In this section, information is organised around regular research data management tasks or challenges. You will find:
- Best practices and guidelines for each data management task.
- A list of all the considerations to be taken into account when performing a specific data management task.
- Links to task-specific training materials.
- Links to tool assemblies implemented by others to address specific data management challenges.
- Links to a Data Stewardship Wizard for your DMP and to step-by-step instructions to make your data FAIR.
- A summary table of tools and resources relevant for the specific task and recommended by communities.
Budgeting and costing for data management
How to make data analysis FAIR.
Best practices to name and organise research data.
How to record information about data provenance.
How to prepare data and find repositories for publication.
How to ensure high quality of research data.
How do you ensure that your data is handled securely.
How to identify the sensitivity of different research data types
- Are there privacy reasons why your data can not be open?
- Will you collect any data connected to a person, "personal data"?
- How is pseudonymization handled?
- Could the coupling of data create a danger of re-identification of pseudo- or anonymized personal data?
- Does this dataset contain personal data?
- Does this dataset contain sensitive information?
- Are personal data sufficiently protected?
How to find appropriate storage solutions.
How to document and describe your data.
How to protect your research data, and how to make research data compliant to GDPR.
How to use identifiers for research data.
How to make machine-actionable (meta)data.
How to coordinate and organise data management activities in collaborative or multi-parter projects.