High Performance Computing (HPC) centers, such as the Oak Ridge Leadership Computing Facility (OLCF), provide advanced infrastructure that enables scientific research at extreme scale. These centers operate with unique hardware configurations, specialized software environments, and elevated security re- quirements that differ substantially from what most users encounter on their local systems. As a result, users often develop customized digital artifacts that are tightly coupled to the specific configuration of a given HPC center. Although necessary, this practice can lead to significant duplication of effort as multiple users independently create similar solutions to common problems. The FAIR Principles, which stand for Findable, Accessible, Interoperable, and Reusable, offer a framework to address these challenges (Section 2.1). Initially designed to improve data stewardship, the FAIR approach has since been extended to encompass software, workflows, models, and infrastructure. By encouraging the use of rich metadata and community standards, FAIR practices aim to make digital artifacts easier to share and reuse, both within and across scientific domains. Many FAIR initiatives have emerged within individual research communities, often aligned by discipline, such as bioinformatics or earth sciences. These communities have made progress in adopting FAIR practices, but their domain-specific nature can lead to silos that limit broader collaboration. To overcome this, we propose that HPC centers play a more active role in fostering FAIR ecosystems that support research across multiple disciplines. This requires designing infrastructure that enables researchers to discover, share, and reuse computational components more effectively. In this report, we build on the architecture of the European Open Science Cloud (EOSC) EOSC-Life FAIR Workflows Collaboratory to propose a model that is tailored to the needs of HPC environments. Rather than focusing on entire workflows, which are often difficult to reuse due to rapid changes in HPC infrastructure, we emphasize the importance of making individual workflow components FAIR. This component-based approach better supports the diverse and evolving needs of HPC users while maximizing the long-term value of their work.
Sean et al. (Sat,) studied this question.