David Y. Hancock

The CI-PIVOT project, a collaboration between Indiana University, Purdue, and Mississippi State, addresses the gap between complex HPC/AI resources and domain researchers by transforming science experts into embedded Cyberinfrastructure Professionals (CIPs). Through an 18-month cohort model, this project establishes a scalable framework for professionalizing the CIP role, providing insights into training that can be generalized across the broader research ecosystem.

Over the past decade, the convergence of Cloud and High-Performance Computing (HPC) has undergone significant movement. We explore the evolution, motivations, and practicalities of establishing on-premise research cloud infrastructure and the complementary nature with HPC and commercial resources; under the belief that research clouds serve a unique role within research and education as a convergence accelerator. This role is highlighted through exploring the design tradeoffs in architecting research clouds versus HPC resources, focusing on the balance between utility, availability, and hardware utilization. The discussion provides insights from experiences with the National Science Foundation-supported Jetstream and Jetstream2 systems, showcasing convergence technologies and challenges. A variety of real-world use cases are provided that show the interplay between these computing paradigms; exploring use in research and education for interactive and iterative development, as an on-ramp to large-scale resources, as a powerful tool for education and workforce development, and for domain specific science gateways.

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

Many areas of biomedical research, including precision health research, inherently involve the use of IT systems properly secured to store and analyze protected health information (PHI). Managing cybersecurity policies in ways that allow such storage and analysis of PHI on computing systems is involved, but so doing can facilitate important research. In 2005, Indiana University established the ability to analyze PHI (HIPAA alignment) as the minimal security level for its High Performance Computing (HPC) systems and research storage systems. Starting in 2010, REDCap was also offered as a central service, providing capabilities for conducting surveys that collect PHI and storing those data in ways that align with HIPAA. Usage data shows that providing computational and data resources aligned with HIPAA has increased the use of advanced research cyberinfrastructure over time. We explain some of our tactics in providing secure environments for handling protected health information and offer suggestions that other cyberinfrastructure centers may find useful in setting strategies for serving medical researchers.

This paper uses accounting concepts—particularly the concept of Return on Investment (ROI)—to reveal the quantitative value of scientific research pertaining to a major US cyberinfrastructure project (XSEDE—the eXtreme Science and Engineering Discovery Environment). XSEDE provides operational and support services for advanced information technology systems, cloud systems, and supercomputers supporting non-classified US research, with an average budget for XSEDE of US$20M+ per year over the period studied (2014–2021). To assess the financial effectiveness of these services, we calculated a proxy for ROI, and converted quantitative measures of XSEDE service delivery into financial values using costs for service from the US marketplace. We calculated two estimates of ROI: a Conservative Estimate, functioning as a lower bound and using publicly available data for a lower valuation of XSEDE services; and a Best Available Estimate, functioning as a more accurate estimate, but using some unpublished valuation data. Using the largest dataset assembled for analysis of ROI for a cyberinfrastructure project, we found a Conservative Estimate of ROI of 1.87, and a Best Available Estimate of ROI of 3.24. Through accounting methods, we show that XSEDE services offer excellent value to the US government, that the services offered uniquely by XSEDE (that is, not otherwise available for purchase) were the most valuable to the facilitation of US research activities, and that accounting-based concepts hold great value for understanding the mechanisms of scientific research generally.

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.

The economics of high performance computing are rapidly changing. Commercial cloud offerings, private research clouds, and pressure on the budgets of institutions of higher education and federally-funded research organizations are all contributing factors. As such, it has become a necessity that all expenses and investments be analyzed and considered carefully. In this paper we will analyze the return on investment (ROI) for three different kinds of cyberinfrastructure resources: the eXtreme Science and Engineering Discovery Environment (XSEDE); the NSF-funded Jetstream cloud system; and the Indiana University (IU) Big Red II supercomputer, funded exclusively by IU for use of the IU community and collaborators. We determined the ROI for these three resources by assigning financial values to services by either comparison with commercially available services, or by surveys of value of these resources to their users. In all three cases, the ROI for these very different types of cyberinfrastructure resources was well greater than 1 - meaning that investors are getting more than $1 in returned value for every $1 invested. While there are many ways to measure the value and impact of investment in cyberinfrastructure resources, we are able to quantify the short-term ROI and show that it is a net positive for campuses and the federal government respectively.

Summary: Jetstream is a first of its kind system for the NSF — a distributed production cloud resource. We review the purpose for creating Jetstream, discuss Jetstream's key characteristics, describe our experiences from the first year of maintaining an OpenStack‐based cloud environment, and share some of the early scientific impacts achieved by Jetstream users. Jetstream offers a unique capability within the XSEDE‐supported US national cyberinfrastructure, delivering interactive virtual machines (VMs) via the Atmosphere interface. As a multi‐region deployment that operates as an integrated system, Jetstream is proving effective in supporting modes and disciplines of research traditionally underrepresented on larger XSEDE‐supported clusters and supercomputers. Already, Jetstream has been used to perform research and education in biology, biochemistry, atmospheric science, earth science, and computer science.