Exploring Research Data Management in the Visual Arts

Across the higher education sector, research councils, organizations, teams, and researchers are under pressure to make publicly funded research data freely available, and in line with the Research Councils UK guidance. Publication of data resulting from the research is increasingly a requirement of funding. Equally important is data transparency and the ability for researchers to access data in order to test the validity and reliability of the research outputs and methods; to reinterpret and reuse data, thereby adding value to publicly funded research; and, ultimately, to access the data in the longer term. By its very nature, research in the visual arts is highly complex and varied, often comprising a wide variety of outputs and formats that present researchers, information managers, and technology teams with many discipline-specific issues. Examples include sketch books, paintings, architectural plans and buildings, physical artifacts, and complex modelling algorithms. Additionally, the methods and processes that generate this type of research information are just as varied and complex. Research in the visual arts relies heavily on sketchbooks, logbooks, journals, and workbooks. Alongside this data, a wide range of related research documentation and protocols (such as “how-to guides” and methodology reports) are also created. The physical nature of research in the arts presents researchers and curators with significant problems with security and preservation issues while also greatly increasing the risk of data loss and deterioration. Issues arise, for example, in the field of architecture. When data is locked up in the physical building that has been created as the output, how can this information be preserved and managed? Therefore, appropriate curation and management of research data in the visual arts is essential to • satisfy funding requirements and demands for open access; • limit the issue that production of data can be extremely time-consuming and therefore costly; • reduce its lack of discoverability or loss; • enable other researchers to test the reliability and validity of the data and the research method;