3.3.3. Doctoral Level

Table 5: Entry-level content including learning outcomes – doctoral level

Topic	Required level	Learning outcomes [b]=basic, [i]=intermediate, [a]=advanced
General principles and concepts in data management – overview	advanced	- [b] Can define Research Data Management (RDM) and can describe its relevance and benefits. - [i] Can describe RDM measures to be taken (including explaining why) at different stages of the research process. - [a] Can practically apply theoretical knowledge about proper RDM measures to be taken at different stages to their own research process/project.
Overview of data types, data type registries and data formats	intermediate	- [b] Can describe what types of data exist (Knowledge). - [b] Can explain what data type registries are (Knowledge). - [b] Can identify data formats (Knowledge). - [i] Can determine proper data types for a resource (Analyse). - [i] Can use a data type registry (Apply). - [i] Can use proper data formats to express resources (Apply).
Metadata, metadata formats, standards and registries	advanced	- [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. - [i] Can articulate metadata of different types to describe a resource. - [i] Can write metadata in a relevant format. - [i] Can appraise the usefulness of metadata standards to describe a resource. - [i] Can search metadata registries to find resources. - [a] Can design rich metadata to describe a resource. - [a] Can use proper metadata formats and models to express these metadata. - [a] Can deposit metadata in a repository.
Open Science/Research, Open Access, Open Data	advanced	- [b] Can paraphrase the concept of Open Science. - [b] Can describe the benefits of Open Science. - [a] Can describe Open Access and Open Data as areas of Open Science. - [i] Can recognise if a publication is open access. - [i] Can discover platforms for Open Access/Open Data. - [i] Can articulate what is required to make research outputs open. - [i] Can contrast FAIR and open. - [a] Can plan publication of Open Access publications and FAIR data.
Persistent Identifiers (PID), Open Researcher and Contributor ID (ORCID), Research Organization Registry (ROR)	intermediate	- [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. - [i] Can apply PIDs to their own research outputs. - [i] Can use PIDs to collaborate with others.
FAIR (Findable, Accessible, Interoperable, Reusable) principles in data management	intermediate	- [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. - [i] Can plan for FAIR research outputs. - [i] Can write and develop a research data management plan. - [i] Can apply the principles to their own work. - [i] Can evaluate the FAIRness of their own work or the work of others.
Master data management, data dictionaries	intermediate	- [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. - [i] Can modify existing documentation. - [i] Can evaluate and prioritise data management activities.
Data security and protection	intermediate	- [b] Can define different levels of data security (user, folder, files). - [b] Can explain different ways of data protection (physical, encryption etc.). - [i] Can use different levels of security for their own work. - [i] Can apply data protection methods like password protection and encoding. - [i] Does share and collaborate in a secure way.
Data backup	advanced	- [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. - [i] Can explain institutional backup solutions and apply them to own files. - [a] Can analyse and evaluate backup. - [a] Can solve backup problems independently or with further assistance from support staff.
Personal data protection, GDPR compliance	intermediate (depending on discipline)	- [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. - [i] Can analyse compliance to legal regulations for sensitive data. - [i] Can apply mechanisms to protect data appropriately.
Data management planning, FAIR data management and compliance	intermediate	- [b] Can describe what a data management plan (DMP) is. - [b] Can explain why data management planning is a step towards FAIR. - [i] Can tell which areas should be covered in a DMP. - [i] Can sketch a DMP for their own research project.
Data interoperability and metadata management	intermediate	- [b] Can explain aspects of interoperability (Knowledge). - [b] Can relate metadata management to interoperability (Understand). - [i] Use domain-relevant standards, models and formats for interoperable data (Apply). - [i] Can relate metadata management to interoperability (Apply).
Data provenance, data lineage	intermediate	- [b] Can illustrate with an example what data provenance/data lineage means. - [i] Can transfer how data provenance/data lineage plays a role in their own research project. - [i] Can apply data provenance good practices to their own data and ensure that an unbroken data lineage is established for their work.
Responsible data use, data privacy, ethical principles, IPR and legal issues	advanced	- [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.). - [i] Can analyse if ethical principles or legal issues play a role in their own work. - [a] Can detect ethical or legal issues and solve them together with ethical and legal experts like e.g. ethics committee, data protection officers or lawyers from the institution.
Data quality management, best practices and frameworks, data quality metrics	advanced	- [b] Can summarise best practices ensuring data quality. - [i] Can describe how to recognise quality data. - [a] Can use best practices and frameworks on their own data to ensure their quality.
Trusted data repositories and certification	intermediate	- [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). - [i] Can discover trusted repositories and identify those that are certified. - [a] Can use a trusted repository to share research output.
Data discovery (published data), data selection and use in research	advanced	- [b] Can explain the importance of data discovery and reuse. - [i] Can discover published datasets in their discipline. - [i] Can cite data. - [a] Can develop a strategy to search for data. - [a] Can articulate criteria for data selection. - [a] Can extract datasets and build their own work on them.
Research data lifecycle	intermediate	- [b] Can explain the steps of the research data lifecycle. - [b] Can compare different lifecycle models. - [i] Can apply the research data lifecycle on their own work.
Ontologies, controlled vocabularies	advanced	- [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. - [i] Can use ontologies to describe resources (Apply). - [a] Can use ontologies for search and analysis (Apply).

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