|Course coordinator(s)||ir. MA Zijp|
|Lecturer(s)||MSc JJ Koehorst|
|dr. M Suarez Diez|
|dr. Q Liu|
|Examiner(s)||dr. M Suarez Diez|
Language of instruction:
Assumed knowledge on:
Familiarity with traditional databases and data management (like INF-21306 Data Management), and computer programming (like INF-22306 Programming in Python) is helpful, but not formally required.
Thesis work (INF, SSB, BIF).
The term Linked Data refers to a set of best practices for publishing and interlinking structured data on the Web. Linked data practices are implemented with semantic web technologies (as URI/IRI, RDF, OWL, SKOS, SPARQL, etc.) through which computers can query data, draw inferences using vocabularies, etc. Linked Data technologies enable research data sharing in ways that are Findable, Accessible, Interoperable, and Reusable (FAIR). This course introduces you to the knowledge and skills needed to use Linked Data, focusing on research data applications and needs in the Life Sciences domains. Such skills that are in increasing demand as the Web evolves from a web of documents to a Web of Data.
After successful completion of this course students are expected to be able to:
- explain the research data life-cycle;
- explain linked data technologies (Internet, Semantic Web, ontologies, graph databases) and standards (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 research data and endpoints for finding data from the Life Sciences domain ;
- develop own ontologies and demonstrate the use of reasoners;
- debate why linked data is important and contemporary views on data stewardship, FAIR principles, data provenance, curation, ownership, open data and their application in the Life Sciences.
- self study.
- practicals logbook (30%, mandatory 5.5);
- final exam (70%, closed book exam, mandatory 5.5).