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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 OfficeWest Big Data Innovation Hub and the US National Data Service (NDS)

The Team

Advisory Board

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