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.
Type:
Planning 5 Items
27.12.2023
LIDO Training
NFDI4Culture Guideline
Author(s):
Barbara Fichtl
Version: 1.0.1
LIDO (Lightweight Information Describing Objects) is a metadata schema for the presentation and publication of data on cultural heritage objects. This training provides the basics for understanding and using LIDO.
31.08.2023
Catalog of Quality Problems in Data, Data Models and Data Transformations (2020)
Link Recommendation
Language(s):
English
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.
15.08.2023
Wikidata
Link Recommendation
Language(s):
English
Wikidata is a rapidly growing database on all knowledge domains that has been in existence since 2012 and can be edited by anyone interested or supplemented by automated enrichment. Wikidata represents one of the most important data hubs of the Linked (Open) Data network.
15.08.2023
The Integrated Authority File - Standards Data for Culture and Research
Link Recommendation
Language(s):
German
The film provides information about the GND as a data collection of authority data from the cultural sciences and as an organizational structure for institutions involved in shaping the GND. It addresses the integration of the GND in international contexts.
14.08.2023
FAIR-Aware: Assess Your Knowledge of FAIR (2021)
Link Recommendation
Language(s):
English
FAIR-Aware is an online tool that helps researchers and data managers assess how much they know about the requirements for the findability, accessibility, interoperability and reusability (FAIR) of datasets.