Here are some general-purpose resources for developing digital humanities projects.
For resources and tools related to specific DH methodologies, please explore the other tabs in this box!
Best Practices for Digital Humanities Projects
Development for the Digital Humanities
Digital Research Tools (DiRT)
Introduction to Digital Humanities Course Book
Visualizing Objects, Places, and Spaces: A Digital Project Handbook
Text encoding and analysie involves using computational tools to analyze large amounts of text, such as books, articles and manuscripts, to uncover patterns and connections that would be difficult to find by reading them manually.
Tools and resources for text encoding and analysis:
Digital mapping and spatial analysis involves creating digital maps and spatial analyses to study the relationships between people, places, and events in the past and present.
Tools and resources for digital mapping and spatial analysis:
Network analysis and visualization involves using computational tools to analyze and visualize relationships between people, ideas, and events, in order to understand how they are connected and how they have changed over time.
Tools and resources for network analysis
Digital preservation and archiving involves creating digital copies of historical and cultural artifacts and making them available online, with the goal of preserving them for future generations.
Tools and resources for digital preservation:
Data visualization is the process of using graphical representations to show the results of data analysis, such as graphs, charts, and maps, which can help to identify patterns and trends. See the UCLA Data Visualization Research Guide for more information.
Collaborative research and annotation is the practice of multiple researchers working together using digital tools and platforms to annotate, analyze, and interpret data.
Tools and resources for collaborative research and annotation:
Virtual reality (VR) and 3D modeling involves using virtual reality and 3D modeling techniques to create immersive simulations of historical and cultural sites, which can be used for research and education.
Tools and resources for VR and 3D modeling:
Here are some commonly used tools for data cleaning, statistical analysis and visualization.
UCLA offers various free and discounted licenses for some software products, so make sure to check the list before paying for a program.
Python is a programming language that enables data analysis.
R is a programming language that enables data analysis.
MATLAB is a proprietary programming language and numeric computing environment.
Open Refine is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.
Tableau Software helps people see and understand data. Tableau allows anyone to perform sophisticated education analytics and share their findings with online dashboards.
Stata is a proprietary, general-purpose statistical software package for data manipulation, visualization, statistics, and automated reporting. It is used by researchers in many fields, including biomedicine, economics, epidemiology and sociology.
SPSS (Statistical Package for the Social Sciences) is a software package used for the analysis of statistical data. Although the name of SPSS reflects its original use in the field of social sciences, its use has since expanded into other data markets.
ArcGIS is geospatial software to view, edit, manage and analyze geographic data. It enables users to visualize spacial data and create maps.