preliminary

23.10.: Session 1: Introduction: Why publish research data?

  • Course organisation and expectations
  • What is research data in historical studies?
  • Motivations and challenges of data publication

30.10.: Session 2: Research Data Life Cycle

  • Phases of research data management
  • Data types: structured, semi-structured, unstructured

06.11.: Session 3: Licensing and Open Data

  • Open access and licensing
  • Copyright for historical data
  • Data protection and personal rights

13.11.: Session 4: FAIR and CARE Principles

20.11.: Session 5: Introduction to data modelling

  • What is a data model?
  • Conceptual vs. technical modelling
  • Importance of standards and interoperability

27.11.: Session 6: Introduction to RDF and Linked Data

  • RDF triples: Subject, Predicate, Object
  • Namespaces and URIs
  • Serialisation formats (Turtle, JSON-LD)

04.12.: Session 7: Ontologies

  • What are ontologies?
  • Classes, properties, instances
  • Ontology landscape for cultural heritage

11.12.: Session 8: CIDOC CRM

  • Structure and basic principles of CIDOC-CRM
  • Central classes and properties
  • Modelling patterns

18.12.: Session 9: Finding and evaluating ontologies

  • Where can I find suitable ontologies?
  • Evaluation criteria for ontology reuse

08.01.: Session 10: Mapping

  • Dealing with modelling conflicts
  • Documentation of mapping decisions

15.01.: Session 11: Other important standards

  • LIDO
  • METS
  • Dublin Core

22.01.: Session 12: Normdata

  • GND, Wikidata, Getty, World Historical Gazetteer
  • Advantages and challenges of linking standardised data
  • Reconciliation processes

29.01.: Session 13: Repositories

  • Zenodo, institutional repositories, Wikidata
  • Advantages and disadvantages of different platforms
  • Metadata and documentation

05.02.: Session 14: Wikidata

  • Wikidata data model and constraints
  • Advantages and disadvantages for historical data
  • Batch upload and maintenance

12.02.: Session 15: Presentations & Outlook

  • Presentation of the processed data sets
  • Reflection on challenges and solutions
  • Outlook: Data culture in historical studies