In this work, we study how to co-locate meta information with visualizations by directly embedding information into visualizations. This allows for visualizations to carry provenance and authorship information themselves for reproducibility. We call these self-describing visualizations-reproducible, authenticatable, and documentable. Self-describing visualizations can be used to extend existing visualization provenance systems. Herein, we start with a survey of existing digital image watermarking literature. We search for and classify watermarking algorithms that can support scientific visualizations. Using our payload-resilience testing framework, we evaluate and recommend algorithms supporting various use cases in the payload-resiliency space, and present guidelines for optimizing visualizations to improve payload capacities and embedding robustness. We demonstrate the efficacy of self-describing visualizations with two sample application implementations: (1) adding an embedding filter as a part the standard rendering pipeline, (2) creating a web reader to automatically and reliably extract provenance information from scientific publications for review and dissemination.

In this paper, we propose using a decoupled architecture to create a microservice that can deliver scientific visualization remotely with efficiency, scalability, and superior availability, affordability and accessibility. Through our effort, we have created an open source platform, Tapestry, which can be deployed on Amazon AWS as a production use microservice. The applications we use to demonstrate the efficacy of the Tapestry microservice in this work are: (1) embedding interactive visualizations into lightweight web pages, (2) creating scientific visualization movies that are fully controllable by the viewers, (3) serving as a rendering engine for high-end displays such as power-walls, and (4) embedding data-intensive visualizations into augmented reality devices efficiently. In addition, we show results of an extensive performance study, and suggest how applications can make optimal use of microservices such as Tapestry.