Culture Knowledge Base

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.

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.

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.

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.).

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.

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…

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.

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.

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.