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As research projects grow more complex and researchers use a mix of tools - command-line scripts, science gateways, and Jupyter notebooks - it becomes increasingly difficult to track exactly how a final result was produced. Each tool often keeps its own logs, making it hard to reconstruct the full sequence of computational steps. This lack of end-to-end visibility poses a serious challenge for scientific reproducibility. Yet advanced computing remains a critical part of nearly every field of academic research, and researchers continue to rely on a wide range of interfaces to run their scientific software. To address this challenge, the Advanced Computing Interfaces group at the Texas Advanced Computing Center (TACC) created a system that collates logs from multiple sources - science gateways, Jupyter notebooks, and the Tapis platform - into one unified “audit trail.” The TACC Research Audit and Integration of Logs (TRAIL) system allows researchers and staff to follow the complete path a dataset or file took: from the moment it was first uploaded to TACC, through every step of computation, to the final result. This kind of tracking helps ensure scientific results can be reproduced and gives advanced computing services better insight into how data and resources are being used. 

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Large scale collaborative science requires tools to facilitate sharing of data, protocols, analysis tools, as well as data products and their provenance. We describe here two recent science gateways successfully deployed to accomplish collaborative research. The first is the Synergistic Discovery and Design Environment (SD2E), which was a web-based analysis platform for collaborative analysis, data sharing, and application development. The second is the 3D Electron Microscopy (3DEM) portal, which is a web-based research platform focused on developing and disseminating new technologies for enhanced resolution 3DEM. Both gateways were hosted and connected to high-performance computing resources at the Texas Advanced Computing Center. 

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In the last decade, the rise of hosted Software-as-a-Service (SaaS) application programming interfaces (APIs) across both academia and industry has exploded, and simultaneously, microservice architectures have replaced monolithic application platforms for the flexibility and maintainability they offer. These SaaS APIs rely on small, independent and reusable microservices that can be assembled relatively easily into more complex applications. As a result, developers can focus on their own unique functionality and surround it with fully functional, distributed processes developed by other specialists, which they access through APIs. The Tapis framework, a NSF funded project, provides SaaS APIs to allow researchers to achieve faster scientific results, by eliminating the need to set up a complex infrastructure stack. In this paper, we describe the best practices followed to create Tapis APIs using Python and the Stream API as an example implementation illustrating authorization and authentication with the Tapis Security Kernel, Tenants and Tokens APIs, leveraging OpenAPI v3 specification for the API definitions and docker containerization. Finally, we discuss our deployment strategy with Kubernetes, which is an emerging orchestration technology and the early adopter use cases of the Streams API service.