Course Glossaries

1. Introduction to DH

Digital Humanities (DH): An interdisciplinary field that combines the use of digital tools, technology, and humanities scholarship to explore, analyze, and preserve human culture, history, and society.
Text Analysis: The use of computational tools and techniques to examine large corpora of text to identify patterns, trends, and themes that may not be immediately apparent through traditional analysis.
Data Visualization: The representation of data in a visual format, such as graphs or maps, to help uncover patterns, relationships, and trends in the data, making complex information more accessible and understandable.
Digital Archives: Repositories of digital copies of cultural artifacts, manuscripts, and other historical documents that have been digitized for preservation and public access.
Geospatial Analysis: The study of spatial relationships and patterns using geographic information systems (GIS) to map and analyze data related to geography and human activities.
Cultural Heritage Preservation: The use of digital technologies to digitize, catalog, and conserve cultural artifacts and historical sites, ensuring their long-term accessibility and conservation.
Interdisciplinary Research: Research that integrates methods, theories, and tools from multiple academic disciplines to address complex questions that cannot be fully understood within a single field.
Crowdsourcing: The practice of engaging the public in data collection, analysis, or enhancement, often used in DH projects to gather and improve information about cultural objects.
Machine Learning: A branch of artificial intelligence that enables computers to learn from and make predictions or decisions based on data, increasingly used in DH for text analysis and data processing.
Natural Language Processing (NLP): A field of computer science that focuses on the interaction between computers and human language, often used in DH for tasks such as text analysis, translation, and sentiment analysis.
3D Scanning: The process of capturing the shape and appearance of real-world objects in three dimensions using specialized equipment, often used in DH to create digital replicas of cultural artifacts.
Virtual Reality (VR): An immersive digital environment that simulates real or imagined worlds, used in DH to allow users to interact with and explore digital representations of historical sites or artifacts.
Open Access: The principle of providing free and unrestricted access to research outputs, including digital cultural heritage materials, to promote knowledge sharing and democratization.
Metadata: Descriptive information about a digital object, such as title, author, and date, used to facilitate discovery, retrieval, and management of digital resources.
Digital Literacy: The ability to effectively use digital tools and technologies for research, communication, and problem-solving, an essential skill in DH.
Artificial Intelligence (AI): The simulation of human intelligence by machines, particularly computer systems, which is increasingly being integrated into DH for tasks such as automated analysis and pattern recognition.
Interoperability: The ability of different systems, devices, or software to work together and share data, crucial for DH projects that involve collaboration across institutions and platforms.
Data Curation: The process of organizing, managing, and preserving data throughout its lifecycle to ensure its long-term accessibility and usability, important in DH for maintaining digital collections.
Ethical Considerations in DH: The examination of moral issues related to data privacy, consent, representation, and bias in digital projects, ensuring that DH practices are fair and respectful.
Digital Divide: The gap between those who have access to modern information and communication technology and those who do not, an important consideration in DH efforts to make knowledge accessible to all.
Multimodal Research: An approach that combines different forms of data (text, image, audio, video) to provide a more comprehensive analysis of cultural artifacts and historical events.
Algorithmic Bias: The presence of systematic errors in computer algorithms that lead to unfair outcomes, a critical issue in DH where data-driven decisions must be scrutinized for fairness.
Data-Driven Research: A research approach that relies heavily on the analysis of large datasets to identify trends, patterns, and relationships, increasingly common in DH.
Sentiment Analysis: The use of computational tools to identify and extract subjective information, such as opinions or emotions, from text, often used in DH to analyze public discourse.
Digital Storytelling: The practice of using digital tools to tell stories, often incorporating multimedia elements, to engage audiences with historical and cultural content.
TEI (Text Encoding Initiative): A standard for the representation of texts in digital form, providing guidelines for encoding textual features such as structure, formatting, and annotations.
Digital Mapping: The creation of interactive maps that combine geographic data with other forms of information to explore spatial relationships, commonly used in DH for historical analysis.
Semantic Web: A concept in web development that allows data to be shared and reused across applications, enterprises, and communities, often utilized in DH to improve data integration and retrieval.
Corpus Linguistics: The study of language as expressed in corpora (bodies of text) and the analysis of linguistic patterns within these texts, a key method in DH.
Digital Pedagogy: The use of digital tools and technologies to enhance teaching and learning, particularly in the context of humanities education.
Text Encoding: The process of converting text into a digital format that preserves its structure and meaning, often using standards like TEI in DH projects.
Digital Exhibitions: Online presentations of cultural artifacts, artworks, or historical documents that allow for interactive exploration and engagement by the public.
Archival Digitization: The process of converting physical archives into digital formats to preserve them and make them accessible for research and public access.
Ontology in DH: The study of how concepts and categories in a subject area are related, often used in DH to organize and structure knowledge in digital databases.
Linguistic Annotation: The process of adding metadata to text to mark linguistic features such as parts of speech, syntax, and semantics, used in DH for detailed text analysis.
Historical GIS (Geographic Information Systems): The application of GIS technology to historical research, allowing scholars to visualize and analyze past events in spatial context.
Computational Stylistics: The use of computational methods to analyze literary style, such as authorial voice or genre characteristics, a growing field within DH.
Digital Philology: The study of texts and languages using digital tools to analyze, edit, and preserve literary works, often involving the digitization of manuscripts.
Network Analysis in DH: The use of network theory to analyze relationships and interactions within datasets, such as social networks or connections between historical figures.
Crowdsourced Transcription: The process of enlisting the public to transcribe digitized documents, often used in DH projects to enhance accessibility and data quality.
Interactive Timelines: Digital tools that allow users to explore historical events in a chronological format, often used in DH to provide context and connections between events.
Digital Manuscripts: Digitally scanned versions of historical manuscripts that have been preserved and made accessible through digital archives and libraries.
Cultural Analytics: The use of computational tools to analyze cultural data, such as visual art, music, or literature, to uncover patterns and trends in cultural production.
Public Humanities: Efforts to engage the public in humanities research and education, often facilitated by digital tools and platforms in the DH context.
Data Mining in DH: The process of discovering patterns, correlations, and trends in large datasets, used in DH to analyze vast amounts of cultural and historical data.
Visual Analytics: The combination of data analysis and visual representation techniques to explore complex datasets, commonly used in DH for exploratory analysis.
Digital Repositories: Online databases that store and provide access to digital objects such as texts, images, and multimedia, essential for the preservation and dissemination of DH research.
Machine Translation in DH: The use of automated tools to translate text from one language to another, increasingly important in multilingual DH projects.
Digital Annotation: The practice of adding notes, comments, or metadata to digital texts or images, often used in DH to enhance understanding and analysis.
Big Data in DH: The use of large and complex datasets in DH research, which require advanced computational tools for storage, analysis, and visualization.