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Current computational notebooks, such as Jupyter, are a popular tool for data science and analysis. However, they use a 1D list structure for cells that introduces and exacerbates user issues, such as messiness, tedious navigation, inefficient use of large screen space, performance of non-linear analyses, and presentation of non-linear narratives. To ameliorate these issues, we designed a prototype extension for Jupyter Notebooks that enables 2D organization of computational notebook cells into multiple columns. In this paper, we present two evaluative studies to determine whether such “2D computational notebooks” provide advantages over the current computational notebook structure. From these studies, we found empirical evidence that our multi-olumn 2D computational notebooks provide enhanced efficiency and usability. We also gathered design feedback which may inform future works. Overall, the prototype was positively received, with some users expressing a clear preference for 2D computational notebooks even at this early stage of development. 

Current computational notebooks, such as Jupyter, are a popular tool for data science and analysis. However, they use a 1D list structure for cells that introduces and exacerbates user issues, such as messiness, tedious navigation, inefficient use of large screen space, performance of non-linear analyses, and presentation of non-linear narratives. To ameliorate these issues, we designed a prototype extension for Jupyter Notebooks that enables 2D organization of computational notebook cells into multiple columns. In this paper, we present two evaluative studies to determine whether such “2D computational notebooks” provide advantages over the current computational notebook structure. From these studies, we found empirical evidence that our multi-olumn 2D computational notebooks provide enhanced efficiency and usability. We also gathered design feedback which may inform future works. Overall, the prototype was positively received, with some users expressing a clear preference for 2D computational notebooks even at this early stage of development. 

Current computational notebooks, such as Jupyter, are a popular tool for data science and analysis. However, they use a 1D list structure for cells that introduces and exacerbates user issues, such as messiness, tedious navigation, inefficient use of large screen space, performance of non-linear analyses, and presentation of non-linear narratives. To ameliorate these issues, we designed a prototype extension for Jupyter Notebooks that enables 2D organization of computational notebook cells into multiple columns. In this paper, we present two evaluative studies to determine whether such “2D computational notebooks” provide advantages over the current computational notebook structure. From these studies, we found empirical evidence that our multi-olumn 2D computational notebooks provide enhanced efficiency and usability. We also gathered design feedback which may inform future works. Overall, the prototype was positively received, with some users expressing a clear preference for 2D computational notebooks even at this early stage of development. 

Current computational notebooks, such as Jupyter, are a popular tool for data science and analysis. However, they use a 1D list structure for cells that introduces and exacerbates user issues, such as messiness, tedious navigation, inefficient use of large screen space, performance of non-linear analyses, and presentation of non-linear narratives. To ameliorate these issues, we designed a prototype extension for Jupyter Notebooks that enables 2D organization of computational notebook cells into multiple columns. In this paper, we present two evaluative studies to determine whether such “2D computational notebooks” provide advantages over the current computational notebook structure. From these studies, we found empirical evidence that our multi-olumn 2D computational notebooks provide enhanced efficiency and usability. We also gathered design feedback which may inform future works. Overall, the prototype was positively received, with some users expressing a clear preference for 2D computational notebooks even at this early stage of development. 

Translational software research bridges the gap between scientific innovations and practical applications, driving impactful societal advancements. However, developing such software is challenging due to interdisciplinary collaboration, technology adoption, and postfunding sustainability. This article presents the experiences and insights of the Scalable Adaptive Graphics Environment (SAGE) team, which has spent two decades developing translational, cross-disciplinary, collaboration tools to benefit computational science research. With a focus on SAGE and its next-generation iterations, we explore the inherent challenges in translational research, such as fostering cross-disciplinary collaboration, motivating technology adoption, and ensuring postfunding product sustainability. We also discuss the roles of funding agencies, policymakers, and academic institutions in promoting translational research. Although the journey is fraught with challenges, the societal impact and satisfaction derived from translational research underscore its significance in the broader scientific landscape. This article aims to encourage further conversation and the development of effective models for translational software projects. 

In collaboration with the Center for Microbiome Analysis through Island Knowledge and Investigations (C-MĀIKI), the Hawaii EPSCoR Ike Wai project and the Hawaii Data Science Institute, a new science gateway, the C-MĀIKI gateway, was developed to support modern, interoperable and scalable microbiome data analysis. This gateway provides a web-based interface for accessing high-performance computing resources and storage to enable and support reproducible microbiome data analysis. The C-MĀIKI gateway is accelerating the analysis of microbiome data for Hawaii through ease of use and centralized infrastructure. 

Microbes are found in nearly every habitat and organism on the planet, where they are critical to host health, fitness, and metabolism. In most organisms, few microbes are inherited at birth; instead, acquiring microbiomes generally involves complicated interactions between the environment, hosts, and symbionts. Despite the criticality of microbiome acquisition, we know little about where hosts’ microbes reside when not in or on hosts of interest. Because microbes span a continuum ranging from generalists associating with multiple hosts and habitats to specialists with narrower host ranges, identifying potential sources of microbial diversity that can contribute to the microbiomes of unrelated hosts is a gap in our understanding of microbiome assembly. Microbial dispersal attenuates with distance, so identifying sources and sinks requires data from microbiomes that are contemporary and near enough for potential microbial transmission. Here, we characterize microbiomes across adjacent terrestrial and aquatic hosts and habitats throughout an entire watershed, showing that the most species-poor microbiomes are partial subsets of the most species-rich and that microbiomes of plants and animals are nested within those of their environments. Furthermore, we show that the host and habitat range of a microbe within a single ecosystem predicts its global distribution, a relationship with implications for global microbial assembly processes. Thus, the tendency for microbes to occupy multiple habitats and unrelated hosts enables persistent microbiomes, even when host populations are disjunct. Our whole-watershed census demonstrates how a nested distribution of microbes, following the trophic hierarchies of hosts, can shape microbial acquisition. 

Abstract — SAGE (the Scalable Adaptive Graphics Environment) and its successor SAGE2 (the Scalable Amplified Group Environment) are operating systems for managing content across wideband display environments. This paper documents the prevalent usage patterns of SAGE-enabled display walls in support of the e-Science enterprise, based on nearly 15 years of observations of the SAGE community. These patterns will help guide e-Science users and cyberinfrastructure developers on how best to leverage large tiled display walls, and the types of software services that could be provided in the future.