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Introduction to digital humanities

Modern science can no longer exist without the use of digital methods and techniques. The same is true in the humanities. Virtually every scholar uses digital methods to search for relevant literature, communicate with colleagues and publish research results, and the use of information technology for research purposes is also becoming increasingly frequent. This is driven by:

  • the increasing availability of digitized resources (such as newspaper texts, novels, archival sources, audiovisual material), source material that is originally digital (so-called born digital material, such as facebook posts, websites and digital expressions of art and culture) and digital data files;
  • the growing awareness of the usefulness of computer technology for humanities research;
  • the development of specific tools for humanities research.

Information technology can not only support the traditional methods of humanities research (such as the hermeneutical method), but also creates the opportunity to ask new questions in the humanities and develop new methods.

The rapidly expanding field that investigates the potential of digital methods and techniques in the humanities is often called digital humanities. The terms computational humanities, e-humanities or the Dutch e-humaniora are also used. A useful compact definition of this field is:

Broadly construed, digital humanities is the use of digital media and technology to advance the full range of thought and practice in the humanities, from the creation of scholarly resources, to research on those resources, and to the communication of results to colleagues and students. [Dan Cohen, 09-03-2010.]

In practice, these are the key elements of digital humanities:


  • (Mass) digitization of data.
  • Building collections ('corpora') of digitized data and / or existing digital data. An important aspect of this is the description of the data by means of so-called metadata.
  • Manual and / or automatic enrichment of data collections with classifying and / or interpretative information (annotation).
  • Archiving and disclosure of digital data.
  • Standardization of metadata and annotations.


  • Search all kinds of data collections (see above).
  • Tools for analysing digitized data. Visualization of research data plays an important role in digital data analysis, for example when investigating developments in place and time, or mapping networks between people.
  • Storage and processing of metadata, markup, annotations and derived data using databases or XML.
  • Development of user-friendly research tools that can combine various tasks.
  • Development of computational models that can combine and analyse information from different data sets.

Research methods:

  • Exploring the new research techniques made possible by the digital availability of data and tools. This is especially true for the analysis of large data sets, comparative approaches and the investigation of long-term trends. On this topic, you can watch this video, where Julia Noordergraaf (UvA) talks about the new opportunities offered by large datasets for research in the field of cultural heritage.
  • The interdisciplinary nature of a lot of digital humanities research: more and more humanities scholars from different areas are cooperating with computer scientists in increasingly large projects.
  • Critical reflection on the theoretical and methodological implications of these new methods and the reliability and representativeness of the data for different research questions.


To what extent will the availability of digitized resources and the development of new technologies change the nature of humanities research? Humanities researchers disagree about the answers to this question. These are some of the main discussion points.

Traditional humanities research typically makes use of a small number of sources and / or relatively small data sets that are manually analysed in detail. The researcher has full knowledge and control of the method of analysis he or she uses, but the scope of the conclusions that can be drawn is sometimes limited. In this type of research, the unique is more important than the general. Using software and / or computational models to analyse a large number of resources provides speed and efficiency and allows researchers to make statements about larger amounts of data, but that often comes at the expense of particular details in the data and of the accuracy of the analysis. Imperfections can arise when data is selected automatically; automatic analysis of text, for example, is always coarser than the careful interpretation of a researcher. It is relatively easy to search for specific words in a corpus, but it is more difficult to automatically detect more abstract concepts, themes and contextual meaning. Researchers will always have to deliberate between choosing a quantitative or a qualitative method, between looking for general patterns and trends or making use of the accuracy of in-depth research.

Using existing digital sources and / or data sets rather than original sources may also have some implications for research. This is explained in more detail here.

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