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The Koa ecosystem is a comprehensive computational ecosystem built on open source software and developed by the University of Hawai‘i to enhance data-intensive research across its campuses. The Koa ecosystem addresses critical needs for computational resources, high-performance storage, and sustainable infrastructure to support multiple scientific disciplines. Over two years, the Koa ecosystem has significantly boosted research output, as evidenced by numerous publications acknowledging it and the research community’s continued investment in expanding the resource. 

The Cyberinfrastructure Training and Capacity Building in Climate and Environmental Sciences (CI-TRACS) program represents a pioneering initiative aimed at enhancing cyberinfrastructure proficiency within Hawaii’s academic community. This paper outlines the program’s comprehensive strategy, which integrates curriculum development, hands-on workshops, and professional growth opportunities to cultivate a robust foundation in CI practices. The initiative’s core objective is to elevate CI literacy, promote cross-disciplinary cooperation, and endorse the principles of open science. Significant contributions from the CI-TRACS program include a suite of educational materials and resources tailored for integration into higher education syllabi. Collaboration with the Hawaii Data Science Institute has been instrumental in nurturing a burgeoning network of data science professionals. The CI-TRACS program is instrumental in realizing the shared vision of equipping Hawaii’s emerging workforce with the sophisticated CI skills necessary to navigate and excel in the evolving landscape of climate and environmental sciences. 

Abstract The Hawai‘i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near–real time. To address these limitations, HCDP researchers developed the cyberinfrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The “virtual station” feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community. 

The C-MĀIKI gateway is a science gateway that leverages a computational workload management API called Tapis to support modern, interoperable, and scalable microbiome data analysis. This project is focused on migrating an existing C-MĀIKI gateway pipeline from Tapis v2 to Tapis v3 so that it can take advantage of the new robust Tapis v3 features and stay modern. This requires three major steps: 1) Containerization of each existing microbiome workflow. 2) Create a new app definition for each of the workflows. 3) Enabling the ability to submit jobs to a SLURM scheduler inside of a singularity container to support the Nextflow workflow manager. This work presents the experience and challenges in upgrading the pipeline. 

Abstract SARS-CoV-2 worldwide spread and evolution has resulted in variants containing mutations resulting in immune evasive epitopes that decrease vaccine efficacy. We acquired SARS-CoV-2 positive clinical samples and compared the worldwide emerged spike mutations from Variants of Concern/Interest, and developed an algorithm for monitoring the evolution of SARS-CoV-2 in the context of vaccines and monoclonal antibodies. The algorithm partitions logarithmic-transformed prevalence data monthly and Pearson’s correlation determines exponential emergence of amino acid substitutions (AAS) and lineages. The SARS-CoV-2 genome evaluation indicated 49 mutations, with 44 resulting in AAS. Nine of the ten most worldwide prevalent (>70%) spike protein changes have Pearson’s coefficient r  > 0.9. The tenth, D614G, has a prevalence >99% and r -value of 0.67. The resulting algorithm is based on the patterns these ten substitutions elucidated. The strong positive correlation of the emerged spike protein changes and algorithmic predictive value can be harnessed in designing vaccines with relevant immunogenic epitopes. Monitoring, next-generation vaccine design, and mAb clinical efficacy must keep up with SARS-CoV-2 evolution, as the virus is predicted to remain endemic. 

This article discusses the design and implementation of the Hawai'i Groundwater Recharge Tool, an application for providing data and analyses of the impacts of land-cover modifications and changes in precipitation on groundwater-recharge rates for the island of O'ahu. This application uses simulation data based on a set of 29 land-cover types and 2 precipitation conditions to provide users with real-time recharge calculations for interactively defined land-cover modifications. The tool provides two visualizations, representing the land cover for the island and the resultant groundwater-recharge rates, and a set of metrics indicating the changes to groundwater recharge for relevant areas to present a set of easily interpretable outcomes based on user-defined scenarios. Users have varying degrees of control over the granularity of data input and output, allowing for the quick production of a roughly defined scenario, or more precise land-cover definitions. These modifications can be exported for further analysis. Heuristics are used to provide a responsive user interface and performant integration with the database containing the full set of simulation data. This tool is designed to provide user-friendly access to the information on the impacts of land-cover and precipitation changes on groundwater-recharge rates needed to assist in making data-driven decisions. 

. Granting agencies invest millions of dollars on the generation and analysis of data, making these products extremely valuable. However, without sufficient annotation of the methods used to collect and analyze the data, the ability to reproduce and reuse those products suffers. This lack of assurance of the quality and credibility of the data at the different stages in the research process essentially wastes much of the investment of time and funding and fails to drive research forward to the level of potential possible if everything was effectively annotated and disseminated to the wider research community. In order to address this issue for the Hawai'i Established Program to Stimulate Competitive Research (EPSCoR) project, a water science gateway was developed at the University of Hawai‘i (UH), called the ‘Ike Wai Gateway. In Hawaiian, ‘Ike means knowledge and Wai means water. The gateway supports research in hydrology and water management by providing tools to address questions of water sustainability in Hawai‘i. The gateway provides a framework for data acquisition, analysis, model integration, and display of data products. The gateway is intended to complement and integrate with the capabilities of the Consortium of Universities for the Advancement of Hydrologic Science's (CUAHSI) Hydroshare by providing sound data and metadata management capabilities for multi-domain field observations, analytical lab actions, and modeling outputs. Functionality provided by the gateway is supported by a subset of the CUAHSI’s Observations Data Model (ODM) delivered as centralized web based user interfaces and APIs supporting multi-domain data management, computation, analysis, and visualization tools to support reproducible science, modeling, data discovery, and decision support for the Hawai'i EPSCoR ‘Ike Wai research team and wider Hawai‘i hydrology community. By leveraging the Tapis platform, UH has constructed a gateway that ties data and advanced computing resources together to support diverse research domains including microbiology, geochemistry, geophysics, economics, and humanities, coupled with computational and modeling workflows delivered in a user friendly web interface with workflows for effectively annotating the project data and products. Disseminating results for the ‘Ike Wai project through the ‘Ike Wai data gateway and Hydroshare makes the research products accessible and reusable. 

This paper discusses the design and implementation of the Hawai‘i Rainfall Analysis and Mapping Application (HI-RAMA) decision support tool. HI-RAMA provides researchers and community stakeholders interactive access to and visualization of hosted historical and near-real-time monthly rainfall maps and aggregated rainfall station observational data for the State of Hawai‘i. The University of Hawai‘i Information Technology Services Cyberinfrastructure team in partnership with members of the Hawai‘i Established Program to Stimulate Competitive Research (EPSCoR) ‘Ike Wai project team developed this application as part of the ‘Ike Wai Gateway to support water sustainability research for the state of Hawai‘i. This tool is designed to provide user-friendly access to information that can reveal the impacts of climate changes related to precipitation so users can make data-driven decisions.