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Constructing and executing reproducible workflows is fundamental to performing research in a variety of scientific domains. Many of the current commercial and open source solutions for workflow en- gineering impose constraints—either technical or budgetary—upon researchers, requiring them to use their limited funding on expensive cloud platforms or spend valuable time acquiring knowledge of software systems and processes outside of their domain expertise. Even though many commercial solutions offer free-tier services, they often do not meet the resource and architectural requirements (memory, data storage, compute time, networking, etc) for researchers to run their workflows effectively at scale. Tapis Workflows abstracts away the complexities of workflow creation and execution behind a web-based API with a simplified workflow model comprised of only pipelines and tasks. This paper will de- tail how Tapis Workflows approaches workflow management by exploring its domain model, the technologies used, application architecture, design patterns, how organizations are leveraging Tapis Workflows to solve unique problems in their scientific workflows, and this projects’s vision for a simple, open source, extensible, and easily deployable workflow engine. 

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, VOEIS and CHORDS. Many of these tools require a great deal of infrastructure to deploy and expertise to manage and scale. Tapis's (formerly known as Agave) platform as a service provides a way to support researchers in a way that they are not responsible for the infrastructure and can focus on the science. The University of Hawaii (UH) and Texas Advanced Computing Center (TACC) have collaborated to develop a new API integration that combines Tapis with the CHORDS time series data service to support projects at both institutions for storing, annotating and querying time-series data. This new Streams API leverages the strengths of both the Tapis platform and CHORDS service to enable capabilities for supporting time-series data streams not available in either tool alone. These new capabilities may be leveraged by Tapis powered science gateways with needs for handling spatially indexed time-series data-sets for their researchers as they have been at UH and TACC. 

The Chameleon testbed is a case study in adapting the cloud paradigm for computer science research. In this paper, we explain how this adaptation was achieved, evaluate it from the perspective of supporting the most experiments for the most users, and make a case that utilizing mainstream technology in research testbeds can increase efficiency without compro- mising on functionality. We also highlight the opportunity inherent in the shared digital artifacts generated by testbeds and give an overview of the efforts we’ve made to develop it to foster reproducibility.