Globus Data Publication as a Service: Lowering Barriers to Reproducible Science

Abstract-Broad access to the data on which scientific results are based is essential for verification, reproducibility, and extension. Scholarly publication has long been the means to this end. But as data volumes grow, new methods beyond traditional publications are needed for communicating, discovering, and accessing scientific data. We describe data publication capabilities within the Globus research data management service, which supports publication of large datasets, with customizable policies for different institutions and researchers; the ability to publish data directly from both locally owned storage and cloud storage; extensible metadata that can be customized to describe specific attributes of different research domains; flexible publication and curation workflows that can be easily tailored to meet institutional requirements; and public and restricted collections that give complete control over who may access published data. We describe the architecture and implementation of these new capabilities and review early results from pilot projects involving nine research communities that span a range of data sizes, data types, disciplines, and publication policies

FAIR principles for AI models, with a practical application for accelerated high energy diffraction microscopy

A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.Comment: 10 pages, 3 figures. Comments welcome