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Interactive computing with Jupyter Notebooks has transformed the state of the art of scientific research computing. Users can perform a multitude of computational tasks in real time, including data cleansing, analysis, visualization, and postprocessing, with Jupyter Notebooks. Additionally, the ability to write and execute code and include supporting text and images in the same document has made it popular for use in scholarly articles and teaching. These capabilities complement the batch computing services provided by high-performance computing centers exposed through traditional science gateways. However, integrating Jupyter into science gateways and other advanced computing ecosystems introduces new challenges related to scalability, collaboration, and reproducibility. In this article, we discuss the Scalable Interactive Computing (Scinco) project, an open source platform for scalable, reproducible, interactive scientific computing designed to be run in academic computing centers and incorporated into science gateways. We describe the prime features, architecture, deployment choices, and challenges of Project Scinco. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]