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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State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near–real time.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. 

Abstract Gridded air temperature data are required in various fields such as ecological modeling, weather forecasting, and surface energy balance assessment. In this work, a piecewise multiple linear regression model is used to produce high‐resolution (250 m) daily maximum (T_max), minimum (T_min), and mean (T_mean) near‐surface air temperature maps for the State of Hawaiʻi for a 32‐year period (1990–2021). Multiple meteorological and geographical variables such as the elevation, daily rainfall, coastal distance index, leaf area index, albedo, topographic position index, and wind speed are independently tested to determine the most well‐suited predictor variables for optimal model performance. During the mapping process, input data scarcity is addressed first by gap‐filling critical stations at high elevation using a predetermined linear relationship with other strongly‐correlated stations, and second, by supplementing the training dataset with station data from neighboring islands. Despite the numerous covariates physically linked to temperature, the most parsimonious model selection uses elevation as its sole predictor, and the inclusion of the additional variables results in increased cross‐validation errors. The mean absolute error of resultant estimatedT_maxandT_minmaps over the Hawaiian Islands from 1990 to 2021 is 1.7°C and 1.3°C, respectively. Corresponding bias values are 0.01°C and −0.13°C, respectively for the same variables. Overall, the results show the proposed methodology can robustly generate daily air temperature maps from point‐scale measurements over complex topography. 

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.