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The need for distance and virtual learning platforms has been emphasized by the COVID-19 pandemic. With the closure of campuses in Spring of 2020 and many classes moving to online only in Fall of 2020, platforms for facilitating computationally oriented curriculum have had to be quickly adopted. Open OnDemand offers a familiar web-based portal to computational resources such as high-performance computing and cloud. Through OnDemands customizable dashboard, students can be offered an interface tailored to the course schedule giving them a just what I need view. Advantages to instructors include a web accessible, platform agnostic interface leading to less time for troubleshooting local student platforms and more time for discussion of the core course curriculum, a fully customizable course page, access controls, and more. Here we present Open OnDemand as a platform for developing, deploying, and presenting software and course material to software-oriented classes as used at Ohio Supercomputer Center and Virginia Tech. 

High performance computing workloads often benefit from human in the loop interactions. Steps in complex pipelines ranging from quality control to parameter adjustments are critical to the successful and efficient completion of modern problems. We give several example workflows in bioinformatics and deep learning where computing decisions are made throughout the processing pipelines ultimately changing the course of the compute. We also show how users can interact with the pipeline using Open OnDemand plus XDMoD or Plot.ly. 

Abstract Patterns of genetic diversity within species contain information the history of that species, including how they have responded to historical climate change and how easily the organism is able to disperse across its habitat. More than 40,000 phylogeographic and population genetic investigations have been published to date, each collecting genetic data from hundreds of samples. Despite these millions of data points, meta‐analyses are challenging because the synthesis of results across hundreds of studies, each using different methods and forms of analysis, is a daunting and time‐consuming task. It is more efficient to proceed by repurposing existing data and using automated data analysis. To facilitate data repurposing, we created a database (phylogatR) that aggregates data from different sources and conducts automated multiple sequence alignments and data curation to provide users with nearly ready‐to‐analyse sets of data for thousands of species. Two types of scientific research will be made easier by phylogatR: large meta‐analyses of thousands of species that can address classic questions in evolutionary biology and ecology, and student‐ or citizen‐ science based investigations that will introduce a broad range of people to the analysis of genetic data. phylogatR enhances the value of existing data via the creation of software and web‐based tools that enable these data to be recycled and reanalysed and increase accessibility to big data for research laboratories and classroom instructors with limited computational expertise and resources. 

Summary High performance computing (HPC) has led to remarkable advances in science and engineering and has become an indispensable tool for research. Unfortunately, HPC use and adoption by many researchers is often hindered by the complex way these resources are accessed. Indeed, while the web has become the dominant access mechanism for remote computing services in virtually every computing area, HPC is a notable exception. Open OnDemand is an open source project negating this trend by providing web‐based access to HPC resources (https://openondemand.org). This article describes the challenges to adoption and other lessons learned over the 3‐year project that may be relevant to other science gateway projects. We end with a description of future plans the project team has during the Open OnDemand 2.0 project including specific developments in machine learning and GPU monitoring.