Reference
Last updated on 2025-04-02 | Edit this page
Refrences
Weller, Katrin. Knowledge Representation in the Social Semantic Web. De Gruyter Saur, 2010
Terras, Melissa, Nyhan, Julianne, & Vanhoutte, Edward (eds.). Defining Digital Humanities: A Reader. Routledge, 2013.
Flanders, Julia & Jannidis, Fotis (eds.). The Shape of Data in Digital Humanities: Modeling Texts and Text-based Resources. Routledge, 2019.
DARIAH (2022). Guidelines for Research Data Management in the Humanities. (https://www.dariah.eu/)
Banzal, S.. XML Basics. Berlin/Boston, 2020, https://doi.org/10.1515/9781683925446.
Harold, Eliotte Rusty & Means, W. Scott. XML in a nutshell. Beijing, 2002.
Van der Vlist, Eric: XML schema. the W3C’s object-oriented descriptions for XML. Beijing, 2002.
Baca, Murtha (Ed.). Introduction to Metadata. 3. Edition. 2016
Glossary
The terms listed here are defined solely within this context and do not claim to be comprehensive, particularly regarding ongoing debates and potential controversies surrounding their scope.
Authenticity – The debate over the authenticity and originality of digitized objects or texts.
Authorship – Issues of authorship in the digital age, particularly concerning remixing, collaboration, and the use of algorithms.
CARE - The CARE Principles for Indigenous Data Governance were developed to complement the FAIR Principles. The acronym stands for Collective Benefit, Authority to Control, Responsibility and Ethics. Based on the CARE Principles, researchers are sensitised to ensure that the rights and interests of indigenous peoples are respected in open data and open science efforts.
Cataloguing – The process of recording, describing, and systematizing data through metadata, particularly in libraries, archives, museums, and databases.
Computational Analysis – Digital methods for examining data, including machine learning and data visualization.
Data Accessibility – The availability and usability of data for research and public use, especially through digital platforms and repositories.
Data Integrity – Ensuring the accuracy, consistency, and reliability of data throughout its lifecycle.
Data Preservation – Strategies for the long-term safeguarding of digital and analog data against loss or degradation.
Data Types (Computing) -
| data type | explanation | example |
|---|---|---|
| string | set of character | this can be a sentence |
| integer | whole number | 14 |
| float | floating-point number | 3.5 |
| boolean | truth value | true/false |
| array | collection of values | „Didi“, 35, „Whatever“, true |
Data Visualization – Methods for graphically representing data to reveal patterns and relationships.
Encoding - In data processing, characters are encoded with a numerical value for transmission or storage. Character encoding allows characters and symbols to be uniquely assigned within a character set. There are different character encodings, so it is important to know which one is being used. For example, the German Ü may be encoded with the decimal value 220 in one character set, while the same value in another character set encodes the curly bracket. To ensure that the data is displayed correctly when used, the form of encoding has to be specified. UTF-8 and UTF-16 are among the most common character sets you may encounter when processing data.
FAIR - The FAIR (Findable, Accessible, Interoperable und Reusable) Principles serve as guidelines to make data suitable for reuse by humans and machines under clearly described conditions.
Hybrid Research Workflow – Research approaches that combine different data types (analog, digitized, and born-digital) to generate new insights.
Information Retrieval – Methods and systems for structured searching and retrieval of data using metadata.
Institutional Infrastructure – Institutions such as libraries, archives, and data centers that ensure the storage, provision, and protection of data.
LOD -
> Linked Open Data is Linked Data which is released under an open
license, which does not impede its reuse for free (Tim Berners-Lee).
Ontologies - Ontologies are linguistically and formally organized representations of concepts and the relationships between them in a given domain. In the cultural heritage field in particular, they specify the presentation of object data so that it can be placed in larger contexts. Ontologies are used to model and standardize information.
Open Access – The principle of free access to academic publications and research findings.
Pattern Recognition – Techniques for identifying recurring structures in data.
Pertinence principle - The principle of pertinence - as a classification priciple - describes the classification of archival records according to territorial, personal or subject matter, regardless of the context in which they were created.
Provenance principle - The provenance principle is an archival classification principle and forms the basis for the classification and indexing of archival records according to their origin and context.
Semantic Web - The Semantic Web is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The aim is to make Internet data machine-readable.
Tag – A keyword or label used for categorizing and quickly retrieving data, content, or metadata. Tags are often freely chosen and are used in digital archives, databases, and social media platforms to structure and connect content.
Text Mining – Computer-assisted analysis of large text collections to detect patterns and trends.