Introduction to Digital Humanities
Digital Humanities (DH) is the application of computational methods to humanities disciplines. It involves the practical techniques and tools involved, but also the critical evaluation of these methods and their outputs.
Digital Humanities is an extremely broad field. There are, however, a number of identifiable areas of activity which are helpful to convey its scope. In real-world projects these different aspects typically overlap. For example, a digital storytelling research project may have component elements of mapping, data cleaning, text encoding and more.
Data Cleaning
An important process for anyone working with data, particularly in humanities research, where messy datasets are common. We offer workshops in Cleaning Data with OpenRefine to show how to identify and resolve inconsistencies of format and structure in data.
Data Visualisation
Visualisation techniques are useful for analysis and presentation of humanities data, helping to make large datasets legible in different forms and for different audiences. It can be a helpful skill during the research process too, enabling fresh perspectives and suggesting new paths of enquiry.
Data Mapping
Spatial humanities or GIS (Geographic Information Systems) is an established area of data visualisation. It is useful in many areas of scholarship beyond geography and can be an engaging way of reframing and visualising content for both research and teaching. The Library offers workshops in ArcGIS software for mapping data and building narratives around your maps.
Digital Storytelling
Digital tools are often used to tell stories, combining narrative with a variety of multimedia, including graphics, audio, video, and web publishing. Output formats include pocket documentaries, digital essays, mapped memoirs, interactive storytelling and, podcasts.
Machine Translation and Natural Language Processing
Machine Translation (MT) and Natural Language Processing (NLP) are related fields of digital humanities; NLP looks at how computers process and analyse natural language data and MT is focused on translation of text or speech. These fields are sometimes categorised as ‘computational humanities’, indicating a more technology-led approach than digital projects and activities from the ‘traditional’ humanities.
Text Analysis and Topic Modelling
Text analysis takes elements such as word frequencies and statistically generated ‘topics’ to perform distant reading of large collections. Programming languages such as Python or R can be used to perform this kind of analysis or out-of-the-box tools like MALLET and Paper Machines.
Text Encoding
Text encoding is the use of mark-up to structure textual data. Tags are applied within the text stream itself. Mark-up remains a standard practice in editing, processing, and publishing texts in electronic forms. HTML is a form of mark-up but for semantic mark-up of literary texts TEI is more prevalent.
In addition to those fields or strands of DH, there are various other practical aspects. Some knowledge or awareness will help most digital research projects both in the early planning stages and in realising outputs.
Web Hosting & Publishing
Many DH projects include some kind of web-based resource as an output. The Library offers consultation and advice in options for hosting and publishing your digital scholarship outputs.
Metadata for Digital Research Projects
Good quality metadata is key to a successful digital research project. It enables you to describe and manage content during a project’s lifecycle and to make it properly accessible and discoverable. We offer training in best practice for metadata and managing digital content.
Given the range of skills involved, we believe interdisciplinarity and collaboration are a core part of digital humanities work. Knowledge and expertise from different domains will help ensure a digital research project has sound methodology. The Library aims to facilitate and foster this collaboration and act as a hub for DH in DCU.