Data Linking
Research Field F
One of the central ideas of the Cluster of Excellence is to combine research in the humanities with research in the natural sciences for the study of written artefacts. Scientists working in artefact profiling analyse materials of written artefacts with a range of recent and up-to-date technologies, thus generating different kinds of associated data (e.g., X-ray data, spectral data). Supported by such materials science data and using various kinds of computational processes, researchers in the humanities have derived new conclusions based on scientific analyses, and, based on results of Research Field F, new results in the field of written artefact research were published in volumes and journal articles.
A data linking infrastructure is used to support humanities scholars from all research fields of the Cluster of Excellence ‘Understanding Written Artefacts’ and various kinds of data are easily and systematically combined. On the one hand, there are images and videos of written artefacts, in some cases associated with text data making parts of image (or video) content explicit, e.g., using optical character recognition techniques. On the other hand, different kinds of chemistry and materials science data are collected to further describe written artefacts under investigation, almost always in combination with descriptive temporal and spatial data. Data of this kind is made available to scientists and scholars such that they are supported in their scientific work.
On the one hand, in the project RFF01, goal of providing information systems with minimum effort has been achieved, which is shown in many interdisciplinary agile projects under the umbrella project RFF01. Latest database technology has been used with natural language processing technology to provide the foundation for linked data shown in information systems derived on the fly from UWA research data that are made persistent in the Hamburg Research Data Repository (RDR).
On the other hand, in RFF02 mathematical and statistical approaches are applied to biological data to classify and characterize the samples that are investigated. This application enables insights into the composition of, e.g., palm-leaf samples that would not be accessible otherwise and is particularly suitable when analytical methods are utilised that generate complex data.
Spokesperson: Ralf Möller
