The continued expansion in size and resolution of volumetric datasets generated by electron microscopy (3DEM) is making high-performance computing (HPC) an essential component. HPC provides the scalable, parallelized infrastructure required to process and analyze these increasingly large datasets. The Texas Advanced Computing Center’s (TACC) Core Experience Portal (CEP) is a science gateway specialized for leveraging HPC. The CEP can be adapted into customized versions based on a research community’s needs. Here we have constructed 3dem.org as a gateway for the community exploring volumetric data generated by electron microscopy (3DEM). This gateway provides the 3DEM community with user-friendly browser-based access to raw and processed datasets including both private and shared data, image processing tools for alignment and segmentation, and simulation and analysis environments. All this is linked to the underlying HPC environment at TACC with the ability to connect to other data storage and compute systems. 3dem.org bridges advanced electron microscopy with HPC, providing the research community with scalable, accessible infrastructure for discovery.

The Texas Advanced Computing Center (TACC) operates multiple initiatives aimed at broadening participation in cyberinfrastructure (CI) and high-performance computing (HPC). One of these programs is the TACC Professional Internship Program (TPIP) for software engineering, an initiative that provides early career or non-traditional background participants with an immersive hands-on team-based experience in the use of software development best practices within a production HPC environment. TPIP aims to provide participants with the foundational skills in support of productive careers. We detail here the motivation, implementation, and structure for this program, as well as the impacts, insights, and recommendations resulting from the past eight years.

Science gateways are a crucial component of critical infrastructure as they provide the means for users to focus on their topics and methods instead of the technical details of the infrastructure. They are defined as end-to-end solutions for accessing data, software, computing services, sensors, and equipment specific to the needs of a science or engineering discipline and their goal is to hide the complexity of the underlying infrastructure. Science gateways are often called Virtual Research Environments in Europe and Virtual Labs in Australasia; we consider these two terms to be synonymous with science gateways. Over the past decade, artificial intelligence (AI) and machine learning (ML) have found applications in many different fields in private industry, and private industry has reaped the benefits. Likewise, in the academic realm, large-scale data science applications have also learned to apply public high-performance computing resources to make use of this technology. However, academic and research science gateways have yet to fully adopt the tools of AI. There is an opportunity in the gateways space, both to increase the visibility and accessibility to AI/ML applications and to enable researchers and developers to advance the field of science gateway cyberinfrastructure itself. Harnessing AI/ML is recognized as a high priority by the science gateway community. It is, therefore, critical for the next generation of science gateways to adapt to support the AI/ML that is already transforming many scientific fields. The goal is to increase collaborations between the two fields and to ensure that gateway services are used and are valuable to the AI/ML community. This chapter presents state-of-the-art examples and areas of opportunity for the science gateways community to pursue in relation to AI/ML and some vision of where these new capabilities might impact science gateways and support scientific research.

The focus on software usability, long-lasting and reproducible software is a timely one that spans various domains of science and significant investment of research funding across the US, Europe, U.K, and elsewhere.The three concepts -usability, sustainability and reproducibility are interconnected with each other and cover a wide range of application areas.They affect all layers of the software processfrom enabling reproducing experiments via an easy user interface to using containerization for application portability.This minitrack focused on the broad spectrum of submissions that deal with complex scenarios such as containerization, strategies for long-lasting software, usability and user interface issues, handling data curation and provenance and more.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

The Science Gateways Community Institute (SGCI) is an NSF Software Infrastructure for Sustained Innovation (S2I2) funded project that leads and supports the science gateway community. Major activities for SGCI include a) sustainability training, including the Focus Week week-long course designed to help science gateway operators develop sustainability plans, and the Jumpstart virtual short-course; b) usability and user experience consulting; c) a community catalog of science gateways and science gateway software; d) workforce development activities, including a coding institute for students, internship opportunities, and hackathons; e) an annual conference; and f) in-depth technical support for client gateway projects. The goals of SGCI's Embedded Technical Support component are to help the institute's clients to create new science gateways or to significantly enhance existing science gateways. Examples of the latter include helping to implement major new capabilities and to implement significant usability improvements suggested by SGCI's usability consultants. The Embedded Technical Support component was managed by Indiana University and involved research software engineers at San Diego Supercomputer Center, Texas Advanced Computing Center, Indiana University, and Purdue University (through 2019). Since 2016, the component has involved 20 research software engineers as consultants and has conducted 59 client consultations. This short paper provides a summary of lessons learned from the Embedded Technical Support program that may be useful for the research software engineering community.

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.

Science gateway projects face challenges utilizing the vast and heterogeneous landscape of powerful cyberinfrastructure available today, and interoperability across technologies remains poor. This interoperability issue leads to myriad problems: inability to bring multiple heterogeneous specialized resources together to solve problems where different resources are optimized for different facets of the problem; inability to choose from multiple resources on-the-fly as needed based on characteristics and available capacity; and ultimately a less than optimal application of nationally-funded resources toward advancing science. This paper presents version 1.0 of the Science Gateways Community Institute (SGCI) Resource Description Specification – a schema providing a common language for describing storage and computing resources utilized by science gateway technologies – as well as an Inventory API and software development kits for incorporating resource definitions into gateway projects. We discuss multiple gateway integration design options, with trade offs regarding robustness and availability. We detail the adoption to date of the SGCI Resource Specification by several prominent projects, including Apache Airavata, HUBzero®, Open OnDemand, Tapis, and XSEDE. The XSEDE adoption is worth highlighting explicitly as it has led to a new API within the XSEDE Information Services architecture which provides SGCI resource descriptions of all active XSEDE resources. Additionally, we show how the use of the SGCI Resource Specification provides interoperability across resource providers and projects that adopt it. Finally, as a proof of concept, we present a multi-step analysis that runs Quantum ESPRESSO and visualizes the energy band structures of a Gallium Arsenide (GaAs) crystal across multiple resource providers including the Halstead cluster at Purdue University and the Stampede2 supercomputer at TACC.

Modern computational research increasingly spans multiple, geographically distributed data centers and leverages instruments, experimental facilities and a network of national and regional cyberinfrastructure (CI). Tapis is an open-source API platform developed at the Texas Advanced Computing Center at the University of Texas at Austin to increase reproducibility and minimize time-to-solution for distributed computational experiments. Core features of Tapis include data management and code execution, a fine-grained permissions system enabling objects to be saved privately, shared with individuals or “published” to a community, and provenance endpoints exposing the detailed history Tapis collects on analyses, enabling workflows to be repeated and results reproduced. In this paper, we describe the evolution of the Tapis platform, from its origins in 2008, and discuss the growth and success of the project as well as challenges and limitations that have led to a new design effort, funded by the National Science Foundation in September of 2019. We present a detailed overview of the new system, including reference architecture and new features such as support for streaming/sensor data, and we discuss some of the early science use cases driving its design. We conclude with the roadmap for future work.

Summary The explosion of IoT devices and sensors in recent years has led to a demand for efficiently storing, processing and analyzing time‐series data. Geoscience researchers use time‐series data stores such as Hydroserver, Virtual Observatory and Ecological Informatics System (VOEIS), and Cloud‐Hosted Real‐time Data Service (CHORDS). Many of these tools require a great deal of infrastructure to deploy and expertise to manage and scale. The Tapis framework, an NSF funded project, provides science as a service APIs to allow researchers to achieve faster scientific results, by eliminating the need to set up a complex infrastructure stack. The University of Hawai'i (UH) and Texas Advanced Computing Center (TACC) have collaborated to develop an open source Tapis Streams API that builds on the concepts of the CHORDS time series data service to support research. This new hosted service allows storing, processing, annotating, archiving, and querying time‐series data in the Tapis multi‐user and multi‐tenant collaborative platform. The Streams API provides a hosted production level middleware service that enables new data‐driven event workflows capabilities that may be leveraged by researchers and Tapis powered science gateways for handling spatially indexed time‐series datasets.

The Tapis framework, an NSF-funded project, is an open-source, scalable API platform that enables researchers to perform distributed computational experiments securely and achieve faster scientific results with increased reproducibility. Tapis Streams API focuses on supporting scientific use cases that require working with real-time sensor data. The Streams Service, built on the top of the CHORDS time-series data service, allows storing, processing, annotating, querying, and archiving time-series data. This paper focuses on the new Tapis Streams API functionality that enables researchers to design and execute real-time data-driven event workflow for their research. We describe the architecture and design choices towards achieving this new capability with Streams API. Specifically, we demonstrate the integration of Streams API with Kapacitor, a native data processing engine for time-series database InfluxDB, and Abaco, an NSF Funded project, web service, and distributed computing platform providing function-as-a-Service (FaaS). The Streams API, which includes a wrapper interface for the Kapacitor alerting system, can define and enable alerts. Finally, simulation results from the water-quality use case depict that Streams API’s new capabilities can support real-time streaming data event-driven workflows.

In 2019 the National Science Foundation in the United States began operation of its Frontera supercomputer at the Texas Advanced Computing Center, debuting at number 5 on the TOP500 list. Unlike more general purpose supercomputers, Frontera was designed for the focused community of users doing computational science at the largest scale. This special issue features articles from Frontera's science users spanning the sciences from climate change to tornadoes and organic solar cells. We also include two computational science articles highlighting the tools and techniques that are needed to enable users running single jobs beyond 8,000 nodes.

As more research depends fundamentally on software, sustainability becomes increasingly critical. Nevertheless, despite valiant efforts from a growing number of researchers and practitioners, a basic understanding of best-practices for sustainable software remains elusive. In this paper, we review the specific practices and strategies that have helped to sustain Tapis, a cyberinfastructure project that has been in use for over a decade. The Tapis framework is an open-source, software-as-a-service Application Programming Interface (API) for collaborative, automated, reproducible, computational research which began as the Foundation API for the iPlant Collaborative Project in 2008. Today Tapis is used by tens of thousands of individuals across more than a dozen active projects. This paper describes our multi-faceted approach to sustaining an increasingly complex ecosystem of software, documentation and other digital assets, including both technical and organizational strategies for minimizing the cost of sustainment while maximizing available resources for sustainment activities.

Beginning with the initial release of the DesignSafe JupyterHub in late 2015, TACC has been building and maintaining custom JupyterHub clusters for research groups across different domains of science and engineering. Today, TACC maintains five production JupyterHub systems utilizing over half a terabyte of memory and hundreds of CPU cores supporting nearly 1,600 unique users combined. In this paper, we describe our approach to utilizing JupyterHub in these cyberinfrastructure projects and our collaborative approach to integrating Jupyter into different research communities. For two such groups, we present an in-depth discussion of the science use cases and technical drivers that informed and evolved the design of our offering, and our outreach and engagement efforts to promote adoption. We discuss the implementation of custom features we have added to better integrate JupyterHub into other components of the cyberinfrastructure platforms, and we conclude with a description of our plans for future architecture and usage of JupyterHub at TACC.

Beginning with the initial release of the DesignSafe JupyterHub in late 2015, TACC has been building and maintaining custom JupyterHub clusters for research groups across different domains of science and engineering. Today, TACC maintains five production JupyterHub systems utilizing over half a terabyte of memory and hundreds of CPU cores supporting nearly 1,600 unique users combined. In this paper, we describe our approach to utilizing JupyterHub in these cyberinfrastructure projects and our collaborative approach to integrating Jupyter into different research communities. For two such groups, we present an in-depth discussion of the science use cases and technical drivers that informed and evolved the design of our offering, and our outreach and engagement efforts to promote adoption. We discuss the implementation of custom features we have added to better integrate JupyterHub into other components of the cyberinfrastructure platforms, and we conclude with a description of our plans for future architecture and usage of JupyterHub at TACC.

We propose a Bayesian model for projecting first-wave COVID-19 deaths in all 50 U.S. states. Our model’s projections are based on data derived from mobile-phone GPS traces, which allows us to estimate how social-distancing behavior is “flattening the curve” in each state. In a two-week look-ahead test of out-of-sample forecasting accuracy, our model significantly outperforms the widely used model from the Institute for Health Metrics and Evaluation (IHME), achieving 42% lower prediction error: 13.2 deaths per day average error across all U.S. states, versus 22.8 deaths per day average error for the IHME model. Our model also provides an accurate, if slightly conservative, assessment of forecasting accuracy: in the same look-ahead test, 98% of data points fell within the model’s 95% credible intervals. Our model’s projections are updated daily at https://covid-19.tacc.utexas.edu/projections/ .