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Research Hypotheses and Objectives

Visualisation methods are increasingly prominent in research across a wide range of arts and humanities disciplines, and in the cultural heritage sector. Amongst these, one of the most challenging, but potentially rewarding, is 3-dimensional (3D) visualisation. However, if users of 3D visual research outputs are to understand and critically to evaluate them, information is required about the incremental process of data gathering and assessment, testing of hypotheses, and consequent choices through which the outcomes were produced. In addition, researchers need to convey distinctions between “hard fact”, plausible conjecture, intelligent guesses, and alternative hypotheses.

Despite a number of initiatives in this area, there is as yet no broad consensus or widely-accepted standard regarding the nature or degree of documentation that visualisation-based projects should include in order for their research outcomes to be "transparent". This absence has undoubtedly contributed to the variable quality of both visualisation-based research processes and outputs, and continues to compromise the perception of such projects within the wider community. With the increasing availability of inexpensive 3D-visualisation-creation tools that can be deployed by those with little or no discipline-based training, the lack of standards for 3D research visualisation documentation is a problem that acquires ever greater urgency. Conversely, the creation and adoption of such standards could dramatically improve the quality standing, and consequent use of potentially very valuable research methods.

The project examines the hypotheses that if the intelectual capital and tacit knowledege that exists within the process of visulisation can be idenitfied and recorded then the visual reseach based outcomes of a project will become less opaque to the end recipient. The objectives of the project can therefore be summerised as follows:

  • Define the new type of data.
  • Examine current practices to capture paradata (if any exist).
  • Explore different methods of capture.
  • Investigate processes by which paradata can be efficently dessimenated.
  • Run trails against exisitng and new project cycles to determine effecacy of the recording process.