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IIIF WASHINGTON 2018 | May 21 - 25 IN WASHINGTON DC

2018 Washington conference submission

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Expanding IIIF for Heritage Scientific Data Visualization and Layering

Fenella France - Library of Congress (United States), Glen Robson - IIIF (United Kingdom), Meghan Wilson - Library of Congress (United States), Chris Bolser - Library of Congress (United States)

Abstract: The field of heritage science is truly multidisciplinary, with collaborations and scientific data being captured from diverse fields such as; materials science, archaeology, botany, biology, engineering, physics and chemistry. The challenge for data in the heritage realm working across disciplines, is sharing this not only with other scientific partners, but making it available within the cultural humanities. This requires an interoperable platform, ease of use, sustainable access, open source file formats, and the capacity for a more integrated approach to data visualization.
This use case of heritage science is a prototype for assessing the capabilities of the IIIF canvas for integrating and layering different views of the same data, while adding cross-disciplinary annotations, and other linked data as additional layers and annotation. Many related fields and disciplines have begun to focus on the need to integrate and assess approaches from other scientific colleagues – while also recognizing the necessity to engage and learn from the humanities, since the data being generated by both groups of scholars is simply viewed from different perspectives. An initiative for linked scientific data generated from heritage materials has been developed within the Library of Congress Preservation Research and Testing Division. A relational database integrating all different types of scientific analyses links these back to the original heritage object, building upon existing standards and authorities. What makes this approach an effective digital tool, is being able to link the various data layers back to a single heritage object with the digital rendition being seen as a “digital cultural object” (DCO) that becomes the digital connection back to the original. This object centric approach allows for a discussion platform for shared data from colleagues.
Since images convey so much information, the base scientific data is a stack of spectral imaging data layers – capturing the response of materials to different visible and non-visible illumination. Turning on and off layers within the Mirador viewer allows comparison of layers within the stack, with additional non-invasive scientific data as annotations. These include X-ray fluorescence (XRF) to determine inorganic elements present, Fourier transform infrared spectroscopy (FTIR) for organic structural information, fiber optic reflectance spectroscopy FORS) for molecular and separation of inks, binder and pigments This rich volume of data is augmented with descriptive metadata describing the data layers and annotations to allow better understanding of the cultural object – what materials have been used?, how was the object constructed?, has it changed over time?
Improving the utility of existing IIIF capabilities can expand the resources for museums, archives, libraries and other cultural institutions, as well as enabling better collaboration with academic partners. Some of the challenges still under discussion and development include accurate X, Y coordinate and geospatial location for layering and adding scientific analyses to the base canvas image, being able to annotate individual layers, inputting specific file formats, extracting and including embedded metadata, and the capability to add structured metadata.

Presentation type: 20 minute presentations (plus 5 mins questions)

Topics:

  • IIIF synergies with regional and national ongoing digital libraries, museums and archives initiatives,
  • IIIF enabled collaboration,
  • IIIF content communities (museums, manuscripts, newspapers, archival content, etc.),
  • Emerging use cases for IIIF technical specifications

Keywords:

  • visualization,
  • heritage science,
  • linked data