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In this paper we present the first naturalistic (in-situ) exploratory study seeking to apply mixed-reality (MR) technologies within the industrial chemical laboratory (wet lab) domain with the aim of identifying opportunities and challenges for such applications. This research was conducted in partnership with Agilent Technologies (Agilent), an industry leader in the wet lab domain, which allowed us to draw on domain expert knowledge of actual work practices to inform the design of our system and its subsequent evaluation. This naturalistic approach is in stark contrast to most existing MR research, which usually involves tightly controlled experimental conditions. Despite this, designing and evaluating solutions in-situ must be explored in order to better understand how these systems succeed or fail to meet user requirements in an industrial environment involving actual work practices. This approach enabled the discovery of a new construct which we term “physically embedded data”. We conclude that existing process models need to be extended to facilitate the design of effective MR systems for knowledge work practices by explicitly incorporating this phenomenon. This understanding also forms the basis for further research opportunities into a new system design methodology for industrial MR support systems for knowledge work practices.



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