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Integrated Data Visualization – the Challenges of Linked Open Data

Fenella G France - Library of Congress (USA), Andrew Forsberg - Library of Congress (USA)

Presentation type: Presentation

Abstract:

The need for interoperable platforms to share cultural heritage information is an important aspect in the digital realm, especially with cultural heritage, where research requires the collaboration of diverse disciplines. Heritage science is the application of science and technology to heritage to improve the understanding, engagement and long-term management of cultural heritage. The research data created and needed for preserving heritage is inherently multi-disciplinary, drawing scientists from a diverse range of fields – chemistry, physics, material science, engineering, and archeology, to name a few. Heritage science data is any information captured about cultural heritage material, from paintings, sculptures, manuscripts, ceramics, and any other item including housing materials and other environmental factors that exist in a library, archive, museum, art gallery, or on a historic site. Monitoring of sites and environment as well as tracking change over time is an important component that is part of heritage science but also many other scientific research projects; from marine science, to medicine, climate change and archaeology. The need for authoritative linked open data (LOD) is critical to enable efficient collaboration and accurate sharing of research data between aligned fields. LOD needs to be globally accessible, interlinked, structured data, with the caveat that it can be accessed easily and can expand the creation of knowledge without impacting the original data source. Currently a Data Visualization Project Initiative (DVP) has created a cloud-based integration of scientific data analyses linked to geo-located data on a visual rendering of the heritage object.

This initiative uses a commonly shared international infrastructure, the International Image Interoperability Framework (IIIF), expanding the framework and open access through the Mirador viewer to include scientific data from a range of science, technology, engineering and mathematics (STEM) disciplines. One of the challenges with including the scientific data is linking to authoritative sources for instrumentation, methods, and scientific definitions, terminology and vocabulary, and ensuring this can be done within the IIIF framework. A number of plug-in applications using complementary software have expanded existing IIIF capabilities for linking and annotating data layers. Avoiding reinventing the wheel for terminology has led to the exploration of available LOD sources, and working with the challenges of integrating the original source terminology, as well as assuring datasets that are reusable and active, rather than static data created for one purpose. Discussions with colleagues at other cultural heritage and research institutions – libraries, archives, museums and art galleries, – have revealed similar challenges with integrating LOD. Data and informatics and library colleagues at the National Institute for Standards and Technology (NIST) and other institutions have indicated significant crossover between research data within chemistry, archaeology, materials science, physics and other fields. Addressing the current capabilities for true linked open data that exist within STEM disciplines to provide authoritative sources will form the scope of the discussion around heritage science. Data analytical and instrumentation types include spectral imaging, x-ray fluorescence (XRF), gas-chromatography-mass spectrometry (GC-MS), fiber optic reflectance spectrometry (FORS), Fourier transform infrared spectrometry (FTIR) to name a few.

Topics:

  • Annotation, including full-text or academic use cases,
  • Linked Open “Usable” Data (LOUD) and IIIF,
  • IIIF Implementation Spectrum: large-scale or small-scale projects,
  • Interoperability in IIIF contexts

Keywords:

  • LOD,
  • cultural heritage,
  • heritage science,
  • interoperable,
  • annotated data