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.

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Documenting 3 Items

Data Curation Profiles Toolkit – User Guide

Link Recommendation
Language(s): English

The publication by Jake Carlson provides instructions on how to use the so-called "toolkit" for documenting research data, as well as templates and examples compiled for free use by Purdue University in the US.

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Data Documentation

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Language(s): German

The site provides detailed information on why, how and which research data should be documented in accordance with the FAIR criteria, with regard to formats, vocabularies, languages, protocols, repositories, persistent identifications.

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Data Curation Profile

Link Recommendation
Language(s): German

The publication by Jake Carlson provides instructions on how to use the so-called "toolkit" for documenting research data, as well as templates and examples compiled for free use by Purdue University in the US.

Read (de)

Overarching topics 1 Items

Research Data in Cultural Studies – an introduction

NFDI4Culture Guideline
Author(s): Martin Albrecht-Hohmaier, Andrea Polywka, Christoph Eggersglüß, Alexander Stark, Katharina Bergmann
Version: 1.0.1

A basic introduction to research data management in the arts, cultural studies and humanities, with explanations for the most important concepts, standards and tools.

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Planning 11 Items

The challenge of data quality – recommendations for the future viability of research in the digital transformation (2019)

Link Recommendation
Language(s): German English

The position paper of the Council for Information Infrastructures (RfII) deals with quality assurance and enhancement in the documentation of research data and is a comprehensive and motivating analysis on the topic of quality assurance in the science system.

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How to be FAIR with your data (2021)

Link Recommendation
Language(s): English

The teaching and training manual is intended to support higher education institutions in integrating FAIR-specific content into curricula. It offers a comprehensive presentation of all FDM and FAIR topics, arranged by competence profiles, and includes practical material.

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GO FAIR

Link Recommendation
Language(s): English

GO FAIR is a bottom-up initiative aiming to implement the FAIR data principles and make data discoverable, accessible, interoperable and reusable (FAIR). It provides an implementation network active in the areas of GO CHANGE, GO TRAIN, GO BUILD.

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Formal Ontologies: A Complete Novice's Guide (2018)

Link Recommendation
Language(s): English

The multi-part learning module of the PARTHENOS project (2016–19, G. Bruseker et al.) explains the purpose, basic principles and application contexts of ontologies in semantic information processing even for users without an information science background.

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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.

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FAIR Data Principles for Research Data (2017)

Link Recommendation
Language(s): English German

The page explains the FAIR principles quickly and clearly. On the one hand, it describes the tasks involved in creating FAIR research data, and on the other hand, it presents the requirements for repositories to offer FAIR data.

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Rights & Ethics 1 Items

CARE – Traditional Knowledge Labels

Link Recommendation
Language(s): English

Traditional Knowledge Labels help to implement the CARE principles. Via the page one gets a detailed explanation and further explanations of the TK labels.

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