A Digital Humanities project is one which uses digital methods and computational techniques as part of its research methodology, dissemination plan, and/or public engagement.

A typical Digital Humanities project will use both digital and non-digital methods in its research design. Digital methods enable scholars to ask questions that are difficult to answer using non-digital methods, due to the size or complexity of the source material. They also enable the public to engage with research in an accessible way.

Digital methods are not always quantitative. Scholarly digital editions, for example, use digital methods to support the manual production of texts that are designed to be read.

A typical Digital Humanities project will use digital methods in both its research methodology and dissemination plan, largely by making the products of its research (data, tools, web apps etc) publicly accessible at the end of the project.

Examples of Digital Humanities methods, processes, and activities

Digital Humanities projects use a wide range of methods drawn from the humanities, social sciences, computing, and design. These include:

  • Creating research datasets from archival sources, such as recording manuscripts, documents, or cultural artefacts into structured databases.
  • Digitising, preserving, and curating archives, including manuscripts, books, images, audio, and other cultural materials.
  • Transcribing and encoding historical texts, including manuscripts and correspondence, for digital scholarly editions such as critical, variorum, or genetic editions.
  • Conducting interviews and ethnographic studies, often coded and analysed using qualitative research methods such as thematic or discourse analysis.
  • Coding and modelling humanities data, including the creation of codebooks, metadata schemas, and domain ontologies.
  • Analysing large cultural archives, such as newspapers, journals, books, image collections, and other large-scale historical datasets.
  • Using artificial intelligence and machine learning methods, including natural language processing, topic modelling, automated classification, computer vision, and AI-assisted transcription of historical documents.
  • Studying language and texts computationally, including corpus linguistics, dialectology, stylometry, translation studies, and other approaches to written and spoken language.
  • Compiling and analysing social media and digital communication, to study contemporary cultural and social phenomena.
  • Creating and annotating audio-visual datasets, including collections of sound recordings, images, film, and video.
  • Using immersive and interactive technologies, such as 3D reconstruction, augmented reality, virtual reality, and virtual environments.
  • Mapping cultural and historical data, including historical GIS, spatial analysis, and map-based digital storytelling such as walking tour apps.
  • Applying network analysis, for example to study correspondence networks, intellectual communities, or cultural connections.
  • Visualising humanities data, including timelines, maps, network diagrams, and other visual representations that help reveal patterns in complex datasets.
  • Developing digital platforms and research tools, including web apps, mobile apps, online research resources, virtual exhibitions, and interactive websites.
  • Crowdsourcing and citizen research, where members of the public contribute to transcription, annotation, or analysis of cultural materials.