3.3.1. Bachelor level

Table 3: Entry-level content including learning outcomes – bachelor level

Topic	Required level	Learning outcomes [b]=basic, [i]=intermediate, [a]=advanced
General principles and concepts in data management – overview	basic	- [b] Can define Research Data Management (RDM) and can describe its relevance and benefits.
Metadata, metadata formats, standards and registries	basic	- [b] Can describe types of metadata. - [b] Can recognise metadata formats. - [b] Can identify metadata standards. - [b] Can use metadata standards to describe resources. - [b] Can explain what metadata registries are. - [b] Can search and find data and metadata standards registries.
Open Research, Open Access, Open Data	basic	- [b] Can paraphrase the concept of Open Research. - [b] Can describe the benefits of Open Research.
Persistent Identifiers (PID), Open Researcher and Contributor ID (ORCID), Research Organization Registry (ROR)	basic	- [b] Can recognise PIDs and explain the different use cases for PIDs. - [b] Can explain the importance of PIDs for FAIR data. - [b] Can use PIDs to access data or other resources.
FAIR (Findable, Accessible, Interoperable, Reusable) principles in data management	basic	- [b] Can paraphrase the FAIR principles. - [b] Can explain why the FAIR principles were developed. - [b] Can recognise the relationship between FAIR, Open and RDM.
Master data management, data dictionaries	basic	- [b] Can develop a data management plan for their own work. - [b] Can identify different types of data documentation. - [b] Can explain the purpose of the documentation. - [b] Can use existing documentation.
Data security and protection	basic	- [b] Can define different levels of data security (user, folder, files). - [b] Can explain different ways of data protection (physical, encryption etc.).
Data backup	basic	- [b] Can describe what a backup is and tell reasons for backup creation. - [b] Can explain the 3-2-1 rule and apply it to their own files. - [b] Can identify institutional backup solutions.
Personal data protection, GDPR compliance	basic	- [b] Can explain reasons for data protection. - [b] Knows basic rules and legal regulations for sensitive data (e.g. GDPR). - [b] Knows how to comply with these rules and laws.
Data management planning, FAIR data management and compliance	basic	- [b] Can describe what a data management plan (DMP) is. - [b] Can explain why data management planning is a step towards FAIR.
Data interoperability and metadata management	basic	- [b] Can explain aspects of interoperability (Knowledge). - [b] Can relate metadata management to interoperability (Understand).
Data provenance, data lineage	basic	- [b] Can illustrate with an example what data provenance/data lineage means.
Responsible data use, data privacy, ethical principles, IPR and legal issues	basic	- [b] Can summarise and explain ethical principles and responsible data use (e.g. CARE, indigenious data). - [b] Can describe legal issues around data use and management (e.g. licences, patents, policies, contracts etc.).
Data quality management, best practices and frameworks, data quality metrics	basic	- [b] Can summarise best practices ensuring data quality.
Trusted data repositories and certification	basic	- [b] Can explain what a trusted data repository is and how to find it (re3data.org and FAIRsharing). - [b] Can compare different certifications for data repositories (e.g. CoreTrustSeal, CLARIN certification).
Data discovery (published data), data selection and use in research	basic	- [b] Can explain the importance of data discovery and reuse.
Research data lifecycle	basic	- [b] Can explain the steps of the research data lifecycle. - [b] Can compare different lifecycle models.
Ontologies, controlled vocabularies	basic	- [b] Can explain the role of ontologies and vocabularies (Knowledge). - [b] Can recognise the use of ontologies and vocabularies (Knowledge). - [b] Can identify a few domain-relevant ontologies. (Knowledge). - [b] Can search and find terminologies in registries.

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