities to prepare for the evolving data needs of AI R&D by integrating community-driven AI-ready data management guidelines. Additionally, Rao is interested in contributing to the community research roadmap for ensuring AI reproducibility and replicability, particularly for large-scale and computationally intensive models.

Daniel S. Katz

Co-Investigator

Daniel S. Katz is Chief Scientist at the National Center for Supercomputing Applications (NCSA) and Research Associate Professor in Computer Science, Electrical and Computer Engineering, and the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign. Dan’s interest is in the development and use of advanced cyberinfrastructure to solve challenging problems at multiple scales, and in policy issues, including citation, credit, and FAIR mechanisms and practices associated with software, data, and machine learning.

Kevin Coakley

Senior Personnel

Kevin Coakley is a Senior Systems and Cloud Integration Engineer at the San Diego Supercomputer Center, UC San Diego where he supports the cloud infrastructure for multiple projects. Kevin’s research interest is in Computer Science where he focuses on reproducibility in Machine Learning.