Culture Knowledge Base
The NFDI4Culture Knowledge Base contains guidelines, reports, and specifications by the task areas of the consortium covering all aspects of research data management in the domain of material and immaterial cultural heritage. Additionally, we provide you with curated link recommendations to high quality open educational resources.
Digitizing, Inventorizing, Enriching 1 Items
3D Formats for Classical Studies
The IANUS Research Data Center for Archaeology and Classical Studies, coordinated by the German Archaeological Institute, has compiled IT recommendations for the sustainable handling of digital data, including recommended formats for 3D data.
Organizing 1 Items
Overarching topics 2 Items
Research Data in Cultural Studies – an introduction
A basic introduction to research data management in the arts, cultural studies and humanities, with explanations for the most important concepts, standards and tools.
Guideline for a FAIR Cultural Studies Research Data Management
Author(s): Angela Kailus
In order to be optimally re-usable, research data should be processed according to the FAIR principles. This guideline explains what these principles mean and how they can be implemented in cultural studies and heritage collections.
Planning 1 Items
Catalog of Quality Problems in Data, Data Models and Data Transformations (2020)
The publication identifies concrete data problems in research data on tangible cultural assets. Each problem is described by a structured profile that includes aspects such as the quality dimensions involved, examples, causes and ideas for improvement.
Data sharing & publishing 2 Items
Culture Graph Interchange Format
The Culture Graph Interchange Format is a lightweight data exchange format based on schema.org. It serves to integrate datasets from the domains of NFDI4Culture into the Culture Knowledge Graph and can enhance their discoverability on the World Wide Web.
From project proposal to data publication
This task sheet for beginners contains eight key tasks on research data management, from data capture to publication: Data Management Plan, Data Structuring and Naming, Data Selection, File Formats, Metadata/README File, Legal Aspects, License Selection, Repository Selection.