We present AI-VERDE , a unified LLM-as-a-platform service designed to facilitate seamless integration of commercial, cloud-hosted, and on-premise open LLMs in academic settings. AI-VERDE streamlines access management for instructional and research groups by providing features such as robust access control, privacy-preserving mechanisms, native Retrieval-Augmented Generation (RAG) support, budget management for third-party LLM services, and both a conversational web interface and API access. In a pilot deployment at a large public university, AI-VERDE demonstrated significant engagement across diverse educational and research groups, enabling activities that would typically require substantial budgets for commercial LLM services with limited user and team management capabilities. To the best of our knowledge, AI-Verde is the first platform to address both academic and research needs for LLMs within an higher education institutional framework.

We present AI-VERDE, a unified LLM-as-a-platform service designed to facilitate seamless integration of commercial, cloud-hosted, and on-premise open LLMs in academic settings. AI-VERDE streamlines access management for instructional and research groups by providing features such as robust access control, privacy-preserving mechanisms, native Retrieval-Augmented Generation (RAG) support, budget management for third-party LLM services, and both a conversational web interface and API access. In a pilot deployment at a large public university, AI-VERDE demonstrated significant engagement across diverse educational and research groups, enabling activities that would typically require substantial budgets for commercial LLM services with limited user and team management capabilities. To the best of our knowledge, AI-Verde is the first platform to address both academic and research needs for LLMs within an higher education institutional framework.

CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 124,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in three countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.

Background Molecular Dynamics (MD) simulation of biomolecules provides important insights into conformational changes and dynamic behavior, revealing critical information about folding and interactions with other molecules. This enables advances in drug discovery and the design of therapeutic interventions. The collection of simulations stored in computers across the world holds immense potential to serve as training data for future Machine Learning models that will transform the prediction of structure, dynamics, drug interactions, and more. A need Ideally, there should exist an open access repository that enables scientists to submit and store their MD simulations of proteins and protein-drug interactions, and to find, retrieve, analyze, and visualize simulations produced by others. However, despite the ubiquity of MD simulation in structural biology, no such repository exists; as a result, simulations are instead stored in scattered locations without uniform metadata or access protocols. A solution Here, we introduce MDRepo, a robust infrastructure that supports a relatively simple process for standardized community contribution of simulations, activates common downstream analyses on stored data, and enables search, retrieval, and visualization of contributed data. MDRepo is built on top of the open-source CyVerse research cyberinfrastructure, and is capable of storing petabytes of simulations, while providing high bandwidth upload and download capabilities and laying a foundation for cloud-based access to its stored data. ### Competing Interest Statement The authors have declared no competing interest.

Molecular Dynamics (MD) simulation of biomolecules provides important insights into conformational changes and dynamic behavior, revealing critical information about folding and interactions with other molecules. The collection of simulations stored in computers across the world holds immense potential to serve as training data for future Machine Learning models that will transform the prediction of structure, dynamics, drug interactions, and more. Ideally, there should exist an open access repository that enables scientists to submit and store their MD simulations of proteins and protein-drug interactions, and to find, retrieve, analyze, and visualize simulations produced by others. However, despite the ubiquity of MD simulation in structural biology, no such repository exists; as a result, simulations are instead stored in scattered locations without uniform metadata or access protocols. Here, we introduce MDRepo, a robust infrastructure that provides a relatively simple process for standardized community contribution of simulations, activates common downstream analyses on stored data, and enables search, retrieval, and visualization of contributed data. MDRepo is built on top of the open-source CyVerse research cyber-infrastructure, and is capable of storing petabytes of simulations, while providing high bandwidth upload and download capabilities and laying a foundation for cloud-based access to its stored data. [GRAPHICS] .

We introduce Cloud Automation and Continuous Analysis Orchestration (CACAO), an open source web platform designed to facilitate access and availability of cloud resources for education and research. By leveraging open source Infrastructure-as-Code (IaC) technologies such as Terraform and Ansible, CACAO empowers users to create customized cloud resources and complex software stacks through an intuitive web interface. CACAO integrates with CyVerse, another prominent open source cyberinfrastructure, and is a featured interface for Jetstream2, a public education and research cloud. CACAO effectively manages multi-cloud deployments in OpenStack and commercial service providers. In 2022 bioinformatics and machine learning workshops worldwide used CACAO during its alpha release. CACAO adheres to open science standards and the FAIR data principles, broadening access to cloud platforms and reducing the complexity of deploying the resources necessary for teaching advanced data informatics skills required in today’s workforce.

Modern science depends on computers, but not all scientists have access to the scale of computation they need. A digital divide separates scientists who accelerate their science using large cyberinfrastructure from those who do not, or who do not have access to the compute resources or learning opportunities to develop the skills needed. The exclusionary nature of the digital divide threatens equity and the future of innovation by leaving people out of the scientific process while over-amplifying the voices of a small group who have resources. However, there are potential solutions: recent advancements in public research cyberinfrastructure and resources developed during the open science revolution are providing tools that can help bridge this divide. These tools can enable access to fast and powerful computation with modest internet connections and personal computers. Here we contribute another resource for narrowing the digital divide: scalable virtual machines running on public cloud infrastructure. We describe the tools, infrastructure, and methods that enabled successful deployment of a reproducible and scalable cyberinfrastructure architecture for a collaborative data synthesis working group in February 2023. This platform enabled 45 scientists with varying data and compute skills to leverage 40,000 hours of compute time over a 4-day workshop. Our approach provides an open framework that can be replicated for educational and collaborative data synthesis experiences in any data- and compute-intensive discipline.

Jetstream2 will be a category I production cloud resource that is part of the National Science Foundation’s Innovative HPC Program. The project’s aim is to accelerate science and engineering by providing “on-demand” programmable infrastructure built around a core system at Indiana University and four regional sites. Jetstream2 is an evolution of the Jetstream platform, which functions primarily as an Infrastructure-as-a-Service cloud. The lessons learned in cloud architecture, distributed storage, and container orchestration have inspired changes in both hardware and software for Jetstream2. These lessons have wide implications as institutions converge HPC and cloud technology while building on prior work when deploying their own cloud environments. Jetstream2’s next-generation hardware, robust open-source software, and enhanced virtualization will provide a significant platform to further cloud adoption within the US research and education communities.

Jetstream is a first-of-a-kind system for the NSF - a distributed production cloud resource. The NSF awarded funds to create Jetstream in November 2014. Here we review the purpose for creating Jetstream, present the acceptance test results that define Jetstream's key characteristics, describe our experiences in standing up an OpenStack-based cloud environment, and share some of the early scientific results that have been obtained by researchers and students using this system. Jetstream offers unique capability within the XSEDE-supported US national cyberinfrastructure, delivering interactive virtual machines (VMs) via the Atmosphere interface developed by the University of Arizona. As a multi-region deployment that operates as a single integrated system, Jetstream is proving effective in supporting modes and disciplines of research traditionally underrepresented on larger XSEDE-supported clusters and supercomputers. Already, researchers in biology, network science, economics, earth science, and computer science have used Jetstream to perform research -- much of it research in the "long tail of science."

Jetstream will be the first production cloud resource supporting general science and engineering research within the XD ecosystem. In this report we describe the motivation for proposing Jetstream, the configuration of the Jetstream system as funded by the NSF, the team that is implementing Jetstream, and the communities we expect to use this new system. Our hope and plan is that Jetstream, which will become available for production use in 2016, will aid thousands of researchers who need modest amounts of computing power interactively. The implementation of Jetstream should increase the size and disciplinary diversity of the US research community that makes use of the resources of the XD ecosystem.

Cloud Computing refers to distributed computing platforms that use virtualization software to provide easy access to physical computing infrastructure and data storage, typically administered through a Web interface. Cloud‐based computing provides access to powerful servers, with specific software and virtual hardware configurations, while eliminating the initial capital cost of expensive computers and reducing the ongoing operating costs of system administration, maintenance contracts, power consumption, and cooling. This eliminates a significant barrier to entry into bioinformatics and high‐performance computing for many researchers. This is especially true of free or modestly priced cloud computing services. The iPlant Collaborative offers a free cloud computing service, Atmosphere, which allows users to easily create and use instances on virtual servers preconfigured for their analytical needs. Atmosphere is a self‐service, on‐demand platform for scientific computing. This unit demonstrates how to set up, access and use cloud computing in Atmosphere. Curr. Protoc. Bioinform . 43:9.15.1‐9.15.20. © 2013 by John Wiley & Sons, Inc.

This paper introduces the iPlant Foundation API Data Services, a cloud-based, hosted solution for distributed data and metadata management in the iPlant Data Store. The iPlant Data Store is a virtual storage solution for over 7000 users providing seamless access to over 6PB of distributed storage within the iPlant cyberinfrastructure using command line utilities, FUSE mounts, desktop GUI tools, and web interfaces. The Foundation API Data Services expand the standard CRUD operations by providing a collection of services that allow users to move data into and out of the iPlant Data Store using multiple transfer protocols, transform data between formats, perform advanced metadata management using both structured and unstructured data, and define their own private storage grid by registering their own resources. Concepts of unified authentication, sharing, provenance, and monitoring are baked into the services so users can focus on innovating their domain science rather than reinventing computer science. This paper briefly describe the iPlant Cyberinfrastructure, details the architecture of the iPlant Foundation API Data Services, reviews the first year of production usage, and concludes with future plans. Keywords—cloud, biology, cyberinfrastructure, iplant, data

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

The cloud platform complements traditional compute and storage infrastructures by introducing capabilities for efficiently provisioning resources in a self-service, on-demand manner. The new provisioning model promises to accelerate scientific discovery by improving access to customizable and task-specific computing resources. This paradigm is well-suited, especially for those applications tailored to leverage cloud-style of infrastructure capabilities. Adoption of the cloud model has been challenging for many domain scientists and scientific software developers due to the technical expertise required to effectively utilize this infrastructure. Some of the key limitations of cloud infrastructure are: limited integration with institutional authentication and authorization frameworks, lack of frameworks to enable domain-specific configurations for instances, and integration with scientific data repositories alongside existing computational clusters and grid deployments. Specifically designed to address some of these operational barriers towards adoptions by the plant sciences community, the iPlant Collaborative cloud platform, aptly named Atmosphere, is an open-source, robust, configurable gateway that extends established cloud infrastructure to meet the diverse computing needs for the plant science. Atmosphere manages the Virtual Machine (VM) lifecycle while maximizing the utilization of cloud resources for scientific workflows. Thus, Atmosphere allows researchers developing novel analytical tools to deploy them with ease while abstracting the underlying computing infrastructure, at the same time making it relatively easy for the users to access these tools via web browser. Atmosphere also provides a rich extensible Application Programming Interface (APIs) for integration and automation with other services. Since its launch, Atmosphere has seen a wide adoption by the plant sciences community for a broad array of applications that range from image processing to next generation sequence (NGS) analysis and can serve as a template for providing similar capabilities to other domains.