Research and Technical Expertise

Organisation Overview

The Digital Humanities Institute (DHI) develops digital infrastructures and computational methodologies for research in the Social Sciences and Humanities (SSH). The institute combines expertise in research software engineering, cultural heritage and societal data infrastructures, and computational analysis to support interdisciplinary research projects involving large-scale cultural, societal, historical, and linguistic datasets.

1. Digital Research Infrastructure

Design and development of sustainable digital platforms for research projects to ensure research outputs become usable digital infrastructures.

Examples:

  • Research databases and digital archives
  • Digital collections platforms
  • Search and retrieval systems
  • AI-assisted discovery, search, and research tools
  • Web applications and digital publishing
  • Long-term digital sustainability
2. Cultural Data Engineering

Development and structuring of complex social science, humanities, and cultural heritage datasets to support interoperable and sustainable cultural and societal data ecosystems.

Examples:

  • Metadata standards and ontologies
  • Semantic annotation and linked open data
  • AI-supported methods for large-scale data extraction and structuring
  • Digitisation and data modelling
  • Record linkage and entity matching
  • FAIR data implementation
3. Computational Analysis of Cultural and Societal Data

Application of advanced computational methods to social science, humanities and heritage datasets to generate new research insights from large datasets.

Examples:

  • NLP and computational linguistics
  • Machine learning and large language models (LLMs)
  • Corpus analysis and semantic change
  • Geospatial humanities and network analysis
  • Large-scale text analysis
4. Digital Cultural Heritage and Humanities Research

Humanities research expertise integrated with digital methods to ensure projects are research-driven, not just technical.

Examples:

  • AI-supported interpretation and reconstruction of cultural heritage, including machine learning analysis of cultural datasets and digital reconstruction of historical environments.
  • Digital archives and heritage infrastructures, including interoperable repositories and sustainable platforms for cultural heritage data.
  • Museum and collections data, including modelling and analysis of museum, library, and archival datasets.
  • Computational and corpus-based linguistic analysis of large text datasets, including semantic change and discourse analysis.
  • Historical gazetteers and spatial humanities, including geospatial datasets for analysing historical places and cultural processes.
  • AI-assisted scholarly editing and manuscript analysis, including automated transcription, variant detection, and computational comparison of textual witnesses.
  • Colonial collections and restitution studies, including provenance research and digital approaches to restitution and decolonisation.