Culture Knowledge Base

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.

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.

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.

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.

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.

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.

To counteract ambiguities in the interpretation of FAIR principles, the Research Data Alliance has developed core assessment criteria for FAIRness of data and formulated a set of indicators, including a checklist, as a starting point for reviewing your own data.

As an application ontology, the Erlangen CRM / OWL is an interpretation of the CIDOC Conceptual Reference Model. 
It is the authoritative application ontology of CRM in the German-speaking world and important, e. g., for data stewards carry out implementations on the basis of…

This little data management horror story illustrates fundamental problems in the subsequent use of data that arise due to poor standardisation, documentation and archiving, suitable for initial sensitisation to the topic of research data management.

The article offers a good first introduction to the topic of DMP and contains numerous useful, further links for the user (including the specifications of the funding institutions, DMP tools, etc.).

The description of the data life cycle on forschungsdaten.info is very compact, but as an introduction it explains aptly what the data life cycle is all about.