The inventory of archaeological material is an old practice and basic to the method of all research and analysis. The whole purpose of finding archaeological material is to be able to keep track of its provenience and describe the cultural history of that object. Fundamentally archaeology is about things and the relationships between things and in turn how those relationships map onto social organization (Hodder 2011). As an important institution for the long-term storage of archaeological material, the museum has always been used as the ideal storage place for archaeologists. The museum is able to both show material off to the public and privately maintain a larger research collection, out of the view of the museum visitor. The curation of material in museums is meant to help guide the interpreter to understand the relationship between artifacts and come to some kind of synthetic conclusion regarding the material. By creating a way to understand the relationship between different kinds of artifacts, meaning is produced but, in a semi-prescribed way, the visitor is able to draw their own connections between the artifacts as they choose. Behind the presentation of artifacts are thousands more artifacts, stored and organized for later study or analysis.

Archaeological data is perfectly suited to 3D modeling and visualization. The geographical remoteness of many heritage sites means that few will ever be able to experience them firsthand. For centuries, practitioners of archaeology have meticulously drafted maps and illustrations, and captured photographs of sites and landscapes (Sanders 2014). Two-dimensional maps and photos reflect scale but never fully embody it. Hence, one of the goals of virtual reality modeling is to take another step toward bridging this gap. This article presents a variety of ways archaeologists, working closely with colleagues from computer science, visual arts, and engineering, have worked toward disseminating archaeological datasets to professionals and non-professional audiences alike through virtual reality.

In 2016, a team of earth scientists directly engaged a team of computer scientists to identify cyberinfrastructure (CI) approaches that would speed up an earth science workflow. This paper describes the evolution of that workflow as the two teams bridged CI and an image segmentation algorithm to do large scale earth science research. The Pacific Research Platform (PRP) and The Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI) resources were used to significantly decreased the earth science workflow's wall-clock time from 19.5 days to 53 minutes. The improvement in wall-clock time comes from the use of network appliances, improved image segmentation, deployment of a containerized workflow, and the increase in CI experience and training for the earth scientists. This paper presents a description of the evolving innovations used to improve the workflow, bottlenecks identified within each workflow version, and improvements made within each version of the workflow, over a three-year time period.