Knowledge graphs in the life sciences

Course coordinator 

Jasper Koehorst, Wageningen University (location WUR-campus) 

Lecturers 

  • Onder Babur (Wageningen University & Research) 
  • Anna Fensel (Wageningen University & Research) 
  • Jasper Koehorst (Wageningen University & Research) 

Course credits 

1.5 ECTS for following the course, 3 ECTS when successfully completing a final assignment 

Course overview 

The term “FAIR Linked Data” refers to a set of best practices for publishing, interlinking and managing structured data on the Web. Semantic Web is an extension of the World Wide Web through recommendations set by the World Wide Web Consortium (W3C). Linked Data practices are implemented with Semantic Web technologies through which computers can query data and enable humans and machines to work in cooperation through a formalised shared meaning. Using knowledge representation formats (such as RDF, RDFS and OWL), ontologies and vocabularies to link data together, inferences can be drawn that show missing links within a given dataset and across datasets. Furthermore, used with Linked Data, SPARQL allows us to query the available information globally. Linked Data technologies enable researchers and industry to share data in ways that are Findable, Accessible, Interoperable, and Reusable (FAIR). As the Web evolves from a web of documents to a Web of Data, Linked Data technologies, such as knowledge graphs, are already broadly in use in industry and research. The first part of the course introduces you to the knowledge, theory, and skills needed to work with Linked Data. We will then focus on research and industrial data applications and needs that are increasingly in demand.  

The learning outcomes are as follows: 

  • Explain Linked Data technologies (Internet, Semantic Web, ontologies, graph databases), standards and recommendations (as URI, XML, RDF, OWL, SPARQL), and their use in forming a Web of Data;
  • Use Linked Data technologies for retrieving information in the Semantic Web (i.e. use of SPARQL);
  • Use of existing vocabularies for annotating data and endpoints for finding data for a particular domain;
  • Understand the FAIR principles and how to apply it to a research project;
  • Able to convert research metadata into FAIR Linked Datasets.

Target audience 

The course is aimed at PhD students with a background in biology, computer science or a related field, and life sciences. Participants from the private sector are also welcome. Preparation material will be distributed during the course. 

More information 

Software used in the computer labs to install on your own computer will be made available before the start of the course. 

Registration

Early bird registration fees (until January 13, 2023)  for this 5-day course are:

  • PhD student: 400 euro (excl. VAT)
  • Academic researcher (PI/Postdoc): 600 euro (excl. VAT)
  • Industry: 900 euros (excl.VAT)

From January 14th, 2023 onwards fees for this 5-day course are:

  • PhD student: 480 euro (excl. VAT)
  • Academic researcher (PI/Postdoc): 720 euro (excl. VAT)
  • Industry: 1080 euros (excl.VAT)

The course fee includes:

  • Course material
  • Catering: coffee, tea and lunch will be provided.

There is room for max. 25 participants.

You can register for the course by filling out this registration form. Find general enrollment information here.