Lecture

Digital Humanities: Defining Big Cultural Data Sets

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In course

DH-405: Foundations of digital humanities

This course introduces the core concepts and methodologies of Digital Humanities, integrating both theoretical and practical perspectives. Students learn to work with large-scale cultural datasets, acquiring the tools and techniques required for their proc ...

Description

This lecture explores the concept of Digital Humanities, discussing the distinction between humanists and digital humanists, the role of computer scientists in DH communities, the debate between makers and interpreters, and the balance between distant and close reading in DH research. It also delves into the question-driven vs. data-driven approaches, the differences between Big Data DH and Small Data DH, and the narratives surrounding Big Data. The lecture further examines the technological, open-ended, relational, and paradigmatic dimensions of data bigness, emphasizing the challenges and opportunities presented by Big Cultural Data Sets.

Instructor

Frédéric Kaplan

Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Short biography Pro

Official source

Related concepts (18)

Data

In common usage and statistics, data (USˈdætə; UKˈdeɪtə) is a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures.

Computer science

Computer science is the study of computation, information, and automation. Computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software). Though more often considered an academic discipline, computer science is closely related to computer programming. Algorithms and data structures are central to computer science.

Relational database

A relational database is a (most commonly digital) database based on the relational model of data, as proposed by E. F. Codd in 1970. A system used to maintain relational databases is a relational database management system (RDBMS). Many relational database systems are equipped with the option of using SQL (Structured Query Language) for querying and updating the database. The term "relational database" was first defined by E. F. Codd at IBM in 1970. Codd introduced the term in his research paper "A Relational Model of Data for Large Shared Data Banks".

Computer science and engineering

Computer science and engineering (CSE) is an academic program at many universities which comprises scientific and engineering aspects of computing. CSE is also a term often used in Europe to translate the name of engineering informatics academic programs. It is offered in both undergraduate as well postgraduate with specializations. Academic programs vary between colleges, but typically include a combination of topics in computer science, computer engineering, and electrical engineering.

Data science

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.

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