Large cyberinfrastructure-enabled data repositories generate massive amounts of metadata, enabling big data analytics to leverage on the intersection of technological and methodological advances in data science for the quantitative study of science. This paper introduces a definition of big metadata in the context of scientific data repositories and discusses the challenges in big metadata analytics due to the messiness, lack of structures suitable for analytics and heterogeneity in such big metadata. A methodological framework is proposed, which contains conceptual and computational workflows intercepting through collaborative documentation. The workflow-based methodological framework promotes transparency and contributes to research reproducibility. The paper also describes the experience and lessons learned from a four-year big metadata project involving all aspects of the workflow-based methodologies. The methodological framework presented in this paper is a timely contribution to the field of scientometrics and the science of science and policy as the potential value of big metadata is drawing more attention from research and policy maker communities.

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Progressive embodiment and the subsequent enhancement of presence have been important goals of VR researchers and designers for some time (Biocca, 1997). Consequently, researchers frequently explore the relationship between increasing embodiment and presence yet rarely emphasize the ties between their work and other work on embodiment. More specifically, we argue that experiments manipulating or implementing visual scale, avatar customization, sensory enrichment, and haptic feedback, to name a few examples, all have embodiment as their independent variable. However, very few studies explicitly frame their work as an exploration of embodiment. In this paper we will leverage the field of Embodied Cognition to help clarify the concept of embodiment.

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