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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Digitizing, Inventorizing, Enriching 2 Items

Spectrum – Digital Asset Management (2013)

Link Recommendation
Language(s): English

This document proposes a methodology for integrating the management of digital collections into the existing curatorial and administrative functions of the institution. It is intended as a companion document to the UK Collections Trust's Spectrum documentation standard.

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DFG Practical Guidelines on Digitisation (2016)

Link Recommendation
Language(s): German English

The DFG Code of Practice offers recommendations for good practice in the preparation and implementation of digitization projects. By formulating standards, they aim to contribute to the sustainability and future viability of digitization projects.

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Overarching topics 1 Items

DFG-Code of Conduct "Guidelines for Safeguarding Good Research Practice" (2019)

Link Recommendation
Language(s): German

The DFG Code of Conduct represents the consensus of the DFG's member organizations on the fundamental principles and standards of good practice, underscoring the importance of integrity in everyday research.

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Planning 6 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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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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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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Data Sharing and Management Snafu in 3 Short Acts (2012)

Link Recommendation
Language(s): English

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.

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Data Management Plan

Link Recommendation
Language(s): German

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.).

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Data Life Cycle

Link Recommendation
Language(s): German

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.

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Rights & Ethics 1 Items

Legal aspects of research data management

Link Recommendation
Language(s): German

The chapter written by Anne Lauber-Rönsberg in the Practical Handbook on Research Data Management (2021) summarizes the legal framework of FDM. The focus is on copyright law, the regulations of good scientific practice, service and labor law, and data protection law.

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Data sharing & publishing 2 Items

Checklist for appropriate handling of research data

Link Recommendation
Language(s): English

This DFG questionnaire helps researchers to plan and describe the handling of research data in their project beyond the context of an application.

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From project proposal to data publication

Link Recommendation
Language(s):

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.

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